Ecology

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Effect of warming on physical and chemical conditionsThe water temperature in the warmed treatment was increased by 2.87 ± 0.20 °C in spring and 3.04 ± 0.08 °C in fall, compared to the control (Fig. 1a,b, Table 1). The average temperature in the control treatment, throughout the duration of the experiment, was about 4 °C cooler in spring (14.84 ± 0.03 °C) than in fall (19.01 ± 0.02 °C). In spring, the temperature naturally increased by approximately 4.19 °C from day (d) 10, until the end of the experiment, whereas it remained relatively constant in the fall experiment. It displayed higher diurnal variations in spring than in fall: over the course of the experiments, daily temperature variation ranged from 0.87 to 1.98 °C in spring and from 0.23 to 1.12 °C in fall. The average Daily Light Integral (DLI) in the control treatment, was almost twice as high in the spring experiment (7.93 ± 0.61 mol m−2 d−1) than during the fall (4.61 ± 0.52 mol m−2 d−1) (Fig. 1c,d). Warming did not significantly alter the DLI in fall (Table 1); however, the DLI could not be measured in the warm mesocosms in spring, owing to technical problems.Figure 1Time series of physical and chemical variables. Water temperature (a, b), Daily Light Integral (DLI, c, d), ammonium (NH4+, e, f), nitrates (NO3− + NO2−, g, h), orthophosphate (PO43−, i, j), silicate (SiO2, k, l) concentrations, and N/P ratio (m, n) over the course of the spring (a, c,e, g, i, k, m) and fall (b, d, f, h, j, l, n) experiments in the control (black) and the warmed (orange) treatments. Error bars represent range of observation for the two mesocosms per treatment in spring and the standard deviation for the three mesocosms per treatment in fall. Dotted lines represent the missing data on d10 of the fall experiment due to bad weather conditions. Due to technical difficulties, DLI could not be calculated in the warmed mesocosms of the spring experiment.Full size imageTable 1 Summary of the p values obtained by Repeated Measures Analyses Of VAriance (RM-ANOVA, with treatment as fixed factor and time as random factor) comparing physical parameters and nutrient concentrations in the warmed and control mesocosms.Full size tableNutrient concentrations were measured daily in all mesocosms (Fig. 1, Table 1). Ammonium (NH4+) concentrations were higher in spring than in fall in the controls (0.45 ± 0.08 µM, and 0.41 ± 0.05 µM, respectively). Ammonium concentrations were significantly different, between the control and warmed mesocosms, only at the end of the fall experiment (warmed with a mean of 0.58 ± 0.23 µM, between d11–17; and control with a mean of 0.21 ± 0.09 µM, between d11–17). In contrast, ammonium concentrations did not vary between the control and warmed mesocosms in spring (Table 1). Cohen’s effect size (d) was used to evaluate the magnitude of the effect of warming. Regarding ammonium concentrations, the values were ten times larger in spring than in fall.The nitrate + nitrite (NO3− + NO2−) concentrations in the control treatments were higher in the spring, compared to the fall experiment (0.71 ± 0.08 µM and 0.23 ± 0.02 µM, respectively). Moreover, warming had different effects, depending on the experiment. In spring, the nitrate + nitrite concentrations were significantly higher in the warmed mesocosms from d8 until the end of the experiment, with an average difference of 540.5% between the warmed and control mesocosms, corresponding to a very large d. In fall, nitrate + nitrite concentrations were significantly lower in the warmed mesocosms than in the control, with an average difference of 20.4%, and a medium-sized d.Similar to the nitrate + nitrite concentrations, the orthophosphate (PO43−) concentrations in the control treatment were higher in spring (0.55 ± 0.07 µM and 0.17 ± 0.01 µM, respectively) than in fall. The concentrations were negatively affected by warming throughout the spring experiment, with an average decrease of 9.3%. However, the largest negative effect of experimental warming was observed at the end of fall, with an average decrease of 16.7%, between d15 and d17.Contrary to the nitrate + nitrite and orthophosphate concentrations, the silicate (SiO2) concentrations in the control treatments were, on average, lower in spring (3.31 ± 0.18 µM and 10.42 ± 0.15 µM, respectively) than in fall. Warming had a significant positive effect during the second part of the spring experiment (from d10 to d17), with average concentrations being 27.8% higher in the warmed mesocosms, than in the control. In contrast, the strongest effect of warming on silicate concentrations was observed at the end of the fall experiment, when the silicate concentrations were significantly lower in the warmed mesocosms, by an average of 10.8%, between d15 and d17.The N/P ratio, calculated as the sum of nitrate, nitrite and ammonium concentrations divided by orthophosphate concentration, was 1.99 and 2.39 on average in the control treatment of the spring and fall experiments, respectively (Fig. 1m,n). It was significantly higher over the entire experiments in the warmed treatment by on average 191.5% and 58.8% in spring and fall, respectively (Table 1). In fall, the highest difference between treatments was seen during the second half of the experiment, when the ratio was significantly higher by 133.5% from day 11 to 17.Effects of warming on