Removals of Some High- and Low-Density Polyethylene (HDPE and
LDPE), Polypropylene (PP) and Polyvinyl Chloride (PVC)
Microplastics Using Some Microalgae Types, Energy Production and
Energy Recovery
DELİA TERESA SPONZA*, RUKİYE ÖZTEKİN
Department of Environmental Engineering
Dokuz Eylül University
Tınaztepe Campus, 35160 Buca/Izmir,
TURKEY
* Corresponding Author
Abstract: - Waste plastic conversion involves the treatment of plastic waste to transform in different forms of
energy (heat, electricity, liquid fuels). Plastic can be converted into different forms of biofuel via thermochemical
conversion methods (gasification, pyrolysis and liquefaction). Algal biomass can be converted into different
forms of biofuel (crude bio-oil, bioethanol, biogas, biodiesel and bio-hydrogen) well as value added chemicals.
Microalgal cells can accumulate more lipids over a shorter life cycle, they are discussed as a promising feedstock
for third-generation biodiesel. The utilization of microalgae as biofuel feedstocks offers an economic, eco-
friendly alternative to the use of fossil fuels the aim of microplastics (MPs) removals. Interactions between MPs
and microalgal cells could enhance several important features for possible microalgal harvest and MPs
accumulation. One hypothesis is microalgal biomass hypothesis can accumulate lipids and carbohydrates under
microplastic stress, supporting biomass conversion into biodiesel and bioethanol. In such systems, algal cells act
as bio-scavengers for MPs, binding the particles to algal surfaces or incorporating them into their cells; they are
filtered from the water body and finally destroyed by further downstream processing of the polluted biomass. In
this study, in order to determine biofuel (1-butanol) and methane gas [CH4(g)] production; High- and low-density
polyethylene (HDPE and LDPE), polypropylene (PP), and polyvinyl chloride (PVC) MPs were removed using
biomass composed of microalgae Chlamydomonas reinhardtii and Chlorella vulgaris. The algal inhibition test
results proved that small groups of MPs with a size of ≈ 100 nm did not show algal inhibition. According to the
algae inhibition test results, the production of 1-butanol from 100 mg/l microalgae biomass under aerobic
conditions were determined as 93 ml/g for HDPE, 236 ml/g for LDPE, 387 ml/g for PP and 459 ml/g for PVC.
According to the algae inhibition test results, the production of CH4(g) from 400 mg/l microalgae biomass under
anaerobic conditions were measured as 452 ml/g for HDPE, 510 ml/g for LDPE, 529 ml/g for PP and 541 ml/g
for PVC. 91.26%, 94.52%, 98.34% and 96.17% energy recoveries were measured for HDPE, LDPE, PP and PVC
MPs, respectively, after microalgae biomass experiments, at pH=7.0 and at 35oC. Maximum 98.34% energy
recovery was obtained for PP MPs after microalgae biomass experiments, at pH=7.0 and at 35oC.
Key-Words: - Biofuel (1-butanol); Chlamydomonas reinhardtii; Chlorella vulgaris; Energy recovery; High- and
low-density polyethylene (HDPE and LDPE) removal; Methane [CH4(g)] production; Microalgae; Microplastics;
Polypropylene (PP) removal; Polyvinyl chloride (PVC) removal.
Received: June 25, 2022. Revised: October 22, 2023. Accepted: November 24, 2023. Published: December 31, 2023.
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1 Introduction
Plastics are widely used in numerous industries,
including agriculture, medicine and packaging. Many
plastics are thrown into the environment due to the
high production volume and difficulty in breaking
them down. Once plastic particles are released into
the environment, they are broken down by various
natural forces and exposed to weathering. Such
natural forces include ultraviolet (UV) radiation,
mechanical forces of water, as well as biological
degradation resulting in the formation of
microplastics (MPs) and nano-plastics (NPs), [1].
“MP” is a term used to refer to any synthetic solid
plastic polymer with a diameter of ≤ 0.5 mm, formed
as a result of primary or secondary processes, [2], [3],
[4]. Although, there is no established definition for
“NPs”, the term is often used to refer to particles of
similar origin and composition to MPs, with smaller
sizes ≤ 100 nm in size, much smaller than the algal
cell diameter, [5], [6], [7]. MPs and NPs (MNPs) are
distributed directly into the environment through
domestic and industrial wastes from cosmetics,
cleaning products and synthetic fibers; By following
the food chain between living things in the
ecosystem; They can eventually enter the human
body and threaten human health.
Biodegradable plastics (BPs) are attracting
attention as a replacement for non-degradable plastic
materials. It is noted that BPs can be converted to
CO2 and H2O as final products through naturally
occurring microorganism mineralization and may
provide new avenues for end-of-life treatment of
plastic waste, such as anaerobic digestion and
composting [8]. 100% degradation of biodegradable
materials cannot be achieved in natural
environments, [9]. BPs in natural environments have
also been proven to lead to the formation of
biodegradable microplastics (BMPs), as do
conventional petroleum-based MPs, [10]. Since BPs
are more vulnerable to degradation forces; More
BMPs can be produced from MPs obtained from non-
degradable raw materials. This situation causes much
more serious BMP pollution in the soil ecosystem,
[11].
According to the United States National Oceanic
and Atmospheric Administration (NOAA),
microplastics (MPs) are defined as pieces of plastic
with particle size < 5 mm. Improper discharge of
industrial and subsistence wastewater (ww); It
contaminates rainwater, surface water and oceans
with large amounts of MPs. Since MPs have a similar
density range (0.85 to 1.41 g/cm3) compared to fresh
and ocean water bodies; They are easily distributed
worldwide, [12]. Environmental pollutants known to
easily adsorb onto MPs include many toxic
compounds such as heavy metals (e.g., Cu, Ni, Pb,
and Zn) and persistent organic pollutants (POPs)
[e.g., polycyclic aromatic hydrocarbons (PAHs),
polychlorinated biphenyls (PCBs), polybrominated
diphenyl ethers (PBDEs),
dichlorodiphenyltrichloroethane (DDT) and 2,2-
bis(p-chlorophenyl)-1,1,1-trichloroethane (DDTs)].
MPs may contain chemicals such as bisphenol A
(BPA) and phthalates, which are added during the
plastic manufacturing process. Through
bioaccumulation, these organic pollutants pose
significant threats to human health as well as the
marine ecological environment. HDPE, LDPE, PP
and PVC are the dominant forms of plastic,
representing approximately 59% of the total amount
of plastic produced worldwide, [13].
The effects of plastic pollution in the aquatic
environment are constantly being investigated. So
much so that the presence of plastic components is
detected even at depths of 7,000-11,000 m in the
oceans and plastic waste discharge is increasing
every year, [14]. These plastic aggregates, known as
MPs, ranging from 0.1 to 5 mm in diameter, are the
main pollutant components of long-term
environmental pollution, [15]. These MPs can
accumulate in aquatic animals through the food
chain, affecting their growth and development,
reducing their nutritional status, and damaging their
ecosystems. As a result of these chain reactions of
MPs in the aquatic ecosystem; They pose serious
health threats to humans, [16], [17]. In addition,
roughness, porosity, polarity and hydrophobicity as a
result of the mixing of more than one contaminant; It
further increases contamination of MPs, [18], [19].
This causes MPs to adsorb more pollutants in the
environment, such as heavy metals, antibiotics,
persistent organic pollutants, and other pollutants
[16], [20], [21], [22]. Microalgae, the primary
producers of aquatic ecosystems, are affected by the
toxicity of MP pollution. In addition to its negative
effects on microalgae growth, studies conducted
include; It proves that MPs affect algal
photosynthesis, that chlorophyll content and
photosynthesis efficiency decrease with exposure to
MPs, and that smaller sizes may be more toxic.
However, studies on the effects of mixing MPs with
different pollutants are quite limited, [19].
