Effects of microplastics on greenhouse gas emissions and the microbial community in fertilized soil

Journal Pre-proof Effects of microplastics on greenhouse gas emissions and the microbial community in fertilized soil Xinwei Ren, Jingchun Tang, Xiaomei Liu, Qinglong Liu PII:

S0269-7491(19)33615-2

DOI:

https://doi.org/10.1016/j.envpol.2019.113347

Reference:

ENPO 113347

To appear in:

Environmental Pollution

Received Date: 9 July 2019 Revised Date:

2 October 2019

Accepted Date: 3 October 2019

Please cite this article as: Ren, X., Tang, J., Liu, X., Liu, Q., Effects of microplastics on greenhouse gas emissions and the microbial community in fertilized soil, Environmental Pollution (2019), doi: https:// doi.org/10.1016/j.envpol.2019.113347. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Ltd.

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Effects of microplastics on greenhouse gas emissions and the microbial community in fertilized soil Xinwei Ren, Jingchun Tang*, Xiaomei Liu, Qinglong Liu Key Laboratory of Pollution Processes and Environmental Criteria (Ministry of Education), Tianjin Engineering Center of Environmental Diagnosis and Contamination Remediation, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China Corresponding author. E-mail address: [email protected]

1

1

Abstract

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Microplastics (MPs) are characterized by small particle sizes (<5 mm) and are widely distributed

3

in the soil environment. To date, little research has been conducted on investigating the effects of

4

MPs on the soil microbial community, which plays a vital role in biogeochemical cycling. In the

5

present study, we investigate the influence of two particle sizes of MPs on dissolved organic

6

carbon (DOC) and its relative functional groups, fluxes of greenhouse gases (GHGs), and the

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bacterial and fungal communities in fertilized soil. The results showed that a 5% concentration of

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MPs had no significant effect on soil DOC, whereas the formation of aromatic functional

9

groups was accelerated. In fertilized soil, the existence of MPs decreased the global warming

10

potential (GWP) as a result of a reduction in N2O emissions during the first three days. A

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potential mechanism for this reduction in N2O emissions might be that MPs inhibited the phylum

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Chloroflexi, Rhodoplanes genera, and increased the abundance of Thermoleophilia on day 3. An

13

increase in N2O emissions was observed on day 30, mainly due to the acceleration of the NO3-

14

reduction and a decrease in the abundance of Gemmatimonadacea. The CH4 uptake was

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significantly correlated with Hyphomicrobiaceae on day 3 and Rhodomicrobium on day 30. In

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soil with MPs, Actinobacteria replaced Proteobacteria as the dominant phylum. Larger MPs

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increased the richness (Chao1) and abundance-based coverage estimators (ACE) and diversity

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(Shannon) of the bacterial community on day 3, whereas these decreased on day 30. The richness

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and diversity of the fungal community were also reduced on days 3 and 30. Smaller MPs

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increased the community richness and diversity of both bacterial and fungal communities in

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fertilized soil. Our findings suggest that MPs have selective effects on microbes and can

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potentially have a serious impact on terrestrial biogeochemical cycles.

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Main findings: Microplastics decreased the global warming potential of soil. Particle size

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affected alpha diversity, and Actinobacteria replaced Proteobacteria as the dominant phylum in

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soil with microplastics.

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Keywords: microplastics, greenhouse gases (GHGs), bacterial community, fungal community,

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terrestrial ecosystem

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1. Introduction

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Microplastics (MPs) present an urgent environmental pollutant situation, and have drawn a

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recent dramatic increase in global attention (Law and Thompson, 2014). Due to their small

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particle size (100 nm–5 mm) and ubiquitous distribution throughout the environment, MPs are

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easily ingested by soil organisms and can accumulate in the food chain (Rillig et al., 2017b).

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MPs can enter the soil as primary or secondary MPs (Ng et al., 2018). Primary MPs are the raw

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materials used for plastic production (Cole et al., 2011), whereas secondary MPs are those that

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result from wastewater contaminated by fibers from washing clothes (Browne et al., 2011),

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environmental degradation of large plastic products, and the ingestion-digestion effect of soil

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fauna (Dris et al., 2016; Klein et al., 2015; Rillig, 2012). In agroecosystem, the main sources of

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MPs include sewage irrigation and sludge applications, industrial and domestic wastewater with

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primary MPs, synthetic micro-fibers from laundry wastewater and wastewater treatment plants

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(Horton et al., 2017; Leslie et al., 2017; Nizzetto et al., 2016; Ziajahromi et al., 2017; Zubris and

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Richards, 2005). In European farmlands, every kilogram of sludge (dry weight) has been

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estimated to contain >1,000–4,000 MP particles, and in the upper 0–10 cm of soil, each kilogram

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of soil contained ~670 fibers of MPs (Barnes et al., 2009; Zubris and Richards, 2005). The

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amount of MP particles entering the European farmlands via sewage sludge and biosolid are

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estimated to be 125–850 tons per million inhabitants, while the total amount of MP particles

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entering the European and North American farmlands are estimated to be 63,000–430,000 tons

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and 44,000–300,000 tons annually, respectively (Ng et al., 2018; Nizzetto et al., 2016).

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Greenhouse materials and soil conditioners as well as the application of mulching film are also

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important sources of MPs (Ng et al., 2018). In Xinjiang Province, China, the maximum residual

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of mulching film was found to gradually increase over time and reached 502 kg ha-1 (Zhang et al.,

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2016). The residual of mulching film can reportedly be broken down by environmental factors or

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by the ingestion-digestion of soil faunal communities (e.g., such as earthworms, slugs, or mites)

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(Cao et al., 2017; Maaß et al., 2017; Rillig, 2012) to subsequently enter the soil as secondary MP

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particles (Huerta Lwanga et al., 2016; Roy et al., 2011; Steinmetz et al., 2016).

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MPs in the soil may bring influence on soil properties (Liu et al., 2017; Rillig, 2018; Rillig et al.,

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2017a), the soil food web (Huerta Lwanga et al., 2017; Rillig, 2012), and the root development

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of crops (Kasirajan and Ngouajio, 2012), causing potentially serious soil environmental

3

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problems. Soil structure (e.g., aggregate size and stability, bulk density and porosity) has an

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important influence on soil functions (Rabot et al., 2018). MP particles and soil microaggregates

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(<0.250 mm) along with organic matter, microbes, and primary soil particles can become

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embedded in soil aggregates (Rillig et al., 2017a). Changes in soil structure can further influence

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soil properties, microbial activities, emissions of greenhouse gases (GHGs), and nutrient cycling

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because many processes in soil are highly sensitive to soil structure (Rabot et al., 2018). Previous

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research has shown that MPs influenced the soil dissolved organic matter (DOM), bulk density,

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water holding capacity, and the functional relationship between the microbial activity and water

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stable aggregates (Liu et al., 2017; de Souza Machado et al., 2018). These findings provided

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direct evidences that MPs, as anthropogenic stressors and drivers, can indeed result in changes to

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terrestrial ecosystems.