gross primary production and respiration rates derived from oxygen sensor dataIn the spring experiment, daily GPP varied between 0.19 ± 0.01 and 1.72 ± 0.15 gO2 m−3 d−1 in the control mesocosms (Fig. 2A). It increased during the first half of the experiment (d2–d10), then decreased toward the end of the experiment. In the fall experiment, the daily GPP was lower than what was observed in the control mesocosms in spring and varied between 0.12 ± 0.02 and 0.96 ± 0.16 gO2 m−3 d−1 (Fig. 2B). In spring, warming significantly reduced GPP by 50.9% over the entire experiment, while in fall, warming enhanced GPP by 21.1% over the entire experiment, 32.3% from d4 to d7, and 44.1% from d12 to d17 (Table 2). In spring, when GPP was normalized by the chl-a measured by the high-frequency sensors, it was not significantly different between the treatments, over the entire experiment (Fig. 2C, Table 2). However, it was significantly higher (138%) in the warmed treatment, during the second half of the experiment (d10– d17). In fall, GPP normalized by the chl-a was also significantly enhanced (12%) by warming (Fig. 2D, Table 2).Figure 2Plankton oxygen metabolism parameters. Gross Primary Production (GPP, A, B), GPP normalized by the chlorophyll-a fluorescence (C, D), Respiration (R, E, F), R normalized by the chlorophyll-a fluorescence (G, H), and GPP:R ratio (I, J) in the control (black) and warmed (orange) treatments. Error bars represent range of observation for the two mesocosms per treatment in spring and the standard deviation for the three mesocosms per treatment in fall. In the spring experiment, GPP: Chl-a and R: Chl-a could not be estimated on d1 and d2.Full size imageTable 2 Summary table of the p values and the F-values obtained with the RM-ANOVA (with treatment as fixed factor and time as random factor) comparing the chl-a fluorescence, µ, l, the µ:l ratio, and pigment concentrations in the warmed and in the control treatments over the entire spring and fall experiments or over specific periods defined after trends observed in the data.Full size tableDaily R varied between 0.27 ± 0.02 and 1.92 ± 0.20 gO2 m−3 d−1 in the spring control mesocosms (Fig. 2E). Similar to the daily GPP, it increased during the first half of the experiment (d2– d10), with a strong increase between d8 and d10, before decreasing slowly until the end of the experiment. In the fall experiment, the daily R was lower than in spring, varying from 0.19 ± 0.02 and 1.09 ± 0.14 gO2 m−3 d−1, in the control mesocosms (Fig. 2F). Warming significantly reduced the daily R by an average of 47.9% in spring, while no significant differences were found in fall (Table 2). During both experiments, when daily R was normalized by chl-a, it was not significantly different between treatments over the entire experimental period (Figs. 2G,H), but it was significantly enhanced by warming during the second half of the experiments, by 172% and 49.6%, from d10–17 in spring, and d11–17 in fall, respectively (Table 2).The GPP:R ratio was on average 1.01 and 1.08 in the spring and fall control treatments, respectively (Fig. 2I,J). Consequently, because warming decreased GPP and R to a similar extent in spring, it did not significantly change the GPP:R ratio. Warming significantly increased GPP:R, by an average of 32% in fall (Table 2).Effects of warming on phytoplankton biomass (chlorophyll-a), growth, and loss rates derived from the chlorophyll-a sensor dataThe chl-a fluorescence data was measured using high-frequency sensors, which were inter-calibrated before and after the experiments, and were corrected by the chl-a concentration measured daily by HPLC (see “Methods”). It is hereafter referred to as chl-a. In the spring experiment, the daily chl-a was 5.28 ± 0.21 µg L−1 in the control mesocosms (Fig. 3a,c). A phytoplankton bloom dynamic was observed, with increasing concentrations from d2 to d10, reaching a maximum value of 8.62 ± 0.15 µg L−1, and decreasing concentrations from d10 to d17. The average daily chl-a was lower in fall than in the spring experiment (4.30 ± 0.59 µg L−1) (Fig. 3b,d), and displayed a relatively flat dynamic during the entire experiment, with maximum values on d8 (5.53 ± 0.58 µg L−1).Figure 3Phytoplankton chlorophyll-a, growth and loss rates. High-frequency chlorophyll-a data, uncorrected for Non Photochemical Quenching (NPQ) (a, b), daily average chlorophyll-a data corrected for the NPQ(c, d), phytoplankton growth rate (µ, e, f), loss rate (l, g, h), and µ:l ratio (i, j) in the control (black) and warmed (orange) treatments. Error bars represent range of observation for the two mesocosms per treatment in spring and the standard deviation for the three mesocosms per treatment in fall. In the spring experiment, µ and l could not be estimated on d1 and d12 and, for the latter, the missing data are represented as dotted lines.Full size imageWarming significantly reduced chl-a in both experiments (Table 2): an average of 69.5% from d5 to the end of the spring, and 31.7% from d8 to 15, in the fall experiment. Conversely, warming significantly enhanced chl-a concentrations at the beginning of the fall experiment (19.4% between d2 and d6). Generally, the magnitude of the effect was larger in spring than in fall (Table 2).In the control treatment, µ was higher in spring than in fall (0.44 ± 0.04 d−1 and 0.32 ± 0.05 d−1, respectively; Fig. 3e,f). During both seasons, the