There are many literature studies reporting the
interaction of microalgae and MPs; However, these
studies mostly examined the effects of microalgae
colonization and toxicity, [19]. In recent years,
efforts have been made to remove MPs by forming
hetero aggregations with microalgae; Preliminary
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studies have been carried out in many literature
studies, [23], [24], [25], [26], [27], [28], [29].
Applied microalgae mostly include seaweed and
freshwater algae; and the tested MP materials mostly
consist of PVC, PP, polystyrene (PS) and HDPE.
Efficiency of forming hetero aggregates; It is greatly
affected by both the algae type, that is, the
morphology of the algal cell and the amount of
extracellular polymeric substance (EPS) of the algae,
and the properties of the plastic, such as the type of
MPs material, the size of the MPs, the density of the
MPs, and the hydrophobicity property of the MPs.
The surface roughness of MP particles has been
reported to be positively related to the number of
attached microalgae, [30], and high-energy surfaces
generally facilitate the growth of biofilms because
they are more hydrophilic surfaces, [31]. For this
reason, MPs with high surface roughness and
hydrophilicity are likely to form hetero aggregations
with microalgae more easily. Physicochemical
characteristics of MPs for example, surface
chemistry, particle size, particle distribution and
types affect the toxicity of MPs greatly in aquatic
organisms, [32].
HDPE, LDPE, PP and PVC are the most dominant
types of plastics, representing approximately 60% of
the total amount of plastic produced worldwide, [13].
The remaining 40% is in plastic forms; PS (6.7%),
polyethylene terephthalate (PET, 7.4%),
polyurethane (PUR, 7.5%), polybutylene
terephthalate (PBT), acrylonitrile butadiene styrene
(ABS), polymethylmethacrylate (PMMA) and
polycarbonate (PC) it consists of other polymers,
[13].
Recently, in the search for sustainable and
environmentally friendly biofuel sources that can be
a truly efficient alternative to fossil fuels; There are
many literature studies investigating production from
plants, bacteria, yeasts and microalgae. Microalgae
stand out as a valuable solution due to their
advantages such as higher lipid productivity, faster
growth rates, accumulation of biomass in smaller
areas, and inability to compete with human food
resources. These photosynthetic microorganisms are
capable of producing numerous metabolic
compounds that can be converted into different forms
of biofuel such as biodiesel, biohydrogen,
biomethane or bioethanol. Biofuel production from
microalgae through various transformation processes
were summarized at Fig. 1. Biofuels include
biohydrogen, biogas, bioethanol, biodiesel, syngas,
bio-oil and bio-char (Fig. 1). The main potential
process is to produce triacylglycerides (TAGs), the
main component of biodiesel feedstocks, through
transesterification into fatty acid methyl esters
(FAMEs), derived from the lipid synthesis metabolic
pathway in microalgae, [33], [34].
* Fig. 1 can be found in the Appendix section.
Biofuels are divided into four main categories
according to the raw material: first generation,
second generation, third generation and fourth
generation. First and second generations biofuels are
made from corn, sugarcane bagasse, wheat starch,
soybeans, rapeseed, canola, jatropha, etc. They are
traditional biofuels obtained from edible and non-
edible terrestrial plants, including, [35]. The biggest
disadvantages of first and second generations
conventional fuels are; What drives direct
competition with agricultural food production is the
need for large areas, excess water and excess
nutrients for the cultivation of the product, [36]. By
using third generation biofuels, which are obtained
from the biomass of various microorganisms such as
bacteria, yeast, fungi and microalgae, can be grown
on smaller lands and have high areal productivity;
The disadvantages of first and second generations
biofuels can be overcome, [36]. Fourth generation
biofuels involve the use of genetically modified
microorganisms to increase their biofuel potential,
[37]. Fourth generation biofuels include
photosynthetic microalgae; They provide superiority
over other microorganisms thanks to their ability to
utilize CO2(g) and solar energy to produce biomass,
thus eliminating the need for high-cost organic
carbon, [38]. The use of microalgae lipids for
biodiesel production is a great advantage as they have
the ability to naturally survive in the sea, brackish
waters or wastewater. Thanks to this advantage, less
land and less fresh water usage, faster growth rates,
less CO2(g) emissions from flue gases, reduction of
the amounts of nutrients such as nitrogen and
phosphorus in wastewater, and continuous
production throughout the year are achieved, [39].
Third generation biofuels produced from
microalgae against energy crisis and environmental
pollution; They offer promising alternatives for
sustainable global economic growth and human
progress. Microalgae biomass can be processed into
biodiesel, bioethanol and biogas; However, high
input costs and technical limitations of biodiesel
production restrict the further development of
biodiesel. Bio-methanation of microalgae biomass
via anaerobic digestion; It increases the energy
efficiency of biodiesel and is an environmentally
friendly and high-efficiency alternative. Most of
these include optimization of light delivery to the
culture, use of residual glycerol as a heterotrophic
carbon source, maximization of triglyceride
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accumulation through nutritional supplementation
and metabolic engineering, use of direct trans-
esterification, especially to prevent desiccation of
biomass, cultivation of algae in ww or application of
anaerobic digestion, as well as lipid digestion. It is
related to additional energy recovery processes from
extracted microalgal biomass, [40].
Chlamydomonas reinhardtii is a unicellular green
alga ≈ 10 μm in diameter, swimming with two
flagella; It has a cell wall composed of glycoproteins
rich in hydroxyproline, a large cup-shaped
chloroplast, a large pyrenoid, and a light-sensitive
eye spot. The typical freshwater alga
Chlamydomonas reinhardtii, which is widely used as
a model aquatic organism in ecotoxicological studies
and nutrient removal, is applied as a test species, [41],
[42]. Lagarde et al. [25], evaluated the interactions of
PP and HDPE with the chlorophyte Chlamydomonas
reinhardtii, a microalgae species, and found that 400
mg/l microalgae biomass; A significant ≈ 18%
reduction in microalgae growth was detected after 78
days of contact with PP. This result was attributed to
the formation of hetero-aggregates of microalgae
with MP during the 20-day mixing period. The
shading effect of the microalgae trap on MPs clusters
causes a decrease in photosynthetic efficiency; and
this reduces the growth rate of microalgae, [25]. It has
been reported that high concentration of MPs with
size > 400 μm has no detrimental effect on the
freshwater microalgae Chlamydomas reinhardtii,
[25]. As a typical phytoplankton, Chlamydomonas
reinhardtii has the potential to be easily cultivated, is
considered highly sensitive to environmental
pollution, is used as a potential candidate for water
pollution assessments, and has many advantages such
as high biosorption and removal efficiency in
personal care products (PPCPs), [43].
Chlorella vulgaris is a species of green
microalgae in the division Chlorophyta; It is used as
a dietary supplement or protein-rich food additive,
especially in Japan. Biodiesel produced from
Chlorella vulgaris provided the most significant
reduction in hydrocarbon, CO and CO2 gas emissions
compared to biodiesel produced from Eruca sativa
plant and waste cooking oil, [44]. It was observed that
aging MPs inhibited the growth of microalgae
Chlorella vulgaris to a greater extent than young
MPs, with enhanced porosity and adsorption
capacity, [45]. PS MPs affected the removal of
levofloxacin by altering the adsorption, enrichment,
and enzymatic degradation of antibiotics by
Chlorella vulgaris; On the third day, the levofloxacin
(initial concentration=93.8 µg/l) removal rates for the
MPs group (35 items/l) and the control group were
23.34% and 46.71%, respectively; however, the
combined toxicity on Chlorella vulgaris microalgae
began to decrease, [46].
