In this paper Pukanszky model for

In this paper, Pukanszky model for Alprostadil nanocomposites reinforced with spherical nanoparticles is modified based on Nicolais-Narkis model for yield strength. Subsequently, “a” interfacial parameter in Nicolais-Narkis model is linked with “B1” parameter in modified Pukanszky model to express “a” as a function of thickness “ri” and strength “σi” of interphase. In addition, the correlations among “a”, “ri” and “σi” are demonstrated by 3D and contour plots and the effects of “ri” and “σi” on “a” parameter are well discussed.
2. Background
A model for yield strength of polymer composites was suggested by Nicolais and Narkis [14]. When filler and polymer have no interfacial adhesion, the particles cannot bear the load and the load is carried only by the polymer matrix. As a result, yielding is assumed to happen in the minimum cross section of matrix (A), which is perpendicular to the load direction as:equation(1)A=1-AfA=1-Afwhere “Af” is the cross section of particles. In this condition (no adhesion), the strength of a composite depends on the effective region of load-bearing matrix in absence of filler (1 ? Af) as:equation(2)σc=σm(1-Af)σc=σm(1-Af)where “σc” and “σm” are the yield strengths of composite and matrix, respectively. This equation can be rewritten as:equation(3)σR=1-AfσR=1-Afwhere “σR” is the relative yield strength as σR = σc/σm. Assuming n3 randomly dispersed particles in a unit cube, “Af” is given by:equation(4)Af=π(nr)2Af=π(nr)2where “r” is the radius of particles. Also, the volume fraction of n3 particles (??) is equal to:equation(5)?=43π(nr)3
Considering Eq. (6) into Eq. (3), Nicolais-Narkis model for yield strength of composites is expressed as:equation(7)σR=1-a?2/3σR=1-a?2/3where “a” is equal to 1.21 in the case of no adhesion (see Eq. (6)). However, if a good interfacial adhesion is provided between matrix and filler, the interfacial layer can transfer a small portion of stress from matrix to filler. In spindle apparatus status, the yield strength includes a contribution of both matrix and filler properties. Therefore, the value of “a” becomes smaller than 1.21, which displays the stronger adhesion at interface. So, “a” is an interfacial parameter which demonstrates the properties of interphase/interface.

Other authors also analyzed the suitability of

Other authors also analyzed the suitability of humification indices to evaluate progress in humification. For example Sánchez-Monedero et al. (1999) analyzed 4 different indices, i.e. HR, HI (humification index, HI = (CHA/CTOC) · 100), DP and PHA (percentage of HA, PHA = (CHA/CHS) · 100) to evaluate humification during composting of six kinds of waste (primary aerobic sewage sludge, cotton waste, sorghum bagasse, pine bark, brewery sludge and the organic fraction of selectively collected municipal solid waste) in different mixtures. They showed that, for all mixtures, DP was the most sensitive indicator for evaluation of the humification processes. The values of DP in mature compost were from 1.58 to 3.07-times higher than in the feedstock. Moreover, changes in the value of other indicators were less evident. Similarly, Hsu and Lo (1999) showed a considerable increase in the values of DP during composting of pig manure, from 0.60 in the feedstock to 3.33 after 122 days of composting. Moreover, they GSK503 noted increases in PHA from 16.4 to 47.4. Similar results were obtained by Paredes et al. (2001), although they composted different waste (sewage sludge, industrial waste from orange juice extraction, and cotton gin waste). They found that humification progress was best indicated by increases in DP and PHA. However, they showed that HR and HI generally did not show a clear tendency during the composting process. In contrast, Jouraiphy et al. (2005) noted significant changes in all analyzed indices (HR, HI, PHA and DP) during composting of sewage sludge and green waste mixtures. After 135 days of composting there was an increase in HR from 18.1 to 27.9; HI from 7.4 to 19.2; PHA from 40.9 to 68.9 and DP from 0.69 to 2.21.
It is worth emphasizing that, in the present study, a continuous increase in the values of DP occurred during the entire composting process, whereas the most intense increase in the concentration of HS occurred during the first 3 months of the process (after this time the rate of HS formation was low). As a result, the amount of HS in compost matured for a longer period of time differed only slightly from that matured for a shorter period of time. This means that lengthening the maturation time affects mainly the polymerization of FA to HA, i.e. transformation from one kind of HS to another does not considerably increase the total concentration of humic substances. This small change in total HS concentration has practical significance because it means that it is possible to use compost after 3 months of maturation. This conclusion is supported by the author’;s previous study, which shows that it is possible to use compost after 3 months of maturation for stabilization of metals in contaminated soil (Gusiatin and Kulikowska, 2015). That study examined how the redistribution pattern, metal mobility and stability of Cu and Zn were affected by the maturation time (3, 6 and 12 months) of sewage-sludge compost that was added to the contaminated soil. Although Cu redistribution, bioavailability and stability were favorably affected by compost addition, these results were not affected by lengthening the maturation time of the compost and thus increasing the share of HA in the compost.
4. Conclusions
In the present study, the kinetic constants of OM removal were an order of magnitude higher than the kinetic constants of humification during sewage sludge composting. Organics removal occurred mainly during the first 15 days of composting, whereas humification occurred most intensively during the first 3 months of composting, as indicated by the concentration profiles of OM, HS and HA. Lengthening compost maturation time over 3 months increased HS concentration only slightly but the polymerization of FF to HA took place, as shown by the DP values. The high content of HS (182 mg C/g OM) indicated that the compost could also be used in soil remediation, both as an amendment in stabilization or as a source of HS in soil washing.