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Recent studies have focused on microbes that are related to MPs and have mainly concentrated

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on water ecosystems (Arias-Andres et al., 2018b; Eckert et al., 2018; Harrison et al., 2014;

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McCormick et al., 2014; Miao et al., 2019; Zettler et al., 2013). This interest has related to the

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specific features of MPs in water known as “plastisphere”, which defines the specific niche for

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microbial life around MPs and is associated with the distinct microbial assembling between MPs

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and surrounding circumstances (Zettler et al., 2013). However, little research has been carried

81

out to characterize the effects of MPs on soil microorganisms as the major drivers in

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biogeochemical cycling. Carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) are the

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three most important climate-relevant GHGs (Oertel et al., 2016; IPCC, 2007), and farmland is

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one of the most important sources and sinks of GHGs (Lenka et al., 2017; Oertel et al., 2016;

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Song et al., 2017). The global warming potential (GWP) of N2O is much higher than the GWP of

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CH4 and CO2 (IPCC, 2007), and studies have shown that the addition of carbon material such as

87

biochar can greatly affect the microbial community and emission of GHGs (Christiansen et al.,

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2015; Hawthorne et al., 2017; Wang et al., 2018; Zhen et al., 2018). Thus, the study of the effect

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of MPs on emission of GHGs is of great importance.

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The present study focused on the influence of MPs on GHGs and the microbial community in

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fertilized soil. We aim to illustrate the following in fertilized soil: (1) the response of the

4

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microbial community to MPs; (2) the effects of MPs on the flux rates of GHGs, and (3) the

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possible mechanisms driving changes in the fluxes of GHGs by MPs.

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2 Materials and methods

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2.1 Materials and experimental set-up

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Soil were sampled in April 2018, from Houge Village, Beichen District, Tianjin, China. The

100

sampled area was a reserved field without film mulching or known direct pollution, and the soil

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type was clay. We collected five sample soil cores (upper 0–15 cm) from four corners and center

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of a 20 m × 20 m area. After stones and plant residues were removed, soil samples were air dried

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at room temperature. Dried soil samples were sieved through a 2 mm mesh and mixed

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thoroughly for the subsequent microcosm experiment. The physicochemical properties of the soil

105

were analyzed (Table S1). Soil pH was measured using a pH meter (PB-10 pH meter, Sartorius,

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Gottingen, Germany) in each sample solution (soil: deionized water (w/w) ratio of 1:5). Total

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nitrogen and carbon were determined by using an element analyzer (Euro Vector, EA3000, Italy).

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MPs used in the experiment were from polyethylene (PE) (Haosheng, Guangzhou) that is used

109

for producing mulching film (density of 0.94–0.96 g cm-3). Two particle size groups were

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selected in this experiment: <13 µm and <150 µm. Different treatments were set as: (1) CK:

111

control (no MPs and no fertilizer added to the soil), (2) CKF: fertilized soil (150 kg N ha-1) with

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no MPs; (3) M1F: fertilized soil (150 kg N ha-1) with PE (<150 µm, 5%, w/w), and (4) M2F:

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fertilized soil (150 kg N ha-1) with PE (<13 µm, 5%, w/w). The concentration 5% in our

114

experiment was chosen based on our pre-experiment. The detailed information is described in the

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Supplementary material. MPs were sterilized on an ultraviolet clean bench for 20 min to

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minimize microbial contamination. MPs were then added to 200 g of each soil sample (dry

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weight) in amounts depending on the treatment (M1F and M2F), and evenly mixed within the

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soil. Soil samples from each treatment were placed in sterilized PET (polyethylene terephthalate)

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pots (6.5 cm diameter and 10 cm high), and after adding water, the pots were incubated at 25 °C

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(relative humidity of 80%). The soil moisture was maintained at 60% of the field capacity (w/w).

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Samples from each pot were removed on 1, 3, 7, 15, and 30 days. On the sampling day, the

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emissions of GHGs from each treatment were detected by a greenhouse analyzer G2508 (Picarro

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Inc., Santa Clara, CA, USA) (see Section 2.2). The samples were then collected and passed

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through a 2 mm sieve and separated into two parts: one subsample was stored at -80 °C for

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molecular analysis, and the other was stored in a refrigerator awaiting analysis for dissolved

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organic carbon (DOC) and functional groups (see Section 2.3). All the experiments were carried

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out in triplicates.

128 129

2.2 GHG flux measurement

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The measurements of the flux of GHGs were conducted according to the methods described in

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Hawthorne et al. (2017) and Zhen et al. (2018), which also used a Picarro G2508. Briefly, the

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soil samples were weighed and placed in a static closed chamber connected to the greenhouse

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gas analyzer. During an 8 min GHG flux measurement, the concentrations of CO2, N2O, and CH4

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were measured every 2 s. The hourly flux of each gas was then calculated according to Equation

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S1 (Christiansen et al., 2015; Hawthorne et al., 2017; Zhen et al., 2018). The overall effects of

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MPs on global warming was estimated using global warming potential (GWP, µg kg-1)

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calculated according to Equation S2 (IPCC, 2007; Wang et al., 2018). (see Equations S1 and S2

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in the Supplementary material)

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2.3 Analysis of DOC and functional groups

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The DOC for each sample was extracted at a soil: water ratio of 1: 5 (7 g of soil, 35 mL of

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deionized water) in a 50 mL centrifugal tube. Functional group characteristics were measured

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according to the method described in Jaffrain et al. (2007) and Su Dongxue et al. (2012). All the

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extracts were centrifuged at 3500 r/min for 15 min. After centrifugation, the filtrate was filtered

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through a pre-rinsed 0.45 µm cellulose-acetate membranes (Solarbio, Beijing, China). The DOC

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was determined by a multi N/C 3100 total organic carbon (TOC) analyzer (Analytik Jena AG,

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Germany), and functional groups were determined using an ultraviolet-visible (UV-Vis)

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spectrophotometer (UV-2550, Shimadzu, Japan).

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UV-Vis absorption from 200 to 500 nm (at 1 nm steps) was measured in a 10-mm quartz cuvette

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with deionized water as blank. The specific UV absorbance at 210, 250, 254, 260, 272, 280, and

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365 nm were measured for all samples. The specific ultraviolet absorbance (SUVA) values at

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210, 254, 260, 272, and 280 nm were calculated as the ratio of absorbance value to DOC content,

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and then referred to as SUVA210, SUVA254, SUVA260, SUVA272, SUVA280. The wavelengths

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used in this study and their corresponding organic functional groups are shown in Table S2.

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2.4 Soil microbial community characterization

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DNA was extracted from soil samples (0.5 g) using the Fast DNA Spin extraction kits (MP

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Biomedicals, Santa Ana, CA, USA) according to manufacturer’s instructions. The quantity of

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extracted DNA was measured using a NanoDrop ND-1000 spectrophotometer (Thermo Fisher

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Scientific, Waltham, MA, USA) and agarose gel electrophoresis. Polymerase chain reaction

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(PCR) amplification of the bacterial 16S rRNA V3-V4 region was performed using the forward

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primer 338F (5’-ACTCCTACGGGAGGCAGCA-3’) and the reverse primer 806R (5’-

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GGACTACHVGGGTWTCTAAT-3’). The fungi ITS1 region was performed using the forward

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primer ITS5F (5’-GGAAGTAAAAGTCGTAACAAGG-3’) and the reverse primer ITS1R (5’-

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GCTGCGTTCTTCATCGATGC-3’). The thermal cycling consisted of initial denaturation at

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98 °C for 2 min, followed by 25 cycles of denaturation at 98 °C for 15 s, annealing at 55 °C for

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30 s, extension at 72 °C for 30 s, and then a final extension of 5 min at 72 °C. PCR amplicons

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were purified with Agencourt AMPure Beads (Beckman Coulter, Indianapolis, IN) and

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quantified using the a PicoGreen dsDNA assay kit (Invitrogen, Carlsbad, CA, USA). After the

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individual steps, amplicons were pooled in equal amounts, and pair-end sequencing was

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performed using the Illlumina MiSeq platform with MiSeq Reagent Kit v3 at Shanghai Personal

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Biotechnology Co., Ltd (Shanghai, China). The detailed bioinformatic analysis process is

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described in the Supplementary material.