Anaerobic digestion of residual lipid-extracted
biomass; It is one of the most promising options for
improving the economic and environmental
sustainability of the process. It allows energy
recovery in the form of biogas, allows nutrients to be
recycled and reused in microalgae culture, stabilizes
waste biomass; thus, reducing the costs associated
with waste disposal and management. The high
temperatures (49oC-57oC) used during thermophilic
anaerobic digestion accelerate biochemical reactions;
By intensifying the hydrolysis of the microalgal cell
wall, it increases organic matter degradation
efficiency and biogas production. Working under
thermophilic conditions provides a higher degree of
effluent stabilization and hygiene compared to
mesophilic conditions, improved sludge dewatering,
potentially higher biomethane yield, greater
reduction of volatile organics, lower risk of foaming,
2-3 times higher bacterial growth rates and higher It
also offers benefits such as allowing for potential
organic loading rates (OLRs), [47]. Few and
contradictory studies have addressed the
thermophilic anaerobic digestion of the residual
microalgal biomass up to this date. Both higher, [48],
and lower, [49], biogas yields have been reported
when compared to mesophilic digestion. It has also
been stated that the optimum temperature for
anaerobic digestion might be dependent on the
microalgae species, [50]. Few studies have evaluated
the potential energy contribution of anaerobic
digestion to the biodiesel production process from
microalgae. The few reports available in the literature
indicate that a considerable part of the total energy
contained within the biomass can be recovered if
anaerobic digestion of lipid-extracted microalgae is
implemented, [51].
Microalgae-based biorefinery approach is a
system where energy, fuel, chemicals and high-value
products (e.g., pigments, proteins, lipids,
carbohydrates, vitamins and antioxidants) are
produced from biomass through various processes.
Microalgae are rich in proteins, lipids and
carbohydrates, and the relative amounts of these
biochemical components vary among various
microalgae species, [52]. They can be used as raw
materials in the production of various high-value bio-
based products such as production of biodiesel from
microalgae lipids, alternative carbon source in
fermentation industries of microalgae carbohydrates,
healthy food supplements from long-chain fatty acids
found in microalgae, and in pharmaceutical
applications, [53]. The main focus of microalgae
biotechnology for the large-scale application of
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microalgae as a sustainable and robust energy
feedstock is: (a) increasing their photosynthetic
efficiency through metabolic engineering for
improved oil yield and improved carbon
sequestration in mass cultures, (b) useful as a source
of biofuel, energy-rich It is based on increasing
carbon flux and energy production into compounds,
(c) developing robust and stable algal cells that are
low-cost, sustainable in large-scale cultivation,
resulting in lower operating costs and a lower carbon
footprint of the chemical produced, [54].
In this study, in order to determine biofuel (1-
butanol) and CH4(g) production in Chlamydomonas
reinhardtii and Chlorella vulgaris microalgae
species; The use of HDPE, LDPE, PP and PVC MPs
has been investigated under anaerobic conditions.
Additionally, the energy production processes and
energy recovery processes were also investigated
after the removal of MPs by microalgae
Chlamydomonas reinhardtii and Chlorella vulgaris)
biomass.
1.1 Originality and Innovation of Our Work
By using biomass consisting of a mixture of
Chlamydomonas reinhardtii and Chlorella vulgaris
microalgae; The recovery of energy by producing 1-
butanol as an energy source from biodegradable
HDPE, LDPE, PP and PVC microplastics under
aerobic conditions, followed by the production of
CH4(g) as an energy source under anaerobic
conditions and the recovery of energy, shows the
originality and innovation of the study. Because
using these four microplastics and biomass, which is
a mixture of two microalgae, has never been used
before to produce and recover energy under aerobic
conditions and to produce and recover energy under
anaerobic conditions.
An important feature of our study is its accuracy
and applicability; It can be tested and compared with
a possible new approach [such as artificial
intelligence (AI) methods].
2 Materials and Methods
2.1 Microalgae Biomass
Chlamydomonas reinhardtii CC124 strain powder
was purchased from Sigma-Aldrich, Germany.
Chlorella vulgaris CCAP 211/11B (Culture
Collection of Algae and Protozoa, Argyll, UK) was
purchased from United Kingdom.
Chlamydomonas reinhardtii powder was cultured
in a tris-acetate-phosphate (TAP) medium, [55], [56],
[57]. Algal cells were cultivated in a constant
temperature light incubator at 22 ± 2oC, at pH=7.0
and at 20 µmol photon/m2.s illumination. Algae were
grown in 250 ml Erlenmeyer flasks and were shaken
daily and randomly arranged to reduce any minor
differences in photon irradiance. Chlamydomonas
reinhardtii powder was utilized as substate with 93.1
± 0.2% total suspended solids (TSS), 84.2 ± 3.6%
total volatile suspended solids (TVSS). The
elemental composition of Chlamydomonas
reinhardtii powder were 53.2±0.5% C, 10.4±0.3% N,
6.1±0.1% H, 0.6±0.01% S, C/N=5.1, 65.06±0.2%
protein, 17.6±0.8% carbohydrate and 18.9±0.3%
lipid of TS, respectively.
Chlorella vulgaris powder was cultured in a bold
basal medium (BBM), [58]. All experiments were
performed at a temperature-controlled environment
at 25 ± 3°C and at optimum pH=7.0. The light was
provided by a cool white LED (T5 15W 6400K,
80μmol/m2.s) with continuous illumination within
the experimental period. Chlorella vulgaris powder
was used as substrate with 93.1 ± 0.2% TSS, 84.2 ±
3.6% TVSS. As for elemental composition of
Chlorella vulgaris were 47.5 ± 0.3% C, 10.7 ± 0.2%
N, 6.9 ± 0.1% H, 0.7 ± 0.02% S, C/N=4.6, 66.9 ±
0.4% protein, 16.2 ± 0.7% carbohydrate and 17.4 ±
0.3% lipid of TS, respectively.
2.2 Lipid Extraction and Characterization of
Microalgae Biomass
The lipid extraction was carried out by Soxhlet
extraction. 25 g dried microalgae biomass was placed
in an extraction thimble (SWISS filter cellulose
extraction thimbles, 33 x 80 mm P2, SW3380), which
was then placed inside the Soxhlet extraction
apparatus. A mixture of 100 ml of hexane and
acetone (3/1 v/v) was used as solvent, with a reflux
period of 8 h. The lipid extraction yield was
determined gravimetrically according to
Balasubramanian et al., [59]. The obtained lipid-
extracted microalgae biomass was dried to remove
any residual solvent at 105oC.
2.3 Inhibition Test for Microalgae Biomass
Inhibition tests for Chlamydomonas reinhardtii and
Chlorella vulgaris were measured according to
Standard Method 8810 and 8813 C, respectively [60].
2.4 Experimental Procedure
To evaluate anaerobic digestion performance: By
continuously circulating hot water through the
jackets of a 1-liter laboratory-scale continuous
anaerobic bioreactor (ABR), keeping the temperature
constant under mesophilic (35oC) conditions; fed
with lipid-extracted 400 mg/l of microalgae
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(Chlamydomonas reinhardtii and Chlorella vulgaris)
biomass.
CO2(g) produced was captured by a 100 ml flask
containing NaOH. Thymolphthalein was used as
indicator to signal the exhaustion of the basic
solution. The values of CH4(g) yields were expressed
as volume of gas produced divided by g of VS of
substrate fed. ABR was stirred automatically using a
mechanical motor connected to a timer. Mixing was
performed for 20 min every 3 h. ABR was operated
at OLR=0.6 g COD/l.d and at HRT=30 d. The overall
operational period was studied for 150 d.