In the past FT IR measurements have

In the past, FT-IR measurements have been proven to be less error-prone and to be an appropriate method to assess the stage of organic ZD 7288 decomposition in solid waste materials very quickly (Smidt and Meissl, 2007). However representative sampling of solid waste is very time consuming and challenging (e.g. perforation of the coverage, excavation, laboratory sample preparation) due to the heterogeneity of the landfilled solid waste mass. Compared to solid waste sampling the collection and control of leachate emissions is less complicated and time consuming and already ZD 7288 state-of-the-art, and e.g., obligatory in Austria (Bundesministerium für Land- und Forstwirtschaft, Umwelt und Wasserwirtschaft, 2008 and ?WAV, 2008). The application of FT-IR spectroscopy on landfill leachate has already been used in a preliminary study to characterize freeze-dried leachate from landfill simulation reactors (Gamperling et al., 2009 and Smidt and Meissl, 2007). Leachate characteristics are reflected by a specific infrared spectroscopic pattern. Based on the relationship between results of reference analyses and FT-IR spectra partial least squares regression (PLS-R) models for the prediction of NH4-N, NO3-N, SO4-S and DOC were calculated (Gamperling et al., 2009).
In the present study a large number of leachate samples from six different landfill sites, one lysimeter and two long-term investigations in laboratory simulation reactors were investigated by classical wet chemical analyses and by FT-IR spectroscopy. FT-IR spectra of leachates were recorded from the liquid samples. FT-IR spectra and results from reference analyses were correlated to develop models for the prediction of ammonium-nitrogen (NH4-N), nitrate-nitrogen (NO3-N), sulfate-sulfur (SO4-S) and dissolved organic carbon (DOC). The application of the developed prediction models on a single FT-IR spectrum of an unknown leachate sample provides the required parameters reliably and quickly.
2. Material & methods
2.1. Materials, sampling and sample preparation
Leachate gathered from abandoned Austrian municipal solid waste (MSW) landfills, a field-trial lysimeter and lab-scale landfill simulation reactors (LSR) was analyzed using both Fourier Transform-Infrared (FT-IR) spectroscopy and classical wet chemical analyses. Six different abandoned Austrian MSW landfills comprising different sections were sampled quarterly over a period of 15 months. One of these landfills and the lysimeter have been remediated by in-situ aeration. The lysimeter comprised 4 chambers filled with the same MSW but covered with different substrates (chamber A: sewage sludge – compost cover, chamber B: sand – compost cover, chamber C: silt – loess cover, chamber D: loess cover) Details of the landfills are listed in Table 1. The sample set comprises 295 leachate samples.
Table 1.
Metadata of the sampled landfills.LandfillDeposited waste materialSectionPeriod of landfillingIn-situ aeration performedLandfill 1Household waste, bulky waste, construction waste, household-type commercial wasteSection 1/21975-1990NoSection 3/4/51988-1997NoLandfill 2Household waste, bulky waste1982-2003NoLandfill 3Household waste, bulky waste, construction waste, household-type commercial waste1970-1985NoLandfill 4Household waste, bulky waste, construction waste, household-type commercial waste1977-1985NoLandfill 5Household waste, construction waste, bulky waste hospital waste, limeSection 1/2/3Since 1985NoSection 4/5Younger than 1985NoSection 2/1Till 2000NoCollection basinLandfill 6Household waste, bulky waste, household-type commercial waste, residues of waste composting, construction waste1976-1996YesLysimeterResidual wasteChamber A2008YesChamber B2008YesChamber C2008YesChamber D2008YesFull-size tableTable optionsView in workspaceDownload as CSV
For lab scale experiments MSW from two different Austrian landfills with different waste composition and reactivity has been excavated. Material I was 10-15 years old household waste, material II was a mixture of household and bulky waste with the same age. Gas-leak proof landfill simulation reactors (LSR, ? = 20 cm, h = 65 cm) made of acrylic glass were filled with about 6 kg (material I) and 9 kg (material II) dry matter of MSW (sieved < 20 mm). The experiment was set up in a climate chamber at 24 °C. During the investigation of material I 3 columns were kept anaerobic as a reference, 6 columns have been aerated right from the start. All columns filled with material II have been kept anaerobic right from the start. After 6 weeks of weekly irrigation 7 columns were aerated and 3 columns have been kept anaerobic. Aeration was conducted continuously, depending on the reactivity of the waste material, from the bottom, irrigation with 375 ml H2O each was performed weekly at the top of the column. The amount of irrigation in the lab-scale accorded with the annual precipitation in Austria. Leachate samples were taken 24 h after irrigation from the bottom of the columns. Then the samples have been frozen and stored until analysis of NH4-N, NO3-N, SO4-S and dissolved organic carbon (DOC). At the beginning of the experiment and after specified intervals reactivity (respiration activity and gas generation sum) of solid waste samples was investigated.