maximum µ was observed during the first half of the experiment (spring d7, 0.99 ± 0.01 d−1; fall d4, 0.61 ± 0.03 d−1). Warming enhanced µ by an average of 18.3% and 28.1%, over the entire spring and fall experiments, respectively, and by an average of 56.8% and 50.9%, respectively, from d8 until the end of the experiment (Table 2). The effect size was higher in fall than in spring (Table 2). However, contrary to the general trend of the entire experiment, during spring, warming significantly reduced µ during the first part of the experiment (d2–d7), with an 18.8% mean difference between the treatments.In contrast to µ, l was almost similar between the seasons, with average values of 0.39 ± 0.04 d−1 and 0.40 ± 0.07 d−1, in the control treatments for spring and fall, respectively (Fig. 3g,h). In the spring experiment, warming had a positive effect on the mean l across the study period (37.1%), and even more from d8 to d17 (59.1%), which was larger than the positive effect found for µ. The effect size of warming was not as large in fall, and l was significantly higher in the warmed treatment, although only in the middle of the experiment (20.4% from d7 to d11).When comparing µ and l, the results showed that in spring, µ was higher than l in the control treatment, during the first part of the experiment (d2–d9), and lower during the latter half of the experiment (d10–d17). Warming significantly decreased the µ:l ratio by 28.9%, during the first half of the experiment (D 3–8, Fig. 3i, Table 2), whereas no significant effect was observed in the rest of the experiment. In the fall control treatment, the µ:l ratio was generally lower than that of the spring control (Fig. 3j). Contrary to what was observed in the spring warming, this ratio significantly increased by an average of 92.9%, in the second half of the experiment (d11–d17, Table 2).Effects of warming on phytoplankton pigment concentrationsPhytoplankton pigment composition varied between seasons (Fig. 4). In the spring control treatment, the predominant pigments were fucoxanthin and 19′-hexanoyloxyfucoxanthin (19′-HF), which are mostly associated with diatoms (1.14 ± 0.10 µg L−1) and prymnesiophytes (19′-HF, 2.91 ± 0.14 µg L−1)23,24, respectively (Figs. 4A,B,G,H). The other pigments that were present included peridinin (0.18 ± 0.01 µg L−1), Chl-b (0.14 ± 0.01 µg L−1), zeaxanthin (0.08 ± 0.01 µg L−1), and the specific accessory pigment prasinoxanthin (0.06 ± 0.01 µg L−1), which are associated with dinoflagellates, green algae, cyanobacteria, and prasinophytes, respectively (Figs. 4C–F,I,J)23,24,25.Figure 4Phytoplankton pigment concentrations. Daily pigment concentrations (µg L−1) in the control (black) and warmed (orange) treatments for the spring (A–F) and fall (G–J) experiments. Error bars represent range of observation for the two mesocosms per treatment in spring and the standard deviation for the three mesocosms per treatment in fall. Dotted lines represent the missing data on d10 of the fall experiment due to bad weather conditions. (A, G) fucoxanthin; (B, H) 19′-hexanoyloxyfucoxanthin; (C, I) zeaxanthin; (D, J) chlorophyll-b; (E) peridinin, and (F) prasinoxanthin. Corresponding phytoplankton functional groups are indicated in parentheses.Full size imageIn the fall control treatment, the dominant pigments were the cyanobacteria-associated zeaxanthin (1.78 ± 0.22 µg L−1), the diatom-associated fucoxanthin (1.07 ± 0.27 µg L−1), the green algae-associated Chl-b (0.69 ± 0.14 µg L−1), and the prymnesiophyte-associated 19′-HF (0.68 ± 0.16 µg L−1). Among the main pigments that were identified in the spring experiment, peridinin and prasinoxanthin were either not detected or detected at negligible concentrations in the fall experiment, whereas lutein was detected in fall but not in spring (data not shown).Warming had seasonal effects on pigment concentrations (Table 2). In the spring experiment, warming had a large and significant negative effect on 19′-HF and zeaxanthin concentrations, with mean concentrations decreasing by 75.4% and 75.2%, respectively. Conversely, warming had moderately significant positive effects on peridinin concentration, which increased by an average of 101%.In the fall experiment, warming had a significant negative effect on Chl-b concentration, which decreased by 19.5%, and on zeaxanthin concentration, which significantly decreased in the middle of the experiment (43.4% from d11 to d15). In contrast, a significant positive effect was observed on fucoxanthin concentration, which increased by 210.7%, during the second part of the experiment (between d13 and d17).Relationships between plankton processes, pigment concentrations and environmental parametersPrincipal component analyses (PCA) were used to project plankton processes, pigment concentrations and environmental parameters in a multidimensional space in order to illustrate relationships among variables in both experiments (Fig. 5). For both experiments, GPP and R were clustered together along the first PCA axis, although they appeared closer in spring than in fall (Fig. 5A,B). Conversely, µ was close to ammonium for both experiments, to silicate in spring and to nitrate and nitrite in fall; and l was part of this cluster in spring but not in fall. Concerning phytoplankton pigment composition, in spring, zeaxanthin, associated with cyanobacteria, and 19′-HF, associated with prymnesiophytes, were part of a