2.5 Analytical Procedures
Chemical oxygen demand-dissolved (CODdissolved),
total ammonium-nitrogen (total NH4+-N), pH,
temperature {T[(oC)]}, TSS, TVSS, chloride ion (Cl-
), volatile fatty acids (VFAs), sodium ion (Na+),
potassium ion (K+), calcium ions (Ca+2), magnesium
ions (Mg+2), copper ions (Cu+2), nickel ions (Ni+2)
and zinc ions (Zn+2) were measured according to the
Standard Methods (2022); 5220D, 4500-NH4+, 4500-
H+, 2320, 2540D, 2540E, 4500-Cl-, 5560B, 3500-
Na+, 3500-K+, 3500-Ca+2, 3500-Mg+2, 3500-Cu+2,
3500-Cr+2 and 3500-Zn+2, respectively, [60].
CH4(g) was measured daily through water volume
displacement with gas chromatography-mass
spectrometry (GC-MS); gas chromatograph (GC)
(Agilent Technology model 6890N) equipped with a
mass selective detector (Agilent Technology model
5973 inert MSD, mass selective detector). Mass
spectra were recorded using a VGTS 250
spectrometer equipped with a capillary SE 52 column
(HP5-MS 30 m, 0.25 mm ID, 0.25 μm) at 220°C with
an isothermal program for 10 min. The initial oven
temperature was kept at 50oC for 1 min, then raised
to 220oC at 25oC/min and from 200oC to 300oC at
8oC/min, and was then maintained for 5.5 min. High
purity He(g) was used as the carrier gas at constant
flow mode (1.5 ml/min, 45 cm/s linear velocity).
During the whole operational period, ABR was
monitored by measuring the volume of biogas
produced, biogas composition, temperature, pH,
TSS, TVSS, CODdissolved, carbohydrates, proteins,
total NH4-N and VFAs concentrations, respectively.
Concentrations of potentially toxic compounds for
anaerobic digestion (e.g., Na+, K+, Ca2+, Mg2+, total
Cr, Cu2+, Ni2+, Zn2+, and Cl-), carbohydrate, VFAs
and the occurrence of residual solvent toxicity,
acetone and hexane concentrations in samples from
ABR was measured and evaluated by GC-flame
ionization detection (GC-FID) (Agilent Technology,
Germany) (column 30 m/0.25 mm ID, temperature
ramp from 60oCx2 min, 10oC/min to 190oCx2.5 min,
detector temperature of 250oC and injector
temperature of 250oC).
2.6 Biofuel (1-Butanol) Production from MPs
with Microalgae Biomass
100 mg/l of microalgae (Chlamydomonas reinhardtii
and Chlorella vulgaris) biomass, in water
contaminated with MPs, surrounds the surface of
MPs particles and absorbs them; They grow by
creating more lipids in their metabolism. More Lipid
provides more energy production. Microalgae
biomass produce biofuel (1-butanol) as a result of a
series of reactions using these lipids under aerobic
conditions (Fig. 2).
* Fig. 2 can be found in the Appendix section.
2.7 Biomethane Potentials (BMPs) Tests
BMPs tests for microalgae (Chlamydomonas
reinhardtii and Chlorella vulgaris) biomass were
performed under mesophilic (35oC) and thermophilic
(55oC) conditions, respectively. BMPs assays were
carried out in 120 ml serum bottles containing 50 ml
of experimental solutions. An initial substrate
concentration of 5 g/l VS was operated. The substrate
to inoculum ratio was set at 1/1 (VS/VS), [61]. The
BMP medium was supplemented with 200 mg/l yeast
extract, 5 g/l sodium bicarbonate (NaHCO3), 65 mg/l
ammonium chloride (NH4Cl), 18.5 mg/l potassium
dihydrogen phosphate (KH2PO4), 5.7 mg/l
magnesium sulphate heptahydrate (MgSO4.7H2O,
Epsomite or Epsom salt) and 4 mg/l calcium chloride
dihydrate (CaCl2.2H2O), respectively.
The CH4(g) production was determined by
monitoring the pressure and composition of the gas
contained in the headspace of the bottles. The BMPs
value was computed dividing the cumulative CH4(g)
produced by the mass of VS of substrate added at the
beginning of the test. The endogenous biogas
production from the anaerobic biomass was
determined by control assays containing only
inoculum. The values of the CH4(g) yields were
normalized at 0oC and atmospheric pressure (1 atm
= 101.325 kPa).
2.8 Energy Production and Energy Recovery
Microalgae are a unique biomass feedstock for
renewable and sustainable energy production. Energy
production; It is released as a result of the breakdown
of MPs by using the lipids and biofuels (e.g., 1-
butanol) in microalgae biomass structure under
anaerobic conditions (e.g., ABR). Energy recovery,
ER (%) was calculated from Eq. 1.
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𝐸𝑅 (%)= 𝐻𝑇𝐿𝑏𝑖𝑜−𝑜𝑖𝑙 . 𝑚𝑏𝑖𝑜−𝑜𝑖𝑙
𝐻𝑇𝐿𝑓𝑒𝑒𝑑 . 𝑚𝑓𝑒𝑒𝑑
(1)
where; HTLbio-oil: is HTL of the products mbio-oil: is the
products amount, HTLfeed: is HTL of the feedstock
and mfeed: is the feedstock amount, respectively.
All experiments were carried out three times and
the results are given as the means of triplicate
samplings. The data relevant to the individual
pollutant parameters are given as the mean with
standard deviation (SD) values.
2.9 Flux Uncertainties and Limits of Detection
(LOD)
The measured flux includes the true flux (F) plus
random (∊) and systematic (δ) error components for
measurement system (x) at time (t) in Eq. (2):
𝐹𝑡,𝑥 = 𝐹𝑡+ ∊𝑡,𝑥+ 𝛿𝑡,𝑥 (2)
Systematic error can result from (I) incorrect
calibration of instrumentation, (II) incomplete
sampling of turbulent fluctuations, (III) failure to
observe non-turbulent flows during weak mixing
conditions, and (IV) potential underestimation of the
flow energy used during mixing in the anaerobic
digestion process.
The calculations were used to identify the main
biodegradable plastics and calculate their
biodegradation behavior in various anaerobic
digestion processes according to ISO 15985
(simulating high solid and thermophilic anaerobic
digestion) and ISO 14853 (simulating semiliquid and
mesophilic anaerobic digestion), [62]. While spectral
corrections induce uncertainties of their own, we
nevertheless assume here that after spectral
corrections, remaining ∊𝑡,𝑥 >> 𝛿𝑡,𝑥.
Before performing experimental error analysis;
High-frequency CH4(g) concentrations were
remeasured separately from GC-MS measurements
with a low-power open path analyzer (LI-7700, LI-
COR Biosciences Inc.) and a closed path tracer gas
analyzer (TGA100A, Campbell Scientific). Laser
spectroscopy was used in both analyses.
3 Results and Discussions
3.1. Effect of Lipid Extraction Process for
Microalgae Biomass Characterization
The results of the proximate analysis for microalgae
(Chlamydomonas reinhardtii and Chlorella vulgaris)
biomass was demonstrated at Table 1.
* Table 1 can be found in the Appendix section.
Low crude fibre proportions (< 3%) may be
indicative of low cellulose content in cell walls; and
this may facilitate cell lysis. The high ash content (>
17%) indicates that a significant fraction of the total
mass of microalgae will not be degraded during
digestion and therefore cannot be reduced to CH4(g)
(Table 1). Regarding the presence of possible
inhibitors, concentrations of the element Na in the
biomass are negligible, while high protein
proportions (≈ 50%) in microalgae biomass can lead
to inhibition of the digestion process due to the
accumulation of free ammonia nitrogen (FAN). This
issue needs to be considered when microalgae are
used as substrates. The lipid extraction method did
not cause lysis of microalgal cells, but only affected
the microalgal cell surface.