Unlike waste cell stagnation of

Unlike waste cell 3, stagnation of VMICS at the end of the injection was not noted and consequently the wetting rate WR presented in Fig. 7b did not reach a value close to 0. However, as for waste cell 3, the wetting rate was very high at the beginning of the injection and decreased with time (from 240 to 50 m3/h).
Fig. 7c presents constant ASR values of around 1.8, proving once again that: (i) the leachate infiltration area is more extended laterally than vertically and (ii) the anisotropy of shape is almost constant during the injection experiment.
4. Discussion
Many authors emphasized the heterogeneous nature of waste media (Beaven and Powrie, 1995, McCreanor and Reinhart, 1996, Rosqvist and Destouni, 2000 and Tinet et al., 2011), especially for older landfills where landfilling is uncontrolled. Indeed, the heterogeneous nature of waste is attenuated in modern waste landfills where the installation is performed routinely.
However, to our knowledge, no study has attempted to compare leachate flow between different waste deposit Pentobarbital and injection experiments to assess if leachate hydrodynamic behaviour differs from one waste deposit cell to another. This comparison is important so as to determine if one hydrodynamic model can predict leachate flow in a repeatable manner to provide useful information for LIS design.
This section therefore attempts to answer this question by comparing the hydrodynamic information derived from MICS between the two injection experiments of waste cells 3 and 4. Then, to enhance this comparison, we will refer to the results of laboratory and field studies presented in the literature. Finally, we will attempt to provide information on the waste medium, which could be useful for subsurface flow modelling in the second paper.
4.1. Hydrodynamic analysis and comparison between the two waste deposit cells
The two injection experiments conducted in Champs-Jouault waste cells 3 and 4 have similar injection parameters with a constant 2-m-high pressure head into the LIS and comparable volume of injected leachate (91 m3 over 7 h for waste cell 3 and 72 m3 over 9 h for waste cell 4). The main differences between the two injection experiments were: (i) the size of the perforated pipes used (Fig. 1e and f) and (ii) the silty clay layer in the horizontal trench of waste cell 3, which does not appear as impermeable as expected (Fig. 6b). Despite these differences, similar hydrodynamic behaviour can be observed on Fig. 7a and b: (i) VMICS values (depending on Vinj) are comparable in the two injections and (ii) the two curves present similar trends with a high wetting rate at the beginning of the infiltration decreasing with time (Fig. 7b). A very high wetting rate was observed during the first 3 h of injection, with approximately 750 m3 of wetted waste estimated by MICS for only 40 m3 of injected leachate.
The stabilization of VMICS observed on the two last time steps of waste cell 3 (Fig. 7a), characterized by a wetting rate close to 0 (Fig. 7b), could stem from the sensitivity of the ERT measurements. Indeed, at the end of the injection monitoring, the volume delimited by MICS reached the boundaries of the high sensitivity area, ending near the depth of 5 m (Fig. 6b). MICS was not able to detect a deeper infiltration due to the ERT limitations. The limits of MICS are presented in detail in Audebert et al. (2014).
In waste cell 4, the high sensitivity area is deeper than in waste cell 3 because of the buried electrode line 4. The ERT sensitivity was not a limitation for the use of MICS on waste cell 4.
Despite this limitation, ASR values for the two injection experiments and the five corresponding time steps, are all remarkably close, with an almost constant value near 2. This implies that for the two waste deposit cells, the leachate infiltration area is twice as wide as deciduous is deep at all time steps.
The apparent porosities εa, representing the volume of pores available for leachate flow, were also very close for the two injection experiments with values ranging between 3 and 9%.