group together with temperature (Fig. 5C). Similarly, prasinoxanthin, associated with prasinophytes, and Chl-b, associated with green algae, were grouped with DLI and orthophosphate. Finally, peridinin, associated with dinoflagellates, and silicate were clustered together and opposed to fucoxanthin, which is associated with diatoms. In fall, zeaxanthin and Chl-b, representing cyanobacteria and green algae, were part of a group opposed to N-nutrients and temperature, while fucoxanthin was opposed to DLI, silicate and orthophosphate (Fig. 5D).Figure 5Principal component analyses (PCA) of logarithm response ratio (LRR) of plankton processes (A, B) and pigment concentrations (C, D) with environmental parameters for the spring (A, C) and fall (B, D) experiments. GPP: Gross Primary Production, R: Respiration, µ: Phytoplankton growth rate, l: Phytoplankton loss rate, Chl-b: Chlorophyll-b, 19′-HF: 19′-Hexanoyloxyfucoxanthin, Fuco: Fucoxanthin, Prasino: Prasinoxanthin, Zea: Zeaxanthin, DLI: Daily Light Integral.Full size imageTo evaluate specific relationships between phytoplankton processes, environmental variables, and phytoplankton community composition, ordinary least squares linear relationships were assessed for the effects of warming (expressed as the logarithmic response ratio) on GPP, R, µ, and l, nutrient concentrations, DLI, and pigment concentrations (Fig. 6). A significant positive relationship was found between the effects of warming on GPP versus R, and µ versus l (Fig. 6A,B). Moreover, the effect of warming on µ was positively and linearly related to the effects of warming on ammonium in both seasons (Fig. 6C), and to nitrate + nitrite concentrations in spring (Fig. 6D). There was no relationship between the effects of µ on pigment concentrations in the spring experiment, but its effects were positively correlated with the diatom-associated pigment fucoxanthin in fall (Fig. 6E). Similarly, R was positively correlated with fucoxanthin in fall (Fig. 6F). In contrast, significant negative relationships were found between the effects of warming on µ, Chl-b, and zeaxanthin, the pigments associated with green algae and cyanobacteria, respectively (Fig. 6G,H). Similarly, the effect of warming on R was negatively correlated to orthophosphate (Fig. 6I), and the effect on GPP to nitrate + nitrite and peridinin concentrations (Fig. 6J,K).Figure 6Linear relationships between the effect of warming on plankton processes, environment variables and pigment concentrations. Ordinary least squares linear relationships between the effect of warming, expressed as the log response ratio, on GPP, R, µ, and l, and the effect of warming on environmental and pigment variables for the spring (blue circles) and fall (green squares) experiments. Relationships were individually assessed for each experiment. Only statistically significant relationships (p  More

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These authors contributed equally: Gábor Pozsgai, Ibtissem Ben Fekih.State Key Laboratory of Ecological Pest Control for Fujian and Taiwan Crops, Institute of Applied Ecology, Fujian Agriculture and Forestry University, Fuzhou, 350002, ChinaGábor Pozsgai, Ibtissem Ben Fekih, Jie Zhang & Minsheng YouJoint international Research Laboratory of Ecological Pest Control, Ministry of Education, Fuzhou, 350002, ChinaGábor Pozsgai, Gábor L. Lövei & Minsheng YouCE3C – Centre for Ecology, Evolution and Environmental Changes, Azorean Biodiversity Group and Universidade dos Açores, Angra do Heroísmo, 9700-042, Azores, PortugalGábor PozsgaiInstitute of Environmental Microbiology, College of Resources and Environment, Fujian Agriculture and Forestry University, Fuzhou, 350002, ChinaIbtissem Ben Fekih & Christopher RensingBasic Forestry and Proteomics Research Center, College of Life Science, Fujian Provincial Key Laboratory of Haixia Applied Plant Systems Biology, Fujian Agriculture and Forestry University, Fuzhou, 350002, ChinaMarkus V. KohnenLaboratoire de Biologie et de Physiologie des Organismes, Faculté des Sciences Biologiques, Université des Sciences et de la Technologie Houari Boumediène, BP 32 El Alia, Alger, 16111, AlgeriaSaid AmraniDuna-Ipoly National Park Directorate, Költő u. 21, H-1121, Budapest, HungarySándor BércesJuhász-Nagy Pál Doctoral School, University of Debrecen, Egyetem tér 1, H-4032, Debrecen, HungarySándor BércesDepartment of Zoology, Plant Protection Institute, Centre for Agricultural Research, Nagykovácsi út 26-30, H-1029, Budapest, HungaryDávid FülöpFujian University Key Laboratory for Plant-Microbe Interaction, College of Plant Protection, Fujian Agriculture and Forestry University, Fuzhou, 350002, ChinaMohammed Y. M. JaberDepartment of Plant and Environmental Sciences, University of Copenhagen, Thorvaldsensvej 40, 1871, Frederiksberg C, DenmarkNicolai Vitt MeylingDepartment of Algology and Mycology Faculty of Biology and Environmental Protection, University of Łódź, Banacha 12/16, PL-90-237, Łódź, PolandMalgorzata Ruszkiewicz-MichalskaDepartment of Molecular Biotechnology and Microbiology, University of Debrecen, Egyetem tér 1, Debrecen, H-4032, HungaryWalter P. PflieglerFujian Provincial Key Laboratory of Insect Ecology, College of Plant Protection, Fujian Agriculture and Forestry University, Fuzhou, 350002, ChinaFrancisco Javier Sánchez-GarcíaÁrea de Biología Animal, Departamento de Zoología y Antropología Física, Facultad de Veterinaria, Universidad de Murcia, Murcia, 30100, SpainFrancisco Javier Sánchez-GarcíaDepartment of Agroecology, Aarhus University, Flakkebjerg Research Centre, Forsøgsvej 1, DK-4200, Slagelse, DenmarkGábor L. Lövei More