3.2 Removals of HDPE MPs with Microalgae
Biomass
Increasing HRTs values (30 days, 60 days, 90 days,
120 days and 150 days) were examined with 100 mg/l
of microalgae (Chlamydomonas reinhardtii and
Chlorella vulgaris) biomass using HDPE MPs during
aerobic conditions for 1-butanol production, at
pH=7.0 and at 35oC (Fig. 3). 19 ml/gVS, 40 ml/gVS,
81 ml/gVS and 76 ml/gVS 1-butanol productions
from HDPE MPs were observed for 30 days, 60 days,
120 days and 150 days HRTs, respectively, during
aerobic conditions, at pH=7.0 and at 35oC (Fig. 3).
The maximum 93 ml/gVS 1-butanol production from
HDPE MPs was measured for 90 days HRTs, during
aerobic conditions, at pH=7.0 and at 35oC (Fig. 3).
* Fig. 3 can be found in the Appendix section.
Increasing HRTs values (30 days, 60 days, 90
days, 120 days and 150 days) were operated with 400
mg/l of microalgae (Chlamydomonas reinhardtii and
Chlorella vulgaris) biomass using HDPE MPs in
ABR during anaerobic conditions for biochemical
CH4(g) production, at pH=7.0 and at 35oC (Fig. 4).
343 ml CH4/gVS, 411 ml CH4/gVS, 286 ml CH4/gVS
and 177 ml CH4/gVS biochemical CH4(g)
productions from HDPE MPs were obtained for 30
days, 90 days, 120 days and 150 days HRTs,
respectively, in ABR during anaerobic conditions, at
pH=7.0 and at 35oC (Fig. 4). The maximum 452 ml
CH4/g VS biochemical CH4(g) production from
HDPE MPs was measured for 60 HRTs in ABR
during anaerobic conditions, at pH=7.0 and at 35oC
(Fig. 4).
* Fig. 4 can be found in the Appendix section.
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For the removal of HDPE MPs in wastewater, 400
mg/l Chlamydomonas reinhardtii microalgae was
applied at 1872 h experimental time, [63]. No
significant reduction in growth, no significant change
in chloro-plastic genes, and no effect on stress
response/apoptosis genes for Chlamydomonas
reinhardtii microalgae were detected by Qin et al.,
[63].
3.3. Removals of LDPE MPs with Microalgae
Biomass
Increasing HRTs values (30 days, 60 days, 90 days,
120 days and 150 days) were studied with 100 mg/l
of microalgae (Chlamydomonas reinhardtii and
Chlorella vulgaris) biomass using LDPE MPs during
aerobic conditions for 1-butanol production, at
pH=7.0 and at 35oC (Fig. 3). 33 ml/gVS, 95 ml/gVS,
204 ml/gVS and 172 ml/gVS 1-butanol productions
from LDPE MPs were measured for 30 days, 60 days,
120 days and 150 days HRTs, respectively, during
aerobic conditions, at pH=7.0 and at 35oC (Fig. 3).
The maximum 236 ml/gVS 1-butanol production
from LDPE MPs was observed for 90 days HRTs,
during aerobic conditions, at pH=7.0 and at 35oC
(Fig. 3).
Increasing HRTs values (30 days, 60 days, 90
days, 120 days and 150 days) were examined with
400 mg/l of microalgae (Chlamydomonas reinhardtii
and Chlorella vulgaris) biomass using LDPE MPs in
ABR during anaerobic conditions for biochemical
CH4(g) production, at pH=7.0 and at 35oC (Fig. 4).
371 ml CH4/gVS, 424 ml CH4/gVS, 329 ml CH4/g
VS and 234 ml CH4/gVS biochemical CH4(g)
productions from LDPE MPs were obtained for 30
days, 90 days, 120 days and 150 days HRTs,
respectively, in ABR during anaerobic conditions, at
pH=7.0 and at 35oC (Fig. 4). The maximum 510 ml
CH4/g VS biochemical CH4(g) production from
LDPE MPs was found for 60 HRTs in ABR during
anaerobic conditions, at pH=7.0 and at 35oC (Fig. 4).
3.4 Removals of PP MPs with Microalgae
Biomass
Increasing HRTs values (30 days, 60 days, 90 days,
120 days and 150 days) were examined with 100 mg/l
of microalgae (Chlamydomonas reinhardtii and
Chlorella vulgaris) biomass using PP MPs during
aerobic conditions for 1-butanol production, at
pH=7.0 and at 35oC (Fig. 3). 118 ml/gVS, 182
ml/gVS, 322 ml/gVS and 248 ml/gVS 1-butanol
productions from PP MPs were observed for 30 days,
60 days, 120 days and 150 days HRTs, respectively,
during aerobic conditions, at pH=7.0 and at 35oC
(Fig. 3). The maximum 387 ml/gVS 1-butanol
production from PP MPs was measured for 90 days
HRTs, during aerobic conditions, at pH=7.0 and at
35oC (Fig. 3).
Increasing HRTs values (30 days, 60 days, 90
days, 120 days and 150 days) were studied with 400
mg/l of microalgae (Chlamydomonas reinhardtii and
Chlorella vulgaris) biomass using PP MPs in ABR
during anaerobic conditions for biochemical CH4(g)
production, at pH=7.0 and at 35oC (Fig. 4). 413 ml
CH4/gVS, 433 ml CH4/gVS, 345 ml CH4/gVS and
258 ml CH4/gVS biochemical CH4(g) productions
from PP MPs were obtained for 30 days, 90 days, 120
days and 150 days HRTs, respectively, in ABR
during anaerobic conditions, at pH=7.0 and at 35oC
(Fig. 4). The maximum 529 ml CH4/g VS
biochemical CH4(g) production from PP MPs was
observed for 60 HRTs in ABR during anaerobic
conditions, at pH=7.0 and at 35oC (Fig. 4).
400 mg/l of Chlamydomonas reinhardtii
microalgae was examined for the removal of PP MPs
in wastewater at 1872 h, [64]. 18% of growth
decrease, non-significant change in expression of
chloro-plastics genes and no effect on stress
response/apoptosis genes for Chlamydomonas
reinhardtii microalgae were evaluated by Sarmah
and Rout, [64].
3.5 Removals of PVC MPs with Microalgae
Biomass
Increasing HRTs values (30 days, 60 days, 90 days,
120 days and 150 days) were operated with 100 mg/l
of microalgae (Chlamydomonas reinhardtii and
Chlorella vulgaris) biomass using PVC MPs during
aerobic conditions for 1-butanol production, at
pH=7.0 and at 35oC (Fig. 3). 131 ml/gVS, 274
ml/gVS, 396 ml/gVS and 310 ml/gVS 1-butanol
productions from PVC MPs were observed for 30
days, 60 days, 120 days and 150 days HRTs,
respectively, during aerobic conditions, at pH=7.0
and at 35oC (Fig. 3). The maximum 459 ml/gVS 1-
butanol production from PVC MPs was measured for
90 days HRTs, during aerobic conditions, at pH=7.0
and at 35oC (Fig. 3).
Increasing HRTs values (30 days, 60 days, 90
days, 120 days and 150 days) were studied with 400
mg/l of microalgae (Chlamydomonas reinhardtii and
Chlorella vulgaris) biomass using PVC MPs in ABR
during anaerobic conditions for biochemical CH4(g)
production, at pH=7.0 and at 35oC (Fig. 4). 417 ml
CH4/gVS, 448 ml CH4/gVS, 356 ml CH4/gVS and
265 ml CH4/gVS biochemical CH4(g) productions
from PVC MPs were obtained for 30 days, 90 days,
120 days and 150 days HRTs, respectively, in ABR
during anaerobic conditions, at pH=7.0 and at 35oC
(Fig. 4). The maximum 541 ml CH4/g VS
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biochemical CH4(g) production from PVC MPs was
found for 60 HRTs in ABR during anaerobic
conditions, at pH=7.0 and at 35oC (Fig. 4).
The different concentrations of Chlorella vulgaris
microalgae biomass (10 mg/l, 100 mg/l and 1000
mg/l) were applied for the removal of PVC MPs in
wastewater at 240 h, [65]. Growth and biomass
inhibitions for 10 mg/l of Chlorella vulgaris
microalgae were recorded for the removal of PP MPs
from wastewater after 240 h, [65].