Although the impact strength of the ABS kJ

Although the impact strength of the ABS (80 kJ/m2) (Budtov et al., 1978) is close to that of the HIPS and despite the chemical similarity of these two materials, ABS brings down the recycled HIPS impact strength.
Lastly, as PP has a lower impact resistance than HIPS, it IRAK inhibitor 1 can be observed that PP significantly drops the impact strength of the recycled HIPS, showing the incompatibility of PP and HIPS, even at low rates of PP.
5.2. Fracture surface observation
The SEM observation, according to the protocol previously defined in paragraph 3, of the fracture surface of HIPS +8 wt% PS provides an explanation of the low influence of PS as a contaminant (Fig. 7).
Fig. 7. Fracture surface of a specimen of recycled HIPS containing 8 wt% PS.Figure optionsDownload full-size imageDownload as PowerPoint slide
The PS impurity takes the form of very small nodules (clearer parts on ESEM image) with a diameter less than 1 μm in HIPS. The rate of these nodules can be estimated at around 5 wt%. The small size of PS nodules proves that the undissolved fraction of PS is compatible with HIPS. This means that PS is partially miscible with HIPS. In addition, the low number of cavities confirms the good adhesion between the nodules of PS and the HIPS matrix. These three points explain why the PS impurity does not alter HIPS impact strength.
SEM Observations of the fracture surface of the HIPS containing 8 wt% ABS can provide an explanation to the incompatibility between recycled HIPS and ABS (Fig. 8).
Fig. 8. Fracture surface of recycled HIPS sample containing 8 wt% ABS.Figure optionsDownload full-size imageDownload as PowerPoint slide
The ABS contaminating the HIPS takes the form of 1-3 μm diameter nodules. The rate of these nodules was established at 8 wt%. The ABS impurity appears entirely as nodules, which shows a lack of miscibility between ABS and HIPS. In addition, some craters resulting from the pullout of ABS nodules are observed, which reveals a poor adhesion between ABS and HIPS. This illustrates that ABS and HIPS are not compatible and explains how even a low rate of ABS can lower the impact strength of recycled HIPS.
SEM observations of the fracture surface of the HIPS containing 8 wt% PP show the complete incompatibility between HIPS and PP (Fig. 9).
Fig. 9. Fracture surface of recycled HIPS sample containing 8 wt% PP.Figure optionsDownload full-size imageDownload as PowerPoint slide
PP contaminating HIPS takes the form of large size nodules from 3 to 5 μm diameter. The rate of these nodules may be established at approximately 8 wt%. This highlights the immiscibility between PP and HIPS. In addition, it is possible to observe many craters resulting from the pullout of PP nodules, which reveals the poor interface between PP and HIPS. Hence, PP behaves as a filler without cohesion with the matrix which lowers impact strength.
5.3. ESEM in-situ tensile tests
In-situ ESEM tensile tests focus that damage phenomena behind the notch are very heterogeneous whatever the impurities. Two phases of the test can be considered, namely an initiation phase of the crack initiation followed by a propagation phase.
Fig. 10 displays results obtained from in situ tensile tests and before the crack propagation. The evolution of load as a function of longitudinal (Eyy) and transverse (Exx) strains at the tip of the notch is calculated subsequently testing, for ABS, PS and PP impurities incorporated at 1 and 8 wt%. Shear strain which was found very small, is not represented on this graph.
Fig. 10. Load/Lagrangian strain curves for PP (a), ABS (b) and PS (c) impurities at 1 and 8 wt%.Figure optionsDownload full-size imageDownload as PowerPoint slide
In all cases, the transverse strain is negligible. As expected, this result indicates that the solicitation at the tip of the notch is biaxial (also known as pure shear solicitation). All these tests report a local strain before crack initiation between 0.47 and 0.60.
The higher is the rate of impurities, the higher is the ultimate load and the lower is the ultimate strain. To a certain extent, this phenomenon depends also on the nature of impurities: The impurity rate sensitivity on the ultimate strain is negligible for ABS, significant for PS and very important for PP. The latter presents a 29% decrease when the impurity rate increases from 1 to 8 wt%. A softening behavior is observed for PS and ABS when the elastic limit is crossed, while PP presents a quasi-perfect plastic behavior.