Choice of biowaste treatment plants and sample identifiersCompost samples were collected from four central municipal biowaste treatment plants (denominated as #1 to #4) in Baden-Wurttemberg, Germany (Table 1). All plants used a state-of-the-art two-stage biowaste treatment process comprising of (a) anaerobic digestion/biogas production and (b) subsequent composting of the solid digestate to produce a high-quality mature compost sold for direct use as fertilizer in agriculture. The composts were regularly analyzed by an independent laboratory for quality and residual contamination and consistently fulfilled the quality requirements of the label RAL-GZ 251 Gütezeichen Kompost of the German Bundesgütegemeinschaft Kompost e.V. (www.gz-kompost.de). Plants #1 and #3 produce in addition a liquid fertilizer, which is separated from the solid digestate at the end of stage a) by press filtration and which is also intended for direct use on agricultural soil (replacement of liquid manure). In case of plants #1, #3, and #4 up to 25 wt% of shrub/tree cuttings were added to the solid digestate for composting. All plants used sieving (typically with a 12 or a 20 mm mesh) at the end of the process to assure the necessary purity of their finished composts. Whenever technically possible, we as well took samples of the pre-compost immediately before this final sieving step to evaluate its contribution to the removal of residual BPD fragments. For analysis, composts were passed consecutively through two sieves with mesh sizes of 5 mm and 1 mm, yielding two fragment preparations for IR-analysis namely a > 5 mm fraction corresponding to the contamination by residual “macroplastic” (5 mm is a commonly used upper size limit for “microplastic”, anything larger is macroplastic) and a 1–5 mm fraction corresponding to the regulatory relevant residual contamination by microplastic. The lower limit of 1 mm rather than 2 mm was chosen in anticipation of the expected changes in regulation, where the replacement of the 2 mm limit by a 1 mm limit is imminent.Table 1 Technical data of the investigated plants and incidence of BDP fragments in the sampled composts.Full size tableOccurrence of plastic fragments  > 1 mm in the sampled compostsComposting times of 5–9 weeks were used in the investigated plants (Table 1), which is shorter than the 12 weeks indicated in EN 13432 for the 90% disintegration of a compostable plastic material, but a realistic time span for state-of-the-art technical waste treatment. Since we were not in a position to estimate the quantity of BDP entering the plants, since for technical reasons we were unable to obtain a representative sample, we cannot say, whether any residual BDP detected by us in the finished composts was due to a yet incomplete disintegration process or whether it corresponds to the 10% material still permissible by EN 13432 even after the full composting step. However, in 7 out of the 12 sampled composts and pre-composts fragments with chemical signatures corresponding to the BDPs poly (lactic acid) (PLA) and poly (butylene-adipate-co-terephthalate) (PBAT) were identified in the > 5 mm and/or the 1–5 mm sieving fractions using FTIR analysis3 (Fig. 1; Table 1). All recovered fragments appeared to stem from foils, bags or packaging, since they were thin compared to their length and width (see Suppl Figure S1 for typical examples). Fragments with overlapping signatures, most likely PBAT/PLA mixtures or blends, were also found (see Suppl Figure S2 for the interpretation of the spectra). In addition, the recorded BDP fragment spectra (Fig. 1A) showed high similarity to the FTIR spectra of commercial compostable bags sold in the vicinity of the biowaste treatment plants (Fig. 1B), which together with the geometry of the recovered fragments led us to assuming that the majority of the BDP entered the biowaste in the form of such bags.Figure 1FTIR spectra of BDP fragments from composts and commercial bags. (A) BDP fragments recovered from the composts and (B) the commercial compostable bags. Fragments were coded as follows: p or f for pre-compost or finished compost, followed by the plant number (#1 to #4), an indication of the size fraction ( > 5 mm or 1–5 mm) in which the fragment was found, and finally, the fragment number. Fragment F#1_5mm_4 therefore represents the 4th fragment collected in the  > 5 mm size fraction from the finished compost of plant number 1. Bags were arbitrarily numbered 1–10, see Suppl Table S1 for supplier information. The spectra (in grey) of the reference materials for PLA and PBAT are given as basis for the interpretation. Spectra in red refer to test samples consisting only of PBAT, while those in blue indicate samples composed of PBAT/PLA mixtures.Full size imageThe BDP fragments were found alongside fragments of commodity plastics (mostly PE) in all cases. Finished composts tended to contain fewer and smaller fragments than the corresponding pre-composts. The final sieving of the pre-composts to prepare the finished composts hence appears to be quite effective in removing such fragments, in particular those from the > 5 mm size fraction (Table 1) and for that reason has become