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2.5 Statistical analysis

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Statistical analyses were performed using IBM SPSS Statistics 24.0. One-way ANOVA was used

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to determine the effect of different treatments on properties of soil DOC and GHGs. The means

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of significant effects at p < 0.05 were then compared using the Duncan’s multiple-range test.

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Operational taxonomic unit (OTU)-level alpha diversity indices, Chao1(Chao, 1984), abundance-

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based coverage estimators (ACE) (Chao and Yang, 1993), Shannon (Shannon, 1948) and

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Simpson’s diversity index (Simpson, 1949) were calculated using the OTU table in QIIME (see

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Equations S3–S7 in the Supplementary material). Principal Coordinates Analysis (PCoA) based

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on weighted UniFrac distance (Lozupone et al., 2007) and Adonis test (McArdle and Anderson,

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2001) were used to determine the difference of beta diversity of bacterial and fungal

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communities by QIIME (v1.8.0). Spearman’s rank correlation analysis was carried out among

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DOC, its relative functional groups and GHGs as well as the interaction between environmental

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factors and microbial community. Figures were visualized by R 3.6.1 (R Core Team, 2019) and

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RStudio 1.1.463 (RStudio Team, 2018), packages information are provided in the Supplementary

194

material. Networks were constructed based on Spearman’s rank correlation coefficients between

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the top 50 dominant genera as well as microbes at different levels and GHGs by Mothur

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(v.1.35.1) and RStudio, respectively, and visualized by Gephi (0.9.2) (Bastian et al., 2009; G.

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Zhang et al., 2019).

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3 Results and discussion

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3.1 Effects of MPs on Dissolved Organic Carbon (DOC) and relative functional group

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characteristics in soil

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Fig. 1. Dissolved organic carbon (DOC) concentration after microplastic addition. Capital letters and lowercase letters designate significant differences treatments in the same incubation day and between sampling time of the same treatment, respectively. Different letters mean significant differences and the same letter means no significant difference, Duncan (p < 0.05)).

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With the exception of day 7, there was no significant difference in DOC concentration among the

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four treatments (Fig. 1). On day 7, in fertilized soil treatments (i.e., M1F and M2F) the soil DOC

210

concentrations increased in comparison to the CKF treatment without MPs (CKF and M1F, p =

211

0.044; CKF and M2F, p = 0.022, least significant difference (LSD), p < 0.05). The SUVA at

212

different wavelengths represents different soil properties (Table 1). During the entire 30-day

213

period, the SUVA210 of the fertilized treatments showed higher values than of that of the CK

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treatment (Fig. 2). SUVA254, SUVA260, SUVA272, and SUVA280 showed the same overall trends.

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On day 7, the existence of MPs in fertilized soil appears to have decreased SUVA254, SUVA260,

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SUVA272 and SUVA280 with MPs of a larger size (i.e., M1F) having a significant effect on the

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SUVAs. In particular, the M1F treatment accelerated the decomposition of high-molecular-

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weight aromatic compounds (Fig. 2). On day 30, the results of SUVA254, SUVA272, and

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SUVA280 for MPs of a smaller size (i.e., M2F) were significantly higher than of those of the CKF

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treatment (p = 0.042, p = 0.043, p = 0.035, LSD, p < 0.05). A250/A365 was used to characterize

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the degree of humification of organic matter, whereby higher values of A250/A365 indicate the

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smaller average molecular weight of DOC and a lower degree of soil agglomeration. On day 30,

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the degree of soil agglomeration was promoted in fertilized soil treatments with MPs (A250/A365

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value: CKF > M1F > M2F) and may have increased the formation of aromatic compounds,

225

which are the basis for the formation of soil humus (SUVA280: M2F > M1F > CKF).

226 227

DOC is a vital part of soil organic matter and a sensitive indicator of the changes in soil quality,

228

(e.g., nutrient availability, structure, moisture, the provision of substrates to microbes) and plays

229

an important role in biogeochemical circulation (Li et al., 2019; Liu et al., 2017). Previous

230

research has shown that the concentration of polypropylene MPs (<180 µm) at 7% (w/w) had no

231

significant influence on DOC, whereas a high concentration (28 %, w/w) increased the DOC,

232

thus resulting in the formation of high-molecular-weight aromatic compounds (Liu et al., 2017).

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Similarly, our results showed that low concentration MPs (5%, w/w) in fertilized soil did not

234

have a significant effect on the DOC content in the short-term. However, the composition of

235

DOC could have been influenced by MPs, and those of a smaller particle size (M2F) may have

236

significantly accelerated the formation of aromatics in fertilized soil treatments.

237

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240 241 242 243 244 245 246

Fig. 2. Specific ultraviolet absorbance (SUVA) at different wavelengths: SUVA210, SUVA254, SUVA260, SUVA272, SUVA280, and A250/A365 after microplastic addition. Capital letters and lowercase letters designate significant differences in the treatments on the same incubation day and between sampling time of the same treatment, respectively. Different letters mean significant differences and the same letter means no significant difference, Duncan (p < 0.05)).

247

3.2 Effects of MPs on soil CO2, N2O and CH4 emission

248 249

On day 7, the flux of CO2 from the M1F treatment was higher than of that from the CKF and

250

M2F treatments (Fig. 3). On day 15, the M1F treatment has significantly increased the CO2 flux

10

251

in comparison to the CKF and M2F treatments (p = 0.029, LSD, p < 0.05). On day 30, the M1F

252

treatment also had significantly increased the CO2 flux in comparison to the CKF (p = 0.003,

253

LSD, p < 0.05) and M2F treatment (p = 0.023, LSD, p < 0.05), whereby fluxes increased by

254

39.67% and 24.16%, respectively. In comparison to the CKF and M2F treatments, the M1F

255

treatment advanced the release of CO2 on all incubation days except for day 3, thus resulting in

256

the highest cumulative level of 0.35 g CO2 (kg dry soil)-1 (Fig. 3). Compared with the CK, CKF

257

and M2F treatments, the increasing percentages of cumulative CO2 emission of the M1F

258

treatment were 7.16%, 13.88% and 9.79%, respectively. Our results suggested that MPs in

259

fertilized soil promoted the release of CO2, which was also influenced by the particle size of the

260

MPs.

261 262

In comparison to the CK treatment, the addition of nitrogen fertilizer clearly enhanced the

263

release of N2O from the CKF treatment on days 1 and 3. In treatments with MPs added to the soil

264

(M1F or M2F), the emission rates of N2O decreased dramatically during the initial stage.

265

Consequently, the cumulative productions of N2O were significantly lower in the M1F and M2F

266

treatments than in the CKF treatment (Fig. 3: 1045.43 ߤg N2O (kg dry soil)-1 in the CKF

267

treatment versus 149.75 ߤg N2O (kg dry soil)-1 and 165.01 ߤg N2O (kg dry soil)-1 in the M1F and

268

M2F treatments, respectively).

269 270

On days 1 and 3, there was no significant difference in CH4 uptake among the four treatments.

271

From day 15, the results suggest that the CH4 uptake corresponded to the particle size of MPs

272

(M1F in comparison to M2F: day 15, p = 0.012; day 30, p = 0.028; LSD, p < 0.05) in a similar

273

way to that of the other two GHGs (Fig. 3). Larger particle sizes of MPs (i.e., M1F) seemingly

274

decreased the cumulative uptake of CH4.

275 276

The GWP for each treatment was determined using Equation (2) and is shown in Fig. S1.