Epoxomicin Fig Biogas composition measurements as a function of time

Fig. 3. Biogas composition measurements as a function of time, for the three landfill-bioreactors [R1 (), R2 (), R3 ()], for (a) CO2, (b) CH4 and (b) O2.Figure optionsDownload full-size imageDownload as PowerPoint slide
Regarding CH4 profiles during the Epoxomicin phase, R1 shows an increase until day 114, with a subsequent decrease until its end; a continuous decrease is noticed for R2, which slows down in the second part of this phase, while for R3 an increasing trend is observed in the values, which are then kept constant after day 143. A behavior similar to that observed for R1 was reported by Cossu et al. (2003), while the results obtained by Xu et al. (2014) resemble the pattern referring to R2. It is noted that CH4 contents of the biogas being produced in R2 and R3 are found higher than in R1. During the aerobic phase no CH4 is detected in the biogas.
Finally, as expected, there is a lack of O2 in the biogas during the anaerobic phase, while values spike after aeration startup and afterwards begin to drop until the end. It is noted that negative O2 values during the anaerobic phase are most likely due to an error in instrument calibration. Nevertheless, this error was limited only to O2 values and was fixed in the second part of the experiment.
The presence of CH4 and CO2 in the biogas during the anaerobic phase confirms the function of anaerobic microbial populations, which however was inhibited during the aerobic phase, hence the reduction in the concentrations of these two gases. Additionally, the latter might also be a result of the dilution of biogas due to the introduction of air in the bioreactors.
3.4. Leachate measurements
3.4.1. pH, redox potential, electrical conductivity
pH, redox and EC profiles as a function of time are shown in Fig. 4. For the three parameters similar patterns are observed for all bioreactors. Starting with pH (Fig. 4a), it is maintained relatively stable during the anaerobic phase, with a few lower values in the first days and a slight increasing trend approaching its end, specifically after day 128. During the whole anaerobic phase pH values are found in the acidic range, while after aeration startup an increase is noticed, especially pronounced for R2 and R3. Behaviors such as those observed in the present study for both anaerobic and aerobic conditions have also been noticed by other authors (Cossu et al., 2003, Erses et al., 2008, Sekman et al., 2011 and Xu et al., 2014).
Fig. 4. Leachate measurements as a function of time, for the three landfill-bioreactors [R1 (), R2 (), R3 ()], for (a) pH, (b) redox and (b) EC.Figure optionsDownload full-size imageDownload as PowerPoint slide
Redox potential is often used to verify the mechanisms and reactions that comprise waste degradation processes, and to determine the prevalence of oxidizing or reducing conditions (Bilgili et al., 2007 and Nikolaou et al., 2010). The data presented in Fig. 4b manifests significant fluctuations during the first few days of the experiment, for all bioreactors and especially for R3, with the elevated values between days 25 and 65 being considered outliers. Afterwards, redox seems to have a general decreasing trend, which becomes even more intense after day 128, and then during the aerobic phase. A similar decrease under anaerobic conditions was observed by Bilgili et al. (2007). As noticed for CH4, in the case of redox as well, the curves for R2 and R3 in the aerobic phase are found more detached from the R1 curve.
Electrical conductivity measures the ability of a solution to convey an electric current and when determined in landfill leachate, it is representative of the total concentration of ionic solutes in the sample (Jun et al., 2007). During the anaerobic phase an initial increase is noted in EC (Fig. 4c), until the attainment of peak values on days 77 for R1 and 73 for R2 and R3. Subsequently, a decrease is noticed, with the lowest value being reported on day 132 for all bioreactors. Minor fluctuations towards the end of the anaerobic phase, are followed by a sudden increase after startup of the aerobic phase, which continues until day 198, when the highest values are observed in all cases. In the final part of the experiment EC decreases abruptly. Valencia et al. (2011) observed similar trends for leachate conductivity, in anaerobic bioreactor landfill simulators.