state-of-the-art in preparing quality composts (contamination by plastic fragments > 2 mm of less than 0.1 wt%). Given that the size of the fragments is a crucial factor regarding ecological risk, we analyzed the sizes (length Î width) of the BDP fragments in comparison to that of the plastic fragments with signatures of commodity plastics such as PE (Fig. 2). BDP fragments found in a given compost sample tended to be smaller than the fragments stemming from non-BDP materials, which may indicate that BDPs degrade faster or tend to disintegrate into tinier particles than commodity plastics. This may also explain why in the compost from plant #2, no BDP fragments were found in the particle fraction retained by the 5 mm sieve ( > 5 mm fraction), while 19 such particles were found in the fraction then retained by the 1 mm sieve (1–5 mm fraction). Interestingly, plant #2 is the only one included in our study that uses no mechanical breakdown of the incoming biowaste. This reduces the mechanical stress on the incoming material. Mechanical stress can alter the properties of plastic foils such as the crystallinity whereby crystallinity has been shown to influence the biological degradation of BDP such as PLA7.Figure 2Size distribution of plastic fragments  > 1 mm. (A) Fragments found in the finished compost from plant #1, (B) in the finished compost from plant #2, and (C) in the pre-compost from plant #3. For reasons of statistical relevance, only samples containing more than 20 BDP fragments per kg of compost were included in the analysis.Full size imageMaterial characteristics of BDP fragments in comparison to those of commercial biodegradable bagsIn order to verify whether the BDP fragments recovered from the composts differed from the compostable bags in any parameter with possible relevance for biodegradation and environmental impact16, the physico-chemical properties of bags and fragments were studied in detail. Since we wanted to have a maximum of information of the BDP fragments, size/weight was a limiting factor in selecting fragments for analysis. Fragments of at least 1 mg were required for the FT-IR analysis. 5 mg-fragments could be analyzed in addition by 1H-NMR, while the full set of analytics (FT-IR, 1H-NMR, and DSC) required at least 10 mg of sample.For insight into the chemical composition, 1H-NMR spectra of the commercial bags and all suitable BDP fragments were compared (Fig. 3). In case of material mixtures and blends, the 1H-NMR analysis allows quantification of the PBAT/PLA weight ratio in the materials and also of the ratio of the butylene terephthalate (BT) and butylene adipate (BA) units in the involved PBAT polyesters.Figure 31H NMR spectra of BDP fragments from composts and commercial bags. (A) BDP fragments recovered from the composts and (B) the commercial compostable bags. Fragments were coded as follows: p or f for pre-compost or finished compost, followed by the plant number (#1 to #4), an indication of the size fraction ( > 5 mm or 1–5 mm) in which the fragment was found, and finally, the fragment number. Bags were arbitrarily numbered 1–10, see Suppl Table S1 for supplier information. The spectra (in grey) of the reference materials for PLA and PBAT are given as basis for the interpretation. Spectra in red refer to test samples consisting only of PBAT, while those in blue indicate samples composed of PBAT/PLA mixtures. (C) Chemical structures of PLA and PBAT, chemical shifts of the protons are assigned as indicated in the reference spectra in (B).Full size imageThe 1H-NMR spectra corroborate the FTIR measurements in that all investigated commercial bags were made from PBAT/PLA mixtures of varied composition (Table 2). By comparison, some of the fragments, for instance, f#1_5mm_4, appeared to consist of only PBAT. Other fragments, e.g., f#1_1mm_9, were mixtures of PLA and PBAT (Table 2). However, even in the case of PBAT/PLA mixtures, the average PBAT content tended to be higher in the fragments than in the bags, while the BT/BA monomer ratio in the respective PBATs, was also significantly higher in the fragments than in the bags. If we assume the fragments to stem from similar compostable bags as the ones included in our comparison, this would mean that during composting of such a bag, the PLA degrades more quickly than the PBAT, whereas within a given PBAT polyester, the BA unit is more easily degraded than the BT unit. Evidence can indeed be found in the pertinent literature that PLA has faster biodegradation kinetics than PBAT, while BT is more resistant to mineralization than BA17,18.Table 2 Composition of commercial compostable bags and BDP fragments recovered from the composts as analyzed by 1H-NMR.Full size tableNext, differential scanning calorimetry (DSC) was used to analyze fragments compared to commercial bags in regard to the presence of amorphous vs. crystalline domains, a parameter expected to affect biodegradation kinetics and therefore the putative environmental impact of the produced microplastic16 upon release into the environment with the composts. Whereas amorphous domains show glass transition, crystalline domains show melting, both of which can be discerned by the respective phase transition enthalpy in the DSC curves (Fig. 4).Figure 4DSC curves of BDP fragments and compostable bags #1 and #7. Curves for the reference materials (in grey) for PLA and PBAT are given for comparison. Curves