277

Fertilizer addition may have been responsible for the significant difference between the GWP to

278

for the CK treatment (0.62 g GHGs (kg dry soil)-1), and the CK treatment (0.34 g GHGs (kg dry

279

soil)-1). However, the existence of MPs reduced the GWP due to the reducing effects on the

280

emission of N2O, thus resulting in the lower values for the M1F and M2F treatments in

281

comparison to CKF.

11

282

283

284

285 286 287 288 289 290 291

Fig. 3. CO2, N2O and CH4 hourly flux and cumulative total fluxes. Capital letters and lowercase letters designate significant differences in the treatments on the same incubation day and between sampling time of the same treatment, respectively. Different letters mean significant differences and the same letter means no significant difference, Duncan (p < 0.05)).

3.3 Effects of MPs on microbial community

292 293

3.3.1 Effects of MPs on the diversity of microbes

294

12

295

Since day 3 was at an initial period and showed the extensive change in greenhouse gas emission,

296

while from day 30, the process became stable, soil samples collected on days 3 and 30 were used

297

for bacterial and fungal community analyses. A Venn diagram showed the shared OTUs in each

298

group on both days (Fig. S3). The number of bacterial OTUs ranged from 2697 to 3742 and

299

fungal OTUs ranged from 561 to 883. The common OTUs for bacteria on day 3 and day 30 were

300

1345 and 1443, respectively, whereas for fungi they were 261 and 287, respectively. The α-

301

diversity and the statistics of microbial groups at different classification levels are shown in

302

Tables S2 and S3. For bacteria on day 3, M1F and M2F treatments increased the Chao1 and

303

ACE index in comparison to the CKF treatment, which suggests that MPs improved the richness

304

of the bacterial communities. Furthermore, MPs with a larger particle size (i.e., M1F) increased

305

the diversity (Shannon) of the bacterial community. On day 30, the M1F treatment had the

306

lowest community richness and diversity as well as microbial numbers at different classification

307

levels, whereas these were highest in the M2F treatment with smaller particle size. With regards

308

to fungi, the M1F treatment decreased the community richness and diversity, whereas these

309

increased in the M2F treatment in addition to an increase in microbial numbers at different

310

classification level in comparison to the CKF treatment on days 3 and 30. Our results therefore

311

illustrate the effect of particle size of MPs on α-diversity.

312 313

Principle coordinate analysis (PCoA) was conducted to exhibit the beta diversity of bacterial and

314

fungal communities at the OTU level (Fig. S4). For the bacterial community, a significant

315

(Adonis test, p < 0.05) separation was observed along the primary principal coordinate (36.76%

316

of the total variance) between days 3 and 30. PC2 (30.74% of the total variance) separated the

317

soil samples with or without MPs. As for the fungal community, PC2 (32.14% of the total

318

variance) significantly (Adonis test, p < 0.05) separated the samples by different sampling days.

319

A heatmap based on the top 50 genera (Fig. S5) showed that on day 3, the bacterial community

320

compositions of the M1F and M2F treatments were different from that of the CKF treatment,

321

whereas the fungal community composition of the M1F treatment was different from that of

322

CKF treatment. On day 30, both of bacterial and fungal community compositions in the M1F and

323

M2F treatments were different from those of the CKF treatment. Overall, MPs showed a

324

selective effect on microbes (Jiang et al., 2018; McCormick et al., 2014).

325

13

326

327 328

329 330 331

3.3.2 Effects of MPs on the microbial community structure

(A)

(B) Fig. 4 Community composition at the phylum level (A) bacteria, and (B) fungi.

14

332

The major bacterial phyla were shown in figure 4(A). In soil without MPs (i.e., CK and CKF) on

333

days 3 and 30, Proteobacteria was the dominant group whereas Actinobacteria was dominant in

334

soil with MPs (i.e., M1F and M2F) (Fig. 4). The abundance of Actinobacteria in the M1F

335

treatment increased from 31.15% on day 3 to 36.15% on day 30, whereas it remained relative

336

stable abundance in the M2F treatment. With regards to Proteobacteria, the change of the

337

abundance was not obvious in the CK, CKF, or M2F treatments from day 3 to day 30, but

338

reduced in the M1F treatment from 28.47% on day 3 to 25.53% on day 30. Both M1F and M2F

339

treatments showed a decrease in the abundances of Acidobacteria, Nitrospirae and Bacteroidetes

340

on both days in comparison to the CK and CKF treatments. On day 30, treatments with MPs

341

show a decline in the abundances of Gemmeatimonadetes, Planctomycetes and Cyanobacteria in

342

comparison to the CK treatment. With regard to the particle size of MPs, the abundances of

343

Proteobacteria, Acidobacteria and Bacteroidetes were decreased in the M1F treatment

344

compared with CK and CKF on two days and continuously declined within M1F on different

345

sampling days (day 30 < day 3). Most of the phylum in the M2F treatment maintained relative

346

stable levels from day 3 to day 30, whereas phylum fluctuated in the M1F treatment. Our results

347

are similar to those of recent studies, which showed that MPs could increase the abundance of

348

Actinobacteria and decrease the abundance of Proteobacteria (Huang et al., 2019; M. Zhang et

349

al., 2019). The possible reason for these finding might be that some species in Actinobacteria are

350

able to degrade the PE through synthesis enzymes (Abraham et al., 2017; Muhonja et al., 2018;

351

Singh and Sedhuraman, 2015; M. Zhang et al., 2019).

352 353

With regard to fungi, the major community compositions at the phylum level were shown in

354

figure 4(B), with Ascomycota being the dominant one in all the treatments on days 3 and 30. In

355

the M1F fertilized soil treatment on day 30, we found an increased abundance of Ascomycota

356

(82.23%) in comparison to the CKF treatment (63.29%). In the M2F treatment the abundance of

357

Ascomycota and Zygomycota increased on day 30, but the abundances of Basidiomycota,

358

Chytridiomycota, Ciliophora and Rozellomycota decreased.

359 360

3.2.3 Co-occurrence network analysis

361

15

362 363

364 365 366 367 368 369 370 371

(A)

(B)

(C)

(D)

Fig. 5. Network of co-occurrence: (A) bacteria and (B) fungi genera in soil without MPs (D3CK, D3CKF, D30CK, and D30CKF); (C) bacteria and (D) fungi genera in soil with MPs (D3M1F, D3M2F, D30M1F, and D30M2F). A connection represents for a strong (Spearman's |ρ| > 0.6) and significant (p-value < 0.01) correlation. For each panel, the size of each node is proportional to the abundance of each genera. Red lines represented positive correlations (Spearman's ρ > 0.6), and green lines represented negative correlation (Spearman's ρ < -0.6).

372

Exogenous disturbances such as pollutants lead to changes in soil nutrients, which are a

373

necessary resource for microbes and when nutrients are limited microbes must compete for them.

374

Network co-occurrence analysis based on genera showed that the existence of MPs in soil could

375

change the possible collaboration or competition among different microorganisms (Fig. 5). The

376

addition of MPs changed the network of bacteria relevant to denitrification process (e.g.,

16

377

Nocardioldes and Acidovorax) as well as fungi related denitrification (e.g., Aspergillus,

378

Penicillium, and Chaetomium). The various relationships from mutualism to competition among

379

microbes might influence metabolic functions such as nitrogen circulation (Ghoul and Mitri,

380

2016). Besides that, the co-occurrence network among microbes could be influenced by

381

environmental conditions and supply of resources (Barberán et al., 2012; M. Zhang et al., 2019).

382

Our results showed that MPs changed the DOC content (Fig. 2). Therefore, microbes in soil with

383

MPs might form a distinct network for metabolizing nutrients based on their own features (Arias-

384

Andres et al., 2018a; Jiang et al., 2018; M. Zhang et al., 2019) which might further influence

385

biogeochemical cycling.