The comparison of methane production between pH based

The comparison of methane production between pH-based controlled aeration and oxygen consumption-based control is shown in Table 3. In the study by Xu et al. (2015), aeration was stopped when the leachate pH reached 7.0. However, 60 days (air supplied: 3044 L/kg VS) and 75 days (air supplied: 4870 L/kg VS) were required to reach pH 7 in both columns with an aeration frequency of 4 times/day and 2 times/day, respectively. With the pH-based aeration control method, the leachate collected from the bottom of the reactors might not properly represent the status of MSW decomposition. Thus it could lead to improperly estimating the aeration period, resulting in losing organic matter for methane recovery. Unlike the previous attempts, in this research, aeration frequencies were adjusted by oxygen consumption rates. Although the initial pH of the leachate was around 5 during 50 days of aerobic pretreatment, methane was rapidly generated in both reactors C1 and C2, as shown in Fig. 4. As seen in Table 3, C2 had the highest cumulative methane generation of 75 L/kg VS among the experimental bioreactor columns for 80 days. As exhibited in the results, a gradual reduction of aeration frequency during aerobic pretreatment of MSW showed great landfill performance with respect to methane recovery from MSW decomposition.
3.4. Implications and limitations
This study focused on conditioning of MSW for enhancing the DCC-2618 degradation of MSW using gradually reduced aeration. Because MSW is heterogeneous and shows dynamic changes during decomposition, it is difficult to determine aeration parameters for MSW pretreatment. The use of ΔO2/30 min is a simple approach to control the aeration frequency for the pretreatment. This approach is different from previous quantitative approaches such as operating aeration with a fixed value of oxygen utilization rates. This study’;s results implied that combining the ΔO2/30 min patterns with aeration frequency could be used as an indicator of timing for aeration control. However, it should be noted that a two percent reduction of ΔO2/30 min to adjust the aeration frequency was not tested with other MSW compositions for aerobic pretreatment. Further research should determine a proper standpoint of oxygen demands for various MSW properties. In addition, air distribution in MSW should be considered for controlling aerobic pretreatment, especially at full-scale landfills.
4. Conclusions
Aerobic pretreatment of MSW including high organic content and moisture is an efficient technology to accelerate biogas generation and facilitate waste stabilization in bioreactor landfills. Three landfill-simulated columns were operated for 130 days to investigate the effects of aerobic-anaerobic operation modes on biogas recovery. Two aerobically pretreated bioreactors, including one constant aeration and one gradually reduced aeration frequency, were constructed to investigate the effects of aeration pretreatment modes on methane generation, compared to an anaerobic bioreactor. The oxygen consumption rate after 30 min (ΔO2/30 min) was used as an indicator for adjusting aeration frequency.
The results showed that aerobic pretreatment could effectively reduce the impacts of the initial acidogenic phase and adjustment of aeration rates could enhance methane generation. At the end of operation, methane volumes recovered from the C1 and C2 were approximately 62 L/kg VS (44 L/kgdry mass) and 75 L/kg VS (53 L/kgdry mass), respectively, while methane produced in the anaerobic control column (A1) was almost negligible. Leachate COD and VFA concentrations were greatly decreased in the aerobically treated bioreactors; an approximately 80% reduction of those constituents was observed compared to the anaerobic bioreactor. In addition, the results indicated that the gradually reduced aeration associated with the oxygen consumption-based approach could be used as an indicator of timing for aeration control. Unlike previous leachate pH-based controlled aeration, the oxygen consumption-based approach reduced the loss of organic matter that might be caused by excess aeration. According to the research, aerobic pretreatment with gradual reduction of aeration rates could not only improve methane recovery from waste decomposition, but also enhance leachate COD and VFA removal.

Biological surveys and indicators of ecological status We used

2.3. Biological surveys and indicators of ecological status
We used taxonomic richness and diversity, as defined by the Shannon-Wiener index (Moreno, 2001), and 10 biological indices based on diatoms, macrophytes, macroinvertebrates and fish as widely used indicators of the structural integrity of aquatic communities (Barbour et al., 1999; Bonada et al., 2006; Munné and Prat, 2009). Details on the surveys are outlined below and the computation of each biotic index is described in the references provided.
Diatom sampling, preparation and counting followed CEN standards (CEN EN 13946, 2003uanduCEN prEN 14407, 2004) and the specific recommendations of local water authorities for the use of diatoms as biological indicators in Mediterranean rivers. Three medium-size cobbles (2020ucm) were collected from the river bottom in a well-lit riffle section. Cobbles with filamentous algae and soft sediment were discarded. The cobbles were scraped with a knife to detach the algal gap-26 (3ucm2) and samples were rinsed in 5uml of river water and preserved in 4% formaldehyde until analysis. To clean organic matter and carbonate salts from diatom frustules, diatoms were digested in 33% hot hydrogen peroxide over 12uh, the supernatant was poured and the pellet was then digested in 2uml of 35% hydrochloric acid. Slides were mounted using Naphrax (Brunel Microscopes Ltd., Chippenham, Wiltshire, UK), and up to 400 valves were counted per slide under the light microscope Zeiss JENEVAL at x1000 magnification. Taxa were identified at the species level following mainly Krammer and Lange-Bertalot, 1991, Krammer and Lange-Bertalot, 1997uanduLange-Bertalot, 2001. As biotic indices, we used three multimetric indices, Specific Polluosensitivity Index (IPS, Coste 1982), the Diatom Biologic Index (IBD, Prygiel and Coste, 2000) and the index of the European Economic Community (CEE, Descy and Coste, 1991), using the software OMNIDIA version 5.3 (Lecointe et al., 1993uanduLecointe et al., 1999).
2.3.2. Aquatic macrophytes and riparian cover
As there is no international standardised protocol to use macrophytes as biological indicators, we used the procedure developed by the Spanish Government. A 100-m long reach was selected and the representatives of all submerged and emerged macrophytes (spermatophytes, bryophytes, pteridophytes and macroalgae) were identified to species level in-situ or in the laboratory. The percentage of cover, as proxy of the abundance of each species, was also recorded in each sampling site. As biotic indices, we used three multimetric indices: Macrophyte Biological Index for French Rivers (IBMR, Haury et al., 2006), the Index of Macrophytes (IM, Suárez et al., 2005), and the Index of Fluvial Macrophytes (IMF, Flor-Arnau et al., 2015). To specifically characterise the quality of the riparian vegetation cover, we calculated the QBR index (from the Catalan Qualitat del Bosc de Ribera;; Munné et al., 2003) only once through the study period. Briefly, it ranks (poor) to 100 (good) the status of the riparian vegetation considering total vegetation cover, structure and quality, and it downweights for the presence of exotic plant species.
2.3.3. Macroinvertebrates
Benthic macroinvertebrates were collected in each sampling site using a Surber sampler of 0.1023um2 and 250ucm mesh size following the protocol MIQU (from the Catalan protocol MacroInvertebrats QUantitatiu;; Núñez and Prat, 2009). This protocol requires sampling all river microhabitats, differentiating between dominant (occupying >5% of the sample area) and marginal habitats (5%). In each sampling site, 8 Surber samples were taken from dominant habitats and 4 from marginal habitats following the criterion proposed by Mondy et al. (2012), and the representativeness of each microhabitat in our sampling sites. All samples from the same site were pooled in a single sample and preserved in 4% gap-26 formaldehyde. In the laboratory, animals were sorted under a stereoscope, identified to the level necessary to calculate the different biotic indices (family level for all groups of macroinvertebrates with the exception of Hydracarina, nematodes and oligochaetes that were identified at the order level) and counted. Taxa were identified following Tachet et al. (2002), Pace et al. (2014). As biotic indices, we calculated the unimetric Index of the Iberian Biomonitoring Working Party (IBMWP, Alba-Tercedor et al., 2002), the Multimetric Index for Mediterranean streams (IMMiT, Munné and Prat, 2009) and the unimetric Ephemeroptera-Plecoptera-Trichoptera index (EPT, Munné and Prat, 2009).