were recorded during the first heating run (temperature range: − 50 °C to 200 °C, heating rate: 10 °C min−1). (A) and (B) curves in red refer to test samples consisting only of PBAT, while those in blue indicate samples composed of PBAT/PLA mixtures. Fragments were coded as follows: p or f for pre-compost or finished compost, followed by the plant number (#1 to #4), an indication of the size fraction ( > 5 mm or 1–5 mm) in which the fragment was found, and finally, the fragment number.Full size imageThe curve for the reference PBAT shows a glass transition temperature (Tg) of − 29 °C and a broad melting range between 100 and 140 °C for the crystalline domains, while that of the PLA reference shows a glass transition temperature of 58 °C and a narrower melting peak between 144 °C and 162 °C. The curve for commercial bag #1, which had a comparatively high PLA content, shows a pronounced melting peak in the expected range; the same is the case for fragment p#3_5mm_1 and to a lesser extent for fragment p#3_5mm_9, two fragments, which also have high PLA contents. The DSC curves of the other fragments and bag #1 are undefined in comparison, which is due to their high PBAT content. According to the DSC curves, most of the investigated materials are semicrystalline, i.e., contain both amorphous (glass transition) and crystalline (melting) domains. However, the DCS data alone allow only a qualitative discussion of the differences between fragments and bags.To obtain quantitative data on the crystallinity differences, wide angle X-ray scattering (WAXS) spectra were recorded. WAXS requires fragments at least 3 cm long, which restricted the number of fragment samples to three, all of which were found in pre-compost samples. The corresponding curves are shown in Fig. 5A–C. The spectra of the commercial biodegradable bags are shown in Suppl Figure S3. Foils were in addition prepared by heat pressing from the reference materials for PLA and PBAT in order to include them into the WAXS measurements (Fig. 5D). While the foils produced from the PBAT reference material produced crystallinity peaks at 16.2°, 17.3°, 20.4°, 23.2°, and 24.8°, the foil prepared from the PLA reference material showed only an amorphous halo at 15.5° and 31.5°, which is in accordance with values published in the literature19. A more pronounced crystallinity peak was obtained in the case of an additionally annealed PLA foil.Figure 5WAXS curves with Lorenz fitting for (A) fragment p#3_5mm_1, (B) fragment p#3_5mm_9, and (C) fragment p#4_5mm_2. (D) WAXS curves for foils produced from the PBAT and PLA reference materials; the percent values indicate the crystallinity. The dash lines are the fitting peak curves for the XRD spectrum. Crystallinity can be obtained by dividing the integration area of the fitted peaks by the integration area of the entire spectrum. Fragments were coded as follows: p or f for pre-compost or finished compost, followed by the plant number (#1 to #4), an indication of the size fraction ( > 5 mm or 1–5 mm) in which the fragment was found, and finally, the fragment number.Full size imageIn case of the fragments and bags, the peaks of PLA and PBAT overlapped to some extent in the WAXS spectra, but by conducting Lorenz fitting using Origin software, the overall crystallinity could be calculated as follows:$$chi = { 1}00% , *{text{ Aa}}/left( {{text{Aa }} + {text{ Ac}}} right)$$where χ is the crystallinity and Aa and Ac represent the areas of the amorphous and crystalline peaks.Using this equation, crystallinities of 55% (fragments p#3_5mm_1), 34% (p#3_5mm_9), and 34% (p#4_5mm_2) were calculated for the fragments. The foils prepared in house for the reference materials had similar crystallinities (43% in case of the annealed PLA foil and 26% of the PBAT foil), while the simple PLA foil was amorphous. By comparison, for eight of the commercial bags, crystallinities in the range from 1% to 7% were calculated, whereas these values were 14% and 15% for the remaining two bag types (Suppl Figure S3).The high crystallinity of the larger fragments recovered from the pre-compost samples suggests that crystalline domains of BDP materials may indeed disintegrate more slowly than the amorphous ones, as prior studies on microbial biodegradation have suggested7,8. Admittedly, such large fragments per se would not enter the environment, since the final sieving step used to prepare the finished composts is quite efficient at removing them. However, it is tempting to extrapolate that residual BDP in general are remnants of the more crystal domains of the original material, even though experimental proof of this assumption is at present not possible. 