386 387

3.4 Correlation analysis between environmental factors and microbial community

388 389

Network analysis between N2O, CH4 and microbes based on different levels are shown in

390

Figures S6 and S7. The denitrification process is shown in Fig. S8. On day 3, the emission of

391

N2O was significantly positively correlated with the phylum Chloroflexi and genera Rhodoplanes

392

(p-value < 0.01) whereas it was negatively correlated with class Thermoleophilia. Species in the

393

phylum Chloroflexi possess denitrification genes such as nirK, and play a key role in nitrogen

394

removal (Long et al., 2018; Zhao et al., 2018). Thermoleophilia has been found to correlate with

395

a decrease in N2O emissions (Brassard et al., 2018).

396 397

On day 30, the N2O emissions were positively correlated with Beijerinckiaceae,

398

Nocardioidaceae, Micromonosporaceae, Geodermatophilaceae, Mycobacteriaceae, Nectriaceae

399

families and Solirubrobacterales order. However, N2O emissions were negatively correlated

400

with Gemmatimonadaceae, Nitrospiraceae families and order Xanthomonadales. Family

401

Micromonosporaceae and order Solirubrobacterales has been found to relate to carbon

402

metabolization, secondary metabolite production, and organic nitrogen metabolism (Anderson et

403

al., 2011; Merloti et al., 2019; Tu et al., 2017; Wang et al., 2019). Xanthomonadales has been

404

reported to possess nirS (Long et al., 2018), Nectriaceae harbors nirK (Chen et al., 2016), and

405

that Gemmatimonadaceae is related to nosZ clade II (Graf et al., 2019). Nocardioidaceae and

406

Mycobacteriaceae have been recognized as NO3− reducers, and bacteria of Nitrospiraceae have

407

been identified as a nitrifiers (Anderson et al., 2011; Redding et al., 2016; Tu et al., 2017).

17

408

Bacteria belonging to Beijerinckiaceae are involved in aerobic CH4 oxidation coupled to

409

denitrification (AME-D) process (Zhu et al., 2016). MPs increased the emission of N2O in our

410

experiment, mainly due to MPs accelerating the NO3- reduction process and increasing the fungi

411

denitrifier Nectriaceae in addition to decreasing Gemmatimonadacea. Previous research has

412

showed that difference in the niche of nirS and nirK harboring denitrifiers might contribute

413

toward different N2O emission (Huang et al., 2019; Jones and Hallin, 2010). Our results showed

414

that in soil with MPs, nirK denitrifier might have been the main driving factor in the NO2- → NO

415

process (Fig. S8).

416 417

With respect to CH4, microbes that are related to CH4-oxidation in the present study included

418

Methylobacteriaceae,

419

Methylophilaceae based on the family level. Methylobacteriaceae was the dominant

420

methanotroph in the CK treatment on days 3 and 30, and in the CKF treatment on day 3.

421

Hyphomicrobiaceae was the dominant methanotroph in the CKF treatment on day 30, and also in

422

treatments with MPs. Hyphomicrobiaceae are reportedly effective methylotrophic bacteria (Xie,

423

2018), and genus Rhodomicrobium within the Hyphomicrobiaceae family have been found to

424

use hydrogen as an electron donor (Singleton et al., 2018). Our results using Spearman’s rank

425

correlation analysis showed that on day 3, the uptake of CH4 was significantly correlated to

426

Hyphomicrobiaceae, whereas on day 30, it was significantly correlated to Rhodomicrobium (Fig.

427

S7).

Methylocystaceae,

Beijerinckiaceae,

Hyphomicrobiaceae

and

428 429

Figure S2 showed M1F treatment enhanced the correlation between CO2 and N2O and

430

correlation between CH4 and A250/A365. Figure S9 reveals the correlation between environmental

431

factors and microbial community based on the phylum level in fertilized soil. The results showed

432

that bacteria Proteobacteria, Actinobacteria, and Acidobacteria, and fungi Ascomycota were

433

significantly influenced by the existence and particle size of MPs. Fungi Ciliophora was

434

significantly correlated with particle size. Bacteroidetes had a significantly negative correlation

435

with the existence of MPs, and its abundance decreased in both M1F and M2F treatments (Fig.

436

4A). Chloroflexi, Nitrospirae and Basidiomycota were significantly correlated to the DOC

437

content. With respect to the relationship between GHGs and microbial community, we found that

438

the abundance of Nitrospirae was negatively correlated to CO2 emissions, and the abundance of

18

439

Cercozoa was negatively correlated with the emission of N2O. The Cercozoa play an important

440

biological role in soil crusts; the latter contributed to nearly 50% of the terrestrial biological N

441

fixation and to CO2 sequestration by acting as a vital reservoir of carbon (Elbert et al., 2012;

442

Fiore-Donno et al., 2018; Maestre et al., 2013).

443 444

4 Conclusions

445 446

MPs have been reported to be widespread in the water environment, but relatively little research

447

has been performed for terrestial system. In this research, we report on the influence of MPs on

448

GHGs fluxes and the potential microbial driving mechanism. Our results showed that MPs at a

449

concetration of 5% (w/w) in fertilized soil did not have a significant effect on the DOC content

450

over the short-term, whereas the composition of DOC was related to the particle size of MPs.

451

The existence of MPs decreased the GWP during the initial stage of the 30-day experiment in

452

fertilized soil due to their decreasing effect on the emission of N2O by changing the abundance

453

of microbes related to N2O emissions and CH4 uptake. In soil with MPs, Actinobacteria replaced

454

Proteobacteria as the dominant phylum. MPs showed a particle size effect on alpha diversity.

455

Microbes in soil with MPs might form a distinct network for metabolizing that is related to their

456

own features. Based on our findings, we conclude that MPs appear to show a selective effect on

457

microbes and pose a serious threat to microbial ecology and biogeochemical cycles, which may

458

further influence the wider ecosystem. With the acclerated release of MPs into the enviroment, it

459

is necessary to take more soil biogeochemical processes into consideration in order to understand

460

better the effects of MPs on soil. In this context, the present study contributed to an improved

461

and more comprehensive understanding of the ecological effects of MPs in the soil.

462 463

ACKNOWLEDGEMENTS

464

This work was supported by the National Natural Science Foundation of China (Nos. U1806216,

465

41877372), the National Key R&D Program of China [2018YFC1802002], the Tianjin S&T

466

Program (Nos. 17ZXSTSF00050, 17PTGCCX00240, and 16YFXTSF00520), and the 111

467

program, Ministry of Education, China (No. T2017002).