3.6 Energy Recovery for HDPE, LDPE, PP
and PVC MPs after Microalgae Biomass
Experiments
Energy recovery for HDPE, LDPE, PP and PVC MPs
were determined after microalgae biomass
experiments, at pH=7.0 and at 35oC (Fig. 5).
* Fig. 5 can be found in the Appendix section.
91.26%, 94.52%, 98.34% and 96.17% energy
recoveries were measured for HDPE, LDPE, PP and
PVC MPs after microalgae biomass experiments, at
pH=7.0 and at 35oC (Fig. 5). Maximum 98.34%
energy recovery was found for PP MPs after
microalgae biomass experiments, at pH=7.0 and at
35oC (Fig. 5).
3.7 Results of Inhibition Test
The algae inhibition test results showed that the small
MPs groups with sizes of ≈ 100 nm did not exhibit
algal inhibition. 1-butanol was produced for HDPE,
LDPE, PP and PVC from 100 mg/l of microalgae
biomass under aerobic conditions, while CH4(g) was
measured under anaerobic conditions from 400 mg/l
of microalgae biomass (Table 2).
* Table 2 can be found in the Appendix section.
The maximum values of 1-butanol productions,
biochemical CH4(g) productions and energy
recoveries for HDPE, LDPE, PP and PVC MPs were
evaluated after microalgae biomass experiments at
pH=7.0 and at 35oC (Table 2).
93 ml/g VS, 236 ml/g VS, 387 ml/g VS and 459
ml/g VS 1-butanol productions were obtained for
HDPE MPs, LDPE MPs, PP MPs and PVC MPs,
respectively, under aerobic conditions, at pH=7.0,
and at 35oC (Table 2). The maximum 459 ml/g VS 1-
butanol production was measured for PVC MPs
under aerobic conditions, at pH=7.0, and at 35oC
(Table 2).
452 ml CH4/g VS, 510 ml CH4/g VS, 529 ml
CH4/g VS and 541 ml CH4/g VS biochemical CH4(g)
productions were measured under anaerobic
conditions in ABR, at pH=7.9, and at 35oC (Table 2).
The maximum 541 ml CH4/g VS biochemical CH4(g)
production was found for PVC MPs under anaerobic
conditions in ABR, at pH=7.0, and at 35oC (Table 2).
91.26%, 94.52%, 98.34% and 96.17% energy
recoveries were determined for HDPE MPs, LDPE
MPs, PP MPs and PVC MPs, respectively, after
microalgae biomass experiments, at pH=7.0, and at
35oC (Table 2). Maximum 98.34% energy recovery
was observed for PP MPs after microalgae biomass
experiments, at pH=7.0, and at 35oC (Table 2).
3.8 A Possible New Approach and Its
Applicability
In today's technology, as in many areas, a wide
variety of methods are used to remove microplastics
from the ecosystem with the highest efficiency or to
reuse these stubborn and toxic waste materials by
converting them into alternative energy forms. Thus,
many alternative removal processes emerge when
choosing the most suitable process for zero waste
management. In recent years, the use of artificial
intelligence (AI) methods has been widely preferred.
AI methods allow learning how various
components interact and combinations of these
components run faster and are much more accurate
than running physical experiments for the same
amount of time. AI methods are frequently preferred
to save both time and financial resources. The most
commonly used AI methods are: (1) artificial neural
networks (ANN), (2) convolutional neural networks
(CNN), (3) long short-term memory network
(LSTMs), (4) k-nearest neighbors (k-NN or KNN)
and (5) random forest (RF).
In recent years, in order to predict interactions in
microalgae cultivation systems; The demand for the
use of AI methods is increasing, [66]. In some
studies, on this subject, artificial intelligence
algorithms such as ANN) and CNN genome
interactions, [67], [68], microalgal aggregation, [69],
biomass measurement, [70], and most importantly, it
provides improvement in processes by reducing the
number of experiments and situation optimization,
[71]. AI methods predict complex interactions
between wastewater treatment and microalgae
growth; The accumulation of internal metabolites is
an important alternative, especially in better
understanding basic factors such as lipid formation,
carbohydrates and energy conversion, and in
providing energy production at higher yields by
converting them into different forms.
As a new approach for this study, we chose to use
ANN, one of the AI methods. For a comparative
example study; We re-evaluated our data according
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to the ANN method. ANNs are increasingly preferred
to fill the gaps in CH4(g) flow time series [72], [73],
[74]. The most important advantages of ANNs are (a)
their greater capacity to model data with variable
temporal periodicity and (b) their independence from
previous assumptions regarding the functional
relationship between independent and dependent
variables [75], [76]. In this ANN approach,
established routines were followed; A feed forward
network with varying architectural complexity and
tan-sigmoid transfer functions was used. A
comparative summary of the error analysis results of
our experimental study and the ANN method is given
in Table 3.
* Table 3 can be found in the Appendix section.
Before network training, the 30-min streaming
time series was evenly subsampled into training,
validation, and testing subsets. Test subsets were
withheld from initialization and validation of
individual network trainings and were used only to
eliminate uncertainty in the final selected networks.
Network training and validation were repeated
multiple times with increasing complexity, i.e.,
increasing the number of hidden layers and neurons
per hidden layer. Thus, the ANN network reliability
rate has been further increased.
Among the educational variables tested; The 1000
ml laboratory-scale continuous anaerobic bioreactor
(ABR) was evaluated for anaerobic digestion
temperature (from 20 cm), activated sludge heat flux
(from an average of 8 heat flux plates at a depth of 15
cm), ambient active radiation (PAR), location of the
water table, active mud moisture and atmospheric
pressure were included. The existence of water and
steam deficit in anaerobic digestion was tested and
observed. First, these variables; were ranked
according to their correlation with observed methane
flux. These were then added stepwise to the training
dataset.
After the training and validation of each neural
network was completed, the mean square error
(MSE) and coefficient of determination of the
modeled data were calculated by comparing them
with the stored test data. Among the data found later;
We chose the network with the least number of
training variables, fewest hidden layers, least number
of nodes, lowest MSE, and highest R2. The ANN
routine, including random subsampling, training, and
validation, was repeated n = 50 times to calculate the
ANN-derived ensemble distribution of space-filled
time series. Uncertainty of the ANN approach; It was
then evaluated against the ensemble range, and the
resulting ensemble mean was used to fill the gap data.
4 Conclusions
The maximum 93 ml/gVS 1-butanol production from
HDPE MPs was measured for 90 days HRTs, during
aerobic conditions, at pH=7.0 and at 35oC. The
maximum 452 ml CH4/g VS biochemical CH4(g)
production from HDPE MPs was obtained for 60
HRTs in ABR during anaerobic conditions, at
pH=7.0 and at 35oC.
The maximum 236 ml/gVS 1-butanol production
from LDPE MPs was found for 90 days HRTs, during
aerobic conditions, at pH=7.0 and at 35oC. The
maximum 510 ml CH4/g VS biochemical CH4(g)
production from LDPE MPs was observed for 60
HRTs in ABR during anaerobic conditions, at
pH=7.0 and at 35oC.
The maximum 387 ml/gVS 1-butanol production
from PP MPs was measured for 90 days HRTs,
during aerobic conditions, at pH=7.0 and at 35oC.
The maximum 529 ml CH4/g VS biochemical CH4(g)
production from PP MPs was found for 60 HRTs in
ABR during anaerobic conditions, at pH=7.0 and at
35oC.
The maximum 459 ml/gVS 1-butanol production
from PVC MPs was obtained for 90 days HRTs,
during aerobic conditions, at pH=7.0 and at 35oC.