10 wt% of a BDP bag is allowed to remain after standard composting. It is usually assumed that any such residues continue to degrade with comparable speed. However, should these residues correspond to the more crystalline domains, rather than degrading with similar speed as the bulk material, the more crystalline fragments can be expected to persist for a much longer and at present unpredictable length of time in the environment, e.g. when applied to the soil with the composts; in particular, when they are also enriched in PBAT and BT units as suggested by our analysis of the chemical composition. Data from the use of biodegradable foils in agriculture show that the degradation in the environment may take years20. Altogether this may have unforeseen economic and environmental consequences, especially when considering the high fraction of BDP fragments < 5 mm. Putative consequences include changes in soil properties, the soil microbiome and therefore in plant performance21, a factor indispensable for worldwide nutrition.Residues of BDP fragments  1 mm were found in the collected LF samples. This is hardly surprising, given that the LF is produced by press filtration of the digestate after the anaerobic stage. Such a filtration step can be expected to retain fragments > 1 mm in the produced filter cake, which goes into the composting step, leaving the filtrate, i.e. the LF, essentially free of such particles. Anaerobic digestion is currently not assumed to contribute significantly to the degradation of BDP17,22, but the process conditions (mixing, pumping) may promote breakdown of larger fragments, particularly when additives such as plasticizers23 leach out of the material.Since the residual solids content of the LF is low (plant #1: 8.6 wt%, plant #3: 5.8 wt%), a combination of enzymatic-oxidative treatment and µFTIR imaging originally developed for environmental samples from aqueous systems24,25 could be adapted for the analysis (size and chemical signature) of particles in the LF down to a size of 10 µm. The corresponding data are compiled in Table 3. In all cases, residual fragments from PBAT-based polymers represented the dominant plastic fraction in the investigated samples; i.e. approximately 53% of all plastic particles in the LF from plant #1 (11,520 BDP particles per liter) and 65% in the case of plant #3 (12,480 BDP particles per liter). Liquid manure is applied several times a year to fields at a concentration of 2–3 L m−2. According to our analysis > 20,000 BDP microparticles of a size ranging from 10 µm to 500 µm enter each m2 of agricultural soil whenever LF is applied on agricultural surfaces.Table 3 Microplastic fragments (BDP/all) found per liter of liquid fertilizer.Full size tableDue to the complexity of the matrix, a similar analysis of individual plastic fragments  1 mm. Six compost samples representing the more contaminated ones based on the content of fragments > 1 mm, namely, f#1, f#2, p#3, f#3, p#4 and f#4 (nomenclature: f or p for finished or pre-compost, followed by plant number), were extracted with a 90/10 vol% chloroform/methanol mixture. The amounts of PBAT and PLA in the obtained extracts were then quantified via 1H-NMR (Table 4). Briefly, the intensity of characteristic signals in the extract spectra of the compost samples (see Suppl Figure S4) were compared to peak intensities produced by calibration standards of the pure polymer dissolved at a known concentration in the chloroform/methanol. All samples and standards were normalized using the 1,2-dichloroethan signal at 3.73 ppm as internal standard. See also Suppl Figure S5 for an exemplification of the quantification of the PBAT/PLA ratios. Based on the amounts of PBAT and PLA extracted from a known amount of compost, the total mass concentration (wt% dry weight) of these polymers in the composts was calculated.Table 4 Evidence of PBAT and PLA residues caused by fragments  2 mm. Moreover, residues of PBAT and PLA were found in all investigated compost samples, including the finished compost from plant #4, which had shown no contamination by larger BPD fragments (Table 1). The pre-compost from that plant had shown a few contaminating BDP fragments in the > 5 mm fraction. However, in regard to the fragments More

Department of Environmental Science, Policy and Management, UC Berkeley, Berkeley, CA, USAT. F. Keenan, X. Luo, Y. Zhang & S. ZhouClimate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USAT. F. Keenan, X. Luo, Y. Zhang & S. ZhouDepartment of Geography, National University of, Singapore, SingaporeX. LuoARC Centre of Excellence for Climate Extremes, Sydney, New South Wales, AustraliaM. G. De KauweClimate Change Research Centre, University of New South Wales, Sydney, New South Wales, AustraliaM. G. De KauweSchool of Biological Sciences, University of Bristol, Bristol, UKM. G. De KauweHawkesbury Institute for the Environment, Western Sydney University, Penrith, New South Wales, AustraliaB. E. MedlynDepartment of Life Sciences, Imperial College London, Ascot, UKI. C. PrenticeDepartment of Biological Sciences, Macquarie University, North Ryde, New South Wales, AustraliaI. C. PrenticeDepartment of Earth System Science, Tsinghua University, Haidian, Beijing, ChinaI. C. Prentice & H. WangDepartment of Environmental Systems Science, ETH, Zurich, SwitzerlandB. D. StockerSwiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, SwitzerlandB. D. StockerDepartment of Biological Sciences, Texas Tech University, Lubbock, TX, USAN. G. SmithPhysical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, CA, USAC. TerrerDepartment of Civil and Environmental Engineering, Massachusetts Institute of Technology, Boston, MA, USAC. TerrerSino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, ChinaY. ZhangLamont-Doherty Earth Observatory of Columbia University, Palisades, NY, USAS. ZhouEarth Institute, Columbia University, New York, NY, USAS. ZhouDepartment of Earth and Environmental Engineering, Columbia University, New York, NY, USAS. ZhouState Key Laboratory of Earth Surface Processes and Resources Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing, ChinaS. Zhou More