468 469

References

19

470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513

Abraham, J., Ghosh, E., Mukherjee, P., Gajendiran, A., 2017. Microbial degradation of low density polyethylene. Environ. Prog. Sustainable Energy 36, 147–154. https://doi.org/10.1002/ep.12467 Anderson, C.R., Condron, L.M., Clough, T.J., Fiers, M., Stewart, A., Hill, R.A., Sherlock, R.R., 2011. Biochar induced soil microbial community change: Implications for biogeochemical cycling of carbon, nitrogen and phosphorus. Pedobiologia 54, 309–320. https://doi.org/10.1016/j.pedobi.2011.07.005 Arias-Andres, M., Kettner, M.T., Miki, T., Grossart, H.-P., 2018a. Microplastics: New substrates for heterotrophic activity contribute to altering organic matter cycles in aquatic ecosystems. Science of The Total Environment 635, 1152–1159. https://doi.org/10.1016/j.scitotenv.2018.04.199 Arias-Andres, M., Rojas-Jimenez, K., Grossart, H.-P., 2018b. Collateral effects of microplastic pollution on aquatic microorganisms: An ecological perspective. TrAC Trends in Analytical Chemistry. https://doi.org/10.1016/j.trac.2018.11.041 Barberán, A., Bates, S.T., Casamayor, E.O., Fierer, N., 2012. Using network analysis to explore co-occurrence patterns in soil microbial communities. ISME J 6, 343–351. https://doi.org/10.1038/ismej.2011.119 Barnes, D.K.A., Galgani, F., Thompson, R.C., Barlaz, M., 2009. Accumulation and fragmentation of plastic debris in global environments. Philosophical Transactions of the Royal Society B: Biological Sciences 364, 1985–1998. https://doi.org/10.1098/rstb.2008.0205 Bastian, M., Heymann, S., Jacomy, M., 2009. Gephi: An Open Source Software for Exploring and Manipulating Networks. https://doi.org/10.13140/2.1.1341.1520 Brassard, P., Godbout, S., Palacios, J.H., Jeanne, T., Hogue, R., Dubé, P., Limousy, L., Raghavan, V., 2018. Effect of six engineered biochars on GHG emissions from two agricultural soils: A short-term incubation study. Geoderma 327, 73–84. https://doi.org/10.1016/j.geoderma.2018.04.022 Browne, M.A., Crump, P., Niven, S.J., Teuten, E., Tonkin, A., Galloway, T., Thompson, R., 2011. Accumulation of Microplastic on Shorelines Woldwide: Sources and Sinks. Environmental Science & Technology 45, 9175–9179. https://doi.org/10.1021/es201811s Cao, D., Wang, X., Luo, X., Liu, G., Zheng, H., 2017. Effects of polystyrene microplastics on the fitness of earthworms in an agricultural soil. IOP Conference Series: Earth and Environmental Science 61, 012148. https://doi.org/10.1088/1755-1315/61/1/012148 Chao, A., 1984. Nonparametric Estimation of the Number of Classes in a Population. candinavian Journal of Statistics 11, 265–270. Chao, A., Yang, M.C.K., 1993. Stopping rules and estimation for recapture debugging with unequal failure rates. Biometrika 80, 193–201. https://doi.org/10.1093/biomet/80.1.193 Chen, H., Yu, F., Shi, W., 2016. Detection of N2O-producing fungi in environment using nitrite reductase gene (nirK)-targeting primers. Fungal Biology 120, 1479–1492. https://doi.org/10.1016/j.funbio.2016.07.012 Christiansen, J.R., Outhwaite, J., Smukler, S.M., 2015. Comparison of CO2, CH4 and N2O soilatmosphere exchange measured in static chambers with cavity ring-down spectroscopy and gas chromatography. Agricultural and Forest Meteorology 211–212, 48–57. https://doi.org/10.1016/j.agrformet.2015.06.004

20

514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559

Cole, M., Lindeque, P., Halsband, C., Galloway, T.S., 2011. Microplastics as contaminants in the marine environment: A review. Marine Pollution Bulletin 62, 2588–2597. https://doi.org/10.1016/j.marpolbul.2011.09.025 de Souza Machado, A.A., Lau, C.W., Till, J., Kloas, W., Lehmann, A., Becker, R., Rillig, M.C., 2018. Impacts of Microplastics on the Soil Biophysical Environment. Environmental Science & Technology 52, 9656–9665. https://doi.org/10.1021/acs.est.8b02212 Dris, R., Gasperi, J., Saad, M., Mirande, C., Tassin, B., 2016. Synthetic fibers in atmospheric fallout: A source of microplastics in the environment? Marine Pollution Bulletin 104, 290–293. https://doi.org/10.1016/j.marpolbul.2016.01.006 Eckert, E.M., Di Cesare, A., Kettner, M.T., Arias-Andres, M., Fontaneto, D., Grossart, H.-P., Corno, G., 2018. Microplastics increase impact of treated wastewater on freshwater microbial community. Environmental Pollution 234, 495–502. https://doi.org/10.1016/j.envpol.2017.11.070 Elbert, W., Weber, B., Burrows, S., Steinkamp, J., Büdel, B., Andreae, M.O., Pöschl, U., 2012. Contribution of cryptogamic covers to the global cycles of carbon and nitrogen. Nature Geoscience 5, 459–462. https://doi.org/10.1038/ngeo1486 Fiore-Donno, A.M., Rixen, C., Rippin, M., Glaser, K., Samolov, E., Karsten, U., Becker, B., Bonkowski, M., 2018. New barcoded primers for efficient retrieval of cercozoan sequences in high-throughput environmental diversity surveys, with emphasis on worldwide biological soil crusts. Molecular Ecology Resources 18, 229–239. https://doi.org/10.1111/1755-0998.12729 Ghoul, M., Mitri, S., 2016. The Ecology and Evolution of Microbial Competition. Trends in Microbiology 24, 833–845. https://doi.org/10.1016/j.tim.2016.06.011 Graf, D.R.H., Saghaï, A., Zhao, M., Carlsson, G., Jones, C.M., Hallin, S., 2019. Lucerne (Medicago sativa) alters N2O-reducing communities associated with cocksfoot (Dactylis glomerata) roots and promotes N2O production in intercropping in a greenhouse experiment. Soil Biology and Biochemistry 137, 107547. https://doi.org/10.1016/j.soilbio.2019.107547 Harrison, J.P., Schratzberger, M., Sapp, M., Osborn, A.M., 2014. Rapid bacterial colonization of low-density polyethylene microplastics in coastal sediment microcosms. BMC Microbiology 14. https://doi.org/10.1186/s12866-014-0232-4 Hawthorne, I., Johnson, M.S., Jassal, R.S., Black, T.A., Grant, N.J., Smukler, S.M., 2017. Application of biochar and nitrogen influences fluxes of CO 2 , CH 4 and N 2 O in a forest soil. Journal of Environmental Management 192, 203–214. https://doi.org/10.1016/j.jenvman.2016.12.066 Horton, A.A., Walton, A., Spurgeon, D.J., Lahive, E., Svendsen, C., 2017. Microplastics in freshwater and terrestrial environments: Evaluating the current understanding to identify the knowledge gaps and future research priorities. Science of The Total Environment 586, 127–141. https://doi.org/10.1016/j.scitotenv.2017.01.190 Huang, R., Wang, Y., Liu, J., Li, J., Xu, G., Luo, M., Xu, C., Ci, E., Gao, M., 2019. Variation in N2O emission and N2O related microbial functional genes in straw- and biocharamended and non-amended soils. Applied Soil Ecology 137, 57–68. https://doi.org/10.1016/j.apsoil.2019.01.010 Huang, Y., Zhao, Y., Wang, J., Zhang, M., Jia, W., Qin, X., 2019. LDPE microplastic films alter microbial community composition and enzymatic activities in soil. Environmental Pollution 254, 112983. https://doi.org/10.1016/j.envpol.2019.112983