The maximum 541 ml CH4/g VS biochemical CH4(g)
production from PVC MPs was measured for 60
HRTs in ABR during anaerobic conditions, at
pH=7.0 and at 35oC.
91.26%, 94.52%, 98.34% and 96.17% energy
recoveries were determined for HDPE, LDPE, PP
and PVC MPs, respectively, after microalgae
biomass experiments, at pH=7.0 and at 35oC.
Maximum 98.34% energy recovery was found for PP
MPs after microalgae biomass experiments, at
pH=7.0 and at 35oC.
Low crude fibre proportions (< 3%) may be
indicative of low cellulose content in cell walls; and
this may facilitate cell lysis. The high ash content (>
17%) indicates that a significant fraction of the total
mass of microalgae will not be degraded during
digestion and therefore cannot be reduced to CH4(g).
Regarding the presence of possible inhibitors,
concentrations of the element Na in the biomass are
negligible, while high protein proportions (≈ 50%) in
microalgae biomass can lead to inhibition of the
digestion process due to the accumulation of FAN.
This issue needs to be considered when microalgae
are used as substrates.
Microalgal biomass (Chlamydomonas reinhardtii
and Chlorella vulgaris) can accumulate lipids and
carbohydrates under the stress of MPs (HDPE,
LDPE, PP and PVC), resulting in microalgal biomass
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through anaerobic digestion; were converted to
biodiesel, biobutanol, and biogas [e.g., CH4(g)],
respectively. Biotransformation results in residues
rich in MPs and is the most proposed way to solve the
problem of redistribution to the environment; It is the
thermochemical transformation of MPs. Microalgae
cells are bio-scavengers for MPs; They bind particles
to algal surfaces or incorporate them into algal cells,
where they are filtered from the water body and
eventually destroyed by further processing of the
contaminated biomass. Very high energy recovery
was achieved with the anaerobic digestion process.
Using microalgae biomass as biofuel feedstock for
the removal of MPs; It offers a much easier, cleaner,
more cost-effective and environmentally friendly
alternative to the use of fossil fuels.
The recorded results show that the removal
efficiency. Production of MPs by microalgae and its
underlying mechanism, it is affected by both the type
of plastic and the duration of exposure. Pre-exposure
it greatly increased the overall removal efficiency of
MPs and proved directly usable in real-life
applications.
Microalgal biomass can accumulate, lipids and
carbohydrates under MPs stress; It is assumed to play
a role in promoting the conversion of biomass to
biodiesel and biobutanol. Microalgae biomass can be
converted to biogas through anaerobic digestion.
Thus, biological transformation results in rich
residues. The most recommended method for these
rich residues is the thermochemical conversion
method; This method can also be applied as a post-
treatment process for the transformation of MPs.
This article proposes a new approach that could
help eliminate MPs. From contaminated water to the
combination of microalgae cultivation and
sustainable biofuels to reduce environmental
impacts; A net zero waste approach was used. Thus,
economic contribution to production is provided by
eliminating waste and converting it into energy, and
it is also possible to prevent stubborn and toxic
environmental pollution on the ecosystem. As an
important conclusion, as a possible new approach to
this study; Among AI technologies, the ANN Method
is safely recommended.
Acknowledgement:
This research study was undertaken in the
Environmental Microbiology Laboratories at Dokuz
Eylül University Engineering Faculty Environmental
Engineering Department, İzmir, Turkey. The authors
would like to thank this body for providing financial
support.
References:
[1] L. Wang, W.M. Wu, N.S. Bolan, D.C.W. Tsang,
Y. Li, M. Qin, D. Hou, Environmental fate,
toxicity and risk management strategies of
nanoplastics in the environment: Current status
and future perspectives, Journal of Hazardous
Materials, Vol.401, 2021, 123415.
[2] M. Oliveira, M. Almeida, I.A. Miguel,
Micro(Nano)Plastic boomerang tale: A never
ending story? TrAC - Trends in Analytical
Chemistry, Vol.112, 2019, pp. 196–200.
[3] C. Schwaferts, R. Niessner, M. Elsner, N.P.
Ivleva, Methods for the Analysis of
Submicrometer- and Nanoplastic Particles in the
Environment, TrAC - Trends in Analytical
Chemistry, Vol.112, 2019, pp. 52–65.
[4] K. Yin, Y. Wang, H. Zhao, D. Wang, M. Guo,
M. Mu, Y. Liu, X. Nie, B. Li, J. Li, M. Xing, A
comparative review of microplastics and
nanoplastics: Toxicity hazards on digestive,
reproductive and nervous system, Science of the
Total Environment, Vol.774, 2021, 145758.
[5] A.A. Koelmans, E. Besseling, W.J. Shim,
Nanoplastics in the aquatic environment, critical
review, Marine Anthropogenic Litter, Vol.67,
2015, pp 325–340.
[6] I. Ferreira, C. Venâncio, I. Lopes, M. Oliveira,
Nanoplastics and marine organisms: What has
been studied? Environmental Toxicology and
Pharmacology, Vol.67, 2019, pp. 1–7.
[7] T. Poerio, E. Piacentini, R. Mazzei, Membrane
processes for microplastic removal, Molecules,
Vol.24, 2019, 4148, pp.1-15.
[8] S. Lambert, C. Scherer, M. Wagner, Ecotoxicity
testing of microplastics: Considering the
heterogeneity of physicochemical properties,
Integrated Environmental Assessment and
Management, Vol.13, No.3, 2017, pp. 470–475.
[9] J.S.C. Viera, M.R.C. Marques, M.C. Nazareth,
P.C. Jimenez, Í.B. Castro, On replacing single-
use plastic with so-called biodegradable ones:
The case with straws, Environmental Science
and Policy, V.106, 2020, pp. 177-181.
[10] V.C. Shruti, G. Kutralam-Muniasamy,
Bioplastics: Missing link in the era of
microplastics, Science of The Total
Environment, Vol.697, 2019, 134139.
[11] J. Fojt, J. David, R. Přikryl, V. Řezáčová, J.
Kučerík, A critical review of the overlooked
challenge of determining micro-bioplastics in
soil, Science of The Total Environment, Vol.745,
2020, 140975.
[12] T.S.M. Amelia, W.M.A.W. Khalik, M.C. Ong,
Y.T. Shao, H.-J. Pan, K. Bhubalan, Marine
microplastics as vectors of major ocean
pollutants and its hazards to the marine
MOLECULAR SCIENCES AND APPLICATIONS
DOI: 10.37394/232023.2023.3.7
Deli
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n
E-ISSN: 2732-9992
76
Volume 3, 2023
ecosystem and humans, Progress in Earth and
Planetary Science, Vol.8, 2021, Article number:
12.
[13] Y. Cao, M. Zhao, X. Ma, Y. Song, S. Zuo, H. Li,
W. Deng, A critical review on the interactions of
microplastics with heavymetals: mechanism and
their combined effect on organisms and humans,
Science of the Total Environment, Vol.788,
2021, 147620.
[14] Z.-L. Zhu, S.-C. Wang, F.-F. Zhao, S.-G. Wang,
F.-F. Liu, G.-Z. Liu, Joint toxicity of
microplastics with triclosan to marine
microalgae Skeletonema costatum,
Environmental Pollution, Vol.246, 2019, pp.
509–517.
[15] Q. Sun, S.Y. Ren, H.G. Ni, Incidence of
microplastics in personal care products: an
appreciable part of plastic pollution, Science of
the Total Environment, 742, 2020, 140218.
[16] R. Coyle, G. Hardiman, K.O. Driscoll, (2020).
Microplastics in the marine environment: a
review of their sources, distribution processes,
uptake and exchange in ecosystems, Case
Studies in Chemical and Environmental
Engineering, Vol.2, 2020, 100010.