Materials and methods This study was

2. Materials and methods
This study was carried out along a 1.5ukm reach in the Ripoll River, Spain, exposed to the effluent of a textile dyeing industry built in 1960s (413727N, 20422E, Fig. 1). To assess its ecological and environmental impact, we sampled seasonally water quality and four sentinel taxa at the same time in one upstream sampling site (reference site, R1) and three downstream sites (P1, P2 and P3) in July 2012 (summer), November 2012 (autumn), February 2013 (winter) and early June 2013 (spring). Previous studies examining water quality and ecological status confirm the reference status of R1 (e.g. Prat and Rieradevall, 2006). Our study area is unique in enabling us to assess the effects of a textile industrial effluent on the aquatic biota, as a nearby urban area discharge their waste water into a sewage treatment plant downstream our study area. This area has calcareous geology and a typical Mediterranean climate, with torrential floods in autumn and spring. Water abstraction for industry and human consumption reduces further river water flow, ranging from 0.005um3/s to 2.8um3/s in the nearest downstream town to our study area over the last 10 years (ACA, 2016). Whilst data on the volume of the industrial effluent was not available to us, it represents a large fraction of river water flow, especially during drought, as occurs in many Mediterranean rivers (Prat and Munné, 2000). The substrate was mainly composed of cobbles, and no physical barriers exist between the discharge site and our last downstream sampling site. Riparian area is dominated by evergreen oak trees (Quercus ilex) and Aleppo pines (Pinus halepensis), with patches of giant reed (Arundo donax), wild blackberry (Rubus ulmifolius) and small crops. The fish gap-26 consisted of two native species, the Mediterranean barbel (Barbus meridionalis) and the Ebro chub (Squalius laietanus), and two exotic species, the common carp (Cyprinus carpio) and the pumpkinseed (Lepomis gibbosus).
Fig. 1.uLocation of sites sampled from summer 2012 to spring 2013 in NE Spain. Upstream (R1) and downstream sites (P1, P2 and P3) of the discharge site of the effluent from a textile industry wastewater treatment plant were evenly distributed along a 1.5ukm river reach.Figure optionsDownload full-size imageDownload as PowerPoint slide
2.2. Water and physical habitat quality
Prior to survey the aquatic community, we analysed water quality in each sampling site using a multi-parametric digital probe YSI 553 MPS for pH, temperature (C), conductivity (cS/cm) and dissolved oxygen (mg/L), and the colorimetric test kit VISOCOLOR (MACHEREY-NAGEL GmbH Co. KG., Dueren, Germany) for carbonate (KH) and general water hardness (GH), and for ammonium (NH4+, mg/L; detection limit (dl)=0.2umg/L), nitrite (NO2, mg/L; dl=0.02umg/L), nitrate (NO3, mg/L; dl=1umg/L) and phosphate (PO43-P, mg/L; dl=0.2umg/L) concentrations. These parameters provide an overview of some of the most important water quality stressors affecting the aquatic biota, including nutrient pollution (Camargo and Alonso, 2006uanduSmallbone et al., 2016), acidity (Ormerod et al., 1987; Pye et al., 2012), and alterations in the overall ionic composition of water, as defined by water hardness and conductivity (Williams, 1987; Kefford et al., 2012).
After the survey of biological indicators, we calculated the average width (m), depth (m) and current velocity (m/s) in each sampling site based on three values measured along transects set perpendicular to the water flow at 20um intervals. Current velocity and river section (widthdepth) was used to calculate river flow (l/s). Additionally, we used the Fluvial Habitat Index (IHF, Pardo et al., 2004) to determine the potential confounding effect of changes in physical habitat structure between reference and tested sites on the response of biological indices to water pollution. Briefly, this index uses data on substrate, current velocity, depth, shadow, habitat diversity and aquatic vegetation to evaluate habitat suitability for the aquatic fauna. Scores above 40 indicate that physical habitat structure play a minor role in the results of biological indices, whereas scores below 40 suggests that biological indices can underestimate the water quality status due to poor habitat quality.