21

560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605

Huerta Lwanga, E., Gertsen, H., Gooren, H., Peters, P., Salánki, T., van der Ploeg, M., Besseling, E., Koelmans, A.A., Geissen, V., 2017. Incorporation of microplastics from litter into burrows of Lumbricus terrestris. Environmental Pollution 220, 523–531. https://doi.org/10.1016/j.envpol.2016.09.096 Huerta Lwanga, E., Gertsen, H., Gooren, H., Peters, P., Salánki, T., van der Ploeg, M., Besseling, E., Koelmans, A.A., Geissen, V., 2016. Microplastics in the Terrestrial Ecosystem: Implications for Lumbricus terrestris (Oligochaeta, Lumbricidae). Environmental Science & Technology 50, 2685–2691. https://doi.org/10.1021/acs.est.5b05478 Jaffrain, J., Gérard, F., Meyer, M., Ranger, J., 2007. Assessing the Quality of Dissolved Organic Matter in Forest Soils Using Ultraviolet Absorption Spectrophotometry. Soil Science Society of America Journal 71, 1851. https://doi.org/10.2136/sssaj2006.0202 Jiang, P., Zhao, S., Zhu, L., Li, D., 2018. Microplastic-associated bacterial assemblages in the intertidal zone of the Yangtze Estuary. Science of The Total Environment 624, 48–54. https://doi.org/10.1016/j.scitotenv.2017.12.105 Jones, C.M., Hallin, S., 2010. Ecological and evolutionary factors underlying global and local assembly of denitrifier communities. ISME J 4, 633–641. https://doi.org/10.1038/ismej.2009.152 Kasirajan, S., Ngouajio, M., 2012. Polyethylene and biodegradable mulches for agricultural applications: a review. Agronomy for Sustainable Development 32, 501–529. https://doi.org/10.1007/s13593-011-0068-3 Klein, S., Worch, E., Knepper, T.P., 2015. Occurrence and Spatial Distribution of Microplastics in River Shore Sediments of the Rhine-Main Area in Germany. Environmental Science & Technology 49, 6070–6076. https://doi.org/10.1021/acs.est.5b00492 Law, K.L., Thompson, R.C., 2014. Microplastics in the seas. Science 345, 144–145. https://doi.org/10.1126/science.1256304 Lenka, S., Lenka, N.K., Singh, A.B., Singh, B., Raghuwanshi, J., 2017. Global warming potential and greenhouse gas emission under different soil nutrient management practices in soybean–wheat system of central India. Environmental Science and Pollution Research 24, 4603–4612. https://doi.org/10.1007/s11356-016-8189-5 Leslie, H.A., Brandsma, S.H., van Velzen, M.J.M., Vethaak, A.D., 2017. Microplastics en route: Field measurements in the Dutch river delta and Amsterdam canals, wastewater treatment plants, North Sea sediments and biota. Environment International 101, 133–142. https://doi.org/10.1016/j.envint.2017.01.018 Li, X.-M., Chen, Q.-L., He, C., Shi, Q., Chen, S.-C., Reid, B.J., Zhu, Y.-G., Sun, G.-X., 2019. Organic Carbon Amendments Affect the Chemodiversity of Soil Dissolved Organic Matter and Its Associations with Soil Microbial Communities. Environmental Science & Technology 53, 50–59. https://doi.org/10.1021/acs.est.8b04673 Liu, H., Yang, X., Liu, G., Liang, C., Xue, S., Chen, H., Ritsema, C.J., Geissen, V., 2017. Response of soil dissolved organic matter to microplastic addition in Chinese loess soil. Chemosphere 185, 907–917. https://doi.org/10.1016/j.chemosphere.2017.07.064 Long, X.-E., Huang, Y., Chi, H., Li, Y., Ahmad, N., Yao, H., 2018. Nitrous oxide flux, ammonia oxidizer and denitrifier abundance and activity across three different landfill cover soils in Ningbo, China. Journal of Cleaner Production 170, 288–297. https://doi.org/10.1016/j.jclepro.2017.09.173 Lozupone, C.A., Hamady, M., Kelley, S.T., Knight, R., 2007. Quantitative and Qualitative Diversity Measures Lead to Different Insights into Factors That Structure Microbial

22

606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650

Communities. Applied and Environmental Microbiology 73, 1576–1585. https://doi.org/10.1128/AEM.01996-06 Maaß, S., Daphi, D., Lehmann, A., Rillig, M.C., 2017. Transport of microplastics by two collembolan species. Environmental Pollution 225, 456–459. https://doi.org/10.1016/j.envpol.2017.03.009 Maestre, F.T., Escolar, C., de Guevara, M.L., Quero, J.L., Lázaro, R., Delgado-Baquerizo, M., Ochoa, V., Berdugo, M., Gozalo, B., Gallardo, A., 2013. Changes in biocrust cover drive carbon cycle responses to climate change in drylands. Global Change Biology 19, 3835– 3847. https://doi.org/10.1111/gcb.12306 McArdle, B.H., Anderson, M.J., 2001. FITTING MULTIVARIATE MODELS TO COMMUNITY DATA: A COMMENT ON DISTANCE-BASED REDUNDANCY ANALYSIS. Ecology 82, 290–297. https://doi.org/10.1890/00129658(2001)082[0290:FMMTCD]2.0.CO;2 McCormick, A., Hoellein, T.J., Mason, S.A., Schluep, J., Kelly, J.J., 2014. Microplastic is an Abundant and Distinct Microbial Habitat in an Urban River. Environmental Science & Technology 48, 11863–11871. https://doi.org/10.1021/es503610r Merloti, L.F., Mendes, L.W., Pedrinho, A., de Souza, L.F., Ferrari, B.M., Tsai, S.M., 2019. Forest-to-agriculture conversion in Amazon drives soil microbial communities and Ncycle. Soil Biology and Biochemistry 137, 107567. https://doi.org/10.1016/j.soilbio.2019.107567 Miao, L., Wang, P., Hou, J., Yao, Y., Liu, Z., Liu, S., Li, T., 2019. Distinct community structure and microbial functions of biofilms colonizing microplastics. Science of The Total Environment 650, 2395–2402. https://doi.org/10.1016/j.scitotenv.2018.09.378 Muhonja, C.N., Makonde, H., Magoma, G., Imbuga, M., 2018. Biodegradability of polyethylene by bacteria and fungi from Dandora dumpsite Nairobi-Kenya. PLoS ONE 13, e0198446. https://doi.org/10.1371/journal.pone.0198446 Ng, E.-L., Huerta Lwanga, E., Eldridge, S.M., Johnston, P., Hu, H.-W., Geissen, V., Chen, D., 2018. An overview of microplastic and nanoplastic pollution in agroecosystems. Science of The Total Environment 627, 1377–1388. https://doi.org/10.1016/j.scitotenv.2018.01.341 Nizzetto, L., Futter, M., Langaas, S., 2016. Are Agricultural Soils Dumps for Microplastics of Urban Origin? Environmental Science & Technology 50, 10777–10779. https://doi.org/10.1021/acs.est.6b04140 Oertel, C., Matschullat, J., Zurba, K., Zimmermann, F., Erasmi, S., 2016. Greenhouse gas emissions from soils—A review. Chemie der Erde - Geochemistry 76, 327–352. https://doi.org/10.1016/j.chemer.2016.04.002 R Core Team, 2019. R: A Language and Environment for Statistical Computing. Rabot, E., Wiesmeier, M., Schlüter, S., Vogel, H.-J., 2018. Soil structure as an indicator of soil functions: A review. Geoderma 314, 122–137. https://doi.org/10.1016/j.geoderma.2017.11.009 Redding, M.R., Shorten, P.R., Lewis, R., Pratt, C., Paungfoo-Lonhienne, C., Hill, J., 2016. Soil N availability, rather than N deposition, controls indirect N 2 O emissions. Soil Biology and Biochemistry 95, 288–298. https://doi.org/10.1016/j.soilbio.2016.01.002 Rillig, M.C., 2018. Microplastic Disguising As Soil Carbon Storage. Environmental Science & Technology 52, 6079–6080. https://doi.org/10.1021/acs.est.8b02338