[17] G.A. Elizalde-Velázquez, L.M. Gómez-Oliván,
Microplastics in aquatic environments: a review
on occurrence, distribution, toxic effects, and
implications for human health, Science of The
Total Environment, Vol.780, 2021, 146551.
[18] J. Brandon, M. Goldstein, M.D. Ohman, Long-
term aging and degradation of microplastic
particles: comparing in situ oceanic and
experimental weathering patterns, Marine
Pollution Bulletin, Vol.110, 2016, pp. 299–308.
[19] V. Nava, B. Leoni, A critical review of
interactions between microplastics, microalgae
and aquatic ecosystem function, Water
Research, Vol.188, 2021, 116476.
[20] L.A. Holmes, A. Turner, R.C. Thompson,
Adsorption of trace metals to plastic resin pellets
in the marine environment, Environmental
Pollution, Vol.160, 2012, pp. 42–48.
[21] S.L. Wright, R.C. Thompson, T.S. Galloway,
The physical impacts of microplastics on marine
organisms: a review, Environmental Pollution,
Vol.178, 2013, pp. 483–492.
[22] C. Barría, I. Brandts, L. Tort, M. Oliveira, M.
Teles, Effect of nanoplastics on fish health and
performance: A review, Marine Pollution
Bulletin, Vol.151, 2020, 110791.
[23] P. Bhattacharya, S. Lin, J.P. Turner, P.C. Ke,
Physical adsorption of charged plastic
nanoparticles affects algal photosynthesis,
Journal of Physical Chemistry C, Vol.114,
No.39, 2010, pp. 16556–16561.
[24] M. Long, B. Moriceau, M. Gallinari, C.
Lambert, A. Huvet, J. Raffray, P. Soudant,
Interactions between microplastics and
phytoplankton aggregates: Impact on their
respective fates, Marine Chemistry, Vol.175,
2015, pp. 39–46.
[25] F. Lagarde, O. Olivier, M. Zanella, P. Daniel, S.
Hiard, A. Caruso, Microplastic interactions with
freshwater microalgae: Hetero-aggregation and
changes in plastic density appear strongly
dependent on polymer type, Environmental
Pollution, Vol.215, 2016, pp. 331–339.
[26] M. Long, I. Paul-Pont, H. Hegaret, B. Moriceau,
C. Lambert, A. Huvet, P. Soudant, Interactions
between polystyrene microplastics and marine
phytoplankton lead to species-specific hetero-
aggregation, Environmental Pollution, Vol.228,
2017, pp. 454–463.
[27] C. Zhang, X. Chen, J. Wang, L. Tan, Toxic
effects of microplastic on marine microalgae
Skeletonema costatum: Interactions between
microplastic and algae, Environmental
Pollution, Vol.220, pp. 1282–1288.
[28] P. Mohlenkamp, A. Purser, L. Thomsen, Plastic
microbeads from cosmetic products: an
experimental study of their hydrodynamic
behaviour, vertical transport and resuspension in
phytoplankton and sediment aggregates,
Elementa: Science of the Anthropocene, Vol.6,
2018, Art.61, pp. 1-16.
[29] C. Cunha, M. Faria, N. Nogueira, A. Ferreira, N.
Cordeiro, Marine vs freshwater microalgae
exopolymers as biosolutions to microplastics
pollution, Environmental Pollution, Vol.249,
2019, pp. 372–380.
[30] H.S. Carson, M.S. Nerheim, K.A. Carroll, M.
Eriksen, The plastic-associated microorganisms
of the North Pacific Gyre, Marine Pollution
Bulletin, Vol.75, No.1-2, 2013, pp. 126–132.
[31] C. Dussud, C. Hudec, M. George, P. Fabre, P.
Higgs, S. Bruzaud, A.-M. Delort, B.
Eyheraguibel, A.-L. Meistertzheim, J. Jacquin,
J. Cheng, N. Callac, C. Odobel, S. Rabouille, J.-
F. Ghiglione, Colonization of non-
biodegradable and biodegradable plastics by
marine microorganisms, Frontiers in
Microbiology, Vol.9, 2018, 1571.
[32] A. Bellingeri, E. Bergami, G. Grassi, C. Faleri,
P. Redondo-Hasselerharm, A.A. Koelmans, I.
Corsi, Combined effects of nanoplastics and
copper on the freshwater alga Raphidocelis
subcapitata, Aquatic Toxicology, Vol.210, 2019,
pp. 179-187.
MOLECULAR SCIENCES AND APPLICATIONS
DOI: 10.37394/232023.2023.3.7
Deli
a Teresa Sponza, Ruki
ye Özteki
n
E-ISSN: 2732-9992
77
Volume 3, 2023
[33] R. Radakovits, R.E. Jinkerson, A. Darzins, M.C.
Posewitz, Genetic engineering of algae for
enhanced biofuel production, Eukaryotic Cell,
Vo.9, No.4, 2010, pp. 486–501.
[34] S. Jeon, J.-M. Lim, H.-G. Lee, S.-E. Shin, N.K.
Kang, Y.-Il Park, H.-M. Oh, W.-J. Jeong, B.-R.
Jeong, Y.K. Chang, Current status and
perspectives of genome editing technology for
microalgae, Biotechnology for Biofuels, Vol.10,
2017, 267, pp.1-18.
[35] A. Doshi, S. Pascoe, L. Coglan, T.J. Rainey,
Economic and policy issues in the production of
algae-based biofuels: a review, Renewable and
Sustainable Energy Reviews, Vol.64, 2016, pp.
329–337.
[36] J.P. Maity, J. Bundschuh, C.Y. Chen, P.
Bhattacharya, Microalgae for third generation
biofuel production, mitigation of greenhouse gas
emissions and wastewater treatment: present
and future perspectives – a mini review, Energy,
Vol.78, 2014, pp. 104–113.
[37] J. Lü, C. Sheahan, P. Fu, Metabolic engineering
of algae for fourth generation biofuels
production, Energy and Environmental
Sciences, Vol.4, 2011, 2451.
[38] A.K. Bajhaiya, J. Ziehe Moreira, J.K. Pittman,
Transcriptional engineering of microalgae:
prospects for high-value chemicals, Trends in
Biotechnology, Vol.35, 2017, pp. 95–99.
[39] L. Brennan, P. Owende, Biofuels from
microalgae — a review of technologies for
production, processing, and extractions of
biofuels and co-products, Renewable and
Sustainable Energy Reviews, Vol.14, 2010, pp.
557–577.
[40] P.D. Patil, V.G. Gude, A. Mannarswamy, S.
Deng, P. Cooke, S. Munson-McGee, I. Rhodes,
P. Lammers, N. Nirmalakhandan, (2011)
Optimization of direct conversion of wet algae
to biodiesel under supercritical methanol
conditions, Bioresource Technology, Vol.102,
No.1, 2011, pp. 118-122.
[41] J. Li, X. Zheng, K. Liu, S. Sun, X. Li, Effect of
tetracycline on the growth and nutrient removal
capacity of Chlamydomonas reinhardtii in
simulated effluent from wastewater treatment
plants, Bioresource Technology, Vol.218, 2016,
pp. 1163-1169.
[42] G. Huang, D. Zhao, C. Lan, B. Wu, X. Li, S.
Lou, Y. Zheng, Y. Huang, Z. Hu, B. Jia,
Glucose-assisted trophic conversion of
Chlamydomonas reinhardtii by expression of
glucose transporter GLUT1, Algal Research,
Vol.62, 2022, 102626.
[43] P. Xie, C. Chen, C. Zhang, G. Su, N. Ren, S.-H.
Ho, Revealing the role of adsorption in
ciprofloxacin and sulfadiazine elimination
routes in microalgae, Water Research, 172,
2020, 115475.
[44] S. Tayari, R. Abedi, A. Rahi, Comparative
assessment of engine performance and
emissions fuelled with three different biodiesel
generations, Renewable Energy, Vol.147, No.