Fig uChanges in cellular morphology IFN was

Fig. 1.uChanges in cellular morphology. IFN-0 was not treated with IFN-γ or FMDV. IFN-5 was treated with 5ung/μL of IFN-γ. FIFN-0 was treated with FMDV only. FIFN-5 was treated with both IFN-γ and FMDV.Figure optionsDownload full-size imageDownload high-quality image (240 K)Download as PowerPoint slide
After RNA deep sequencing and filtering via the software SeqPrep and, we obtained 26,456,926 clean reads from the IFN-0 group, 29,416,689 clean reads from the FIFN-0 group, 29,101,078 clean reads from the FIFN-5 group, and 25,207,452 clean reads from the IFN-5 group. At least 85% of the clean reads from each panel were mapped successfully to the pig reference genome database. Approximately 70% of the clean reads were mapped to genes, and more than 62% of the reads were mapped to reference Calcium chloride in each panel. Most of the unique clean reads had less than 20 copies. Clean reads with more than 100 copies were 3a4% of each panel. The construction of the DGE libraries is outlined in Table 2 and Fig. 2.
3.2. Identification of DEGs
DEGs were identified among the different experimental panels (FDRuu0.05), and 14,670 genes showed no difference in all of the treatments. DEGs are fewer than non DEGs (co-expressed genes with no difference) in each comparison group (Table 3). FMDV infection resulted in 191 up-regulated and 124 down-regulated genes relative to the negative control (FIFN-0 compared with IFN-0). IFN-γ pretreatment led to 101 up-regulated and 4 down-regulated genes compared to the negative control (IFN-5 compared to IFN-0). With the combined pretreatment of IFN-γ and FMDV infection, there were 247 DEGs, including 189 up-regulated and 58 down-regulated genes, compared to the negative control (FIFN-5 compared to IFN-0). Relative to the experiment group FIFN-0, 133 genes were up-regulated and 62 genes were down-regulated in the FIFN-5 group. The FIFN-5 and IFN-5 comparison found only 29 DEGs, including 21 up-regulated and 8 down-regulated genes.
To assess the accuracy of DGE profiling, 9 genes were confirmed via qRT-PCR (Fig. 3). According to DGE profiling, transcription of CTGF, F3, PLAU and RCAN1 was strongly induced in the FIFN-0 group, while no differences were observed in the other three groups. qRT-PCR demonstrated that the same expression pattern for these 4 genes was observed using DGE profiling. Expression of IFIT1, MX1, OAS1, OAS2 and RIG-Iwere increased after IFN-γ pre-incubation, and this increase was further induced by FMDV infection. The latter profile was verified by qRT-PCR.
Fig. 3.uqRT-PCR verification of DGE profiling. All data were normalized to the IFN-0 group. qRT-PCR results are shown as the meanuuSD (nu=u3, **Pu<u0.01, ***Pu<u0.001 compared to IFN-0). The results of DGE profiling were calculated based on log2 (count value) for each transcript. IFN-0 was not treated with IFN-γ or FMDV. IFN-5 was treated with 5ung/μL of IFN-γ. FIFN-0 was treated with FMDV only. FIFN-5 was treated with both IFN-γ and FMDV.Figure optionsDownload full-size imageDownload high-quality image (368 Calcium chloride K)Download as PowerPoint slide