23

651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696

Rillig, M.C., 2012. Microplastic in Terrestrial Ecosystems and the Soil? Environmental Science & Technology 46, 6453–6454. https://doi.org/10.1021/es302011r Rillig, M.C., Ingraffia, R., de Souza Machado, A.A., 2017a. Microplastic Incorporation into Soil in Agroecosystems. Frontiers in Plant Science 8. https://doi.org/10.3389/fpls.2017.01805 Rillig, M.C., Ziersch, L., Hempel, S., 2017b. Microplastic transport in soil by earthworms. Scientific Reports 7. https://doi.org/10.1038/s41598-017-01594-7 Roy, P.K., Hakkarainen, M., Varma, I.K., Albertsson, A.-C., 2011. Degradable Polyethylene: Fantasy or Reality. Environmental Science & Technology 45, 4217–4227. https://doi.org/10.1021/es104042f RStudio Team, 2018. RStudio: Integrated Development for R. Shannon, C.E., 1948. A Mathematical Theory of Communication.pdf. The Bell System Technical Journal 27, 379–423, 623–656. Simpson, E.H., 1949. Measurement of Diversity. Nature 163, 688–688. https://doi.org/10.1038/163688a0 Singh, M.J., Sedhuraman, P., 2015. Biosurfactant, polythene, plastic, and diesel biodegradation activity of endophytic Nocardiopsis sp. mrinalini9 isolated from Hibiscus rosasinensis leaves. Bioresour. Bioprocess. 2, 2. https://doi.org/10.1186/s40643-014-0034-4 Singleton, C.M., McCalley, C.K., Woodcroft, B.J., Boyd, J.A., Evans, P.N., Hodgkins, S.B., Chanton, J.P., Frolking, S., Crill, P.M., Saleska, S.R., Rich, V.I., Tyson, G.W., 2018. Methanotrophy across a natural permafrost thaw environment. ISME J 12, 2544–2558. https://doi.org/10.1038/s41396-018-0065-5 Solomon, S., Intergovernmental Panel on Climate Change, Intergovernmental Panel on Climate Change (Eds.), 2007. Climate change 2007: the physical science basis: contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge ; New York. Song, L., Tian, P., Zhang, J., Jin, G., 2017. Effects of three years of simulated nitrogen deposition on soil nitrogen dynamics and greenhouse gas emissions in a Korean pine plantation of northeast China. Science of The Total Environment 609, 1303–1311. https://doi.org/10.1016/j.scitotenv.2017.08.017 Steinmetz, Z., Wollmann, C., Schaefer, M., Buchmann, C., David, J., Tröger, J., Muñoz, K., Frör, O., Schaumann, G.E., 2016. Plastic mulching in agriculture. Trading short-term agronomic benefits for long-term soil degradation? Science of The Total Environment 550, 690–705. https://doi.org/10.1016/j.scitotenv.2016.01.153 Su Dongxue, Wang Wenjie, Qiu Ling, Wang Hongyan, An Jing, Zheng Guangyu, Zu Yuangang, 2012. Temporal and spatial variations of DOC, DON and their function group characteristics in larch plantations and possible relations with other physical-chemical properties. Acta Ecologica Sinica 32, 6705–6714. https://doi.org/10.5846/stxb201109141347 Tu, Q., He, Z., Wu, L., Xue, K., Xie, G., Chain, P., Reich, P.B., Hobbie, S.E., Zhou, J., 2017. Metagenomic reconstruction of nitrogen cycling pathways in a CO2-enriched grassland ecosystem. Soil Biology and Biochemistry 106, 99–108. https://doi.org/10.1016/j.soilbio.2016.12.017 Wang, N., Yu, J.-G., Zhao, Y.-H., Chang, Z.-Z., Shi, X.-X., Ma, L.Q., Li, H.-B., 2018. Straw enhanced CO2 and CH4 but decreased N2O emissions from flooded paddy soils: Changes in microbial community compositions. Atmospheric Environment 174, 171–179. https://doi.org/10.1016/j.atmosenv.2017.11.054

24

697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737

Wang, Q., Ma, M., Jiang, X., Guan, D., Wei, D., Zhao, B., Chen, S., Cao, F., Li, L., Yang, X., Li, J., 2019. Impact of 36 years of nitrogen fertilization on microbial community composition and soil carbon cycling-related enzyme activities in rhizospheres and bulk soils in northeast China. Applied Soil Ecology 136, 148–157. https://doi.org/10.1016/j.apsoil.2018.12.019 Xie, T., 2018. Perchlorate bioreduction linked to methane oxidation in a membrane biofilm reactor_ Performance and microbial community structure. Journal of Hazardous Materials 9. Zettler, E.R., Mincer, T.J., Amaral-Zettler, L.A., 2013. Life in the “Plastisphere”: Microbial Communities on Plastic Marine Debris. Environ. Sci. Technol. 47, 7137–7146. https://doi.org/10.1021/es401288x Zhang, D., Liu, H., Hu, W., Qin, X., Ma, X., Yan, C., Wang, H., 2016. The status and distribution characteristics of residual mulching film in Xinjiang, China. Journal of Integrative Agriculture 15, 2639–2646. https://doi.org/10.1016/S2095-3119(15)61240-0 Zhang, G., Li, B., Guo, F., Liu, J., Luan, M., Liu, Y., Guan, Y., 2019. Taxonomic relatedness and environmental pressure synergistically drive the primary succession of biofilm microbial communities in reclaimed wastewater distribution systems. Environment International 124, 25–37. https://doi.org/10.1016/j.envint.2018.12.040 Zhang, M., Zhao, Y., Qin, X., Jia, W., Chai, L., Huang, M., Huang, Y., 2019. Microplastics from mulching film is a distinct habitat for bacteria in farmland soil. Science of The Total Environment 688, 470–478. https://doi.org/10.1016/j.scitotenv.2019.06.108 Zhao, S., Wang, Q., Zhou, J., Yuan, D., Zhu, G., 2018. Linking abundance and community of microbial N2O-producers and N2O-reducers with enzymatic N2O production potential in a riparian zone. Science of The Total Environment 642, 1090–1099. https://doi.org/10.1016/j.scitotenv.2018.06.110 Zhen, M., Song, B., Liu, X., Chandankere, R., Tang, J., 2018. Biochar-mediated regulation of greenhouse gas emission and toxicity reduction in bioremediation of organophosphorus pesticide-contaminated soils. Chinese Journal of Chemical Engineering 26, 2592–2600. https://doi.org/10.1016/j.cjche.2018.01.028 Zhu, J., Wang, Q., Yuan, M., Tan, G.-Y.A., Sun, F., Wang, C., Wu, W., Lee, P.-H., 2016. Microbiology and potential applications of aerobic methane oxidation coupled to denitrification (AME-D) process: A review. Water Research 90, 203–215. https://doi.org/10.1016/j.watres.2015.12.020 Ziajahromi, S., Kumar, A., Neale, P.A., Leusch, F.D., 2017. Impact of microplastic beads and fibers on waterflea (Ceriodaphnia dubia) survival, growth and reproduction: Implications of single and mixture exposures. Environmental Science & Technology. https://doi.org/10.1021/acs.est.7b03574 Zubris, K.A.V., Richards, B.K., 2005. Synthetic fibers as an indicator of land application of sludge. Environmental Pollution 138, 201–211. https://doi.org/10.1016/j.envpol.2005.04.013

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Highlights

Smaller particle size microplastics could accelerate the aromatic matters’ formation

Microplastics in fertilized soil could reduce N2O emission Actinobacteria replaced Proteobacteria as the Dominant phylum in microplastics soil

Microplastic size effect was shown on alpha diversity

Microplastics influenced the co-occurrence network among different microorganisms

Conflict of interest The authors declare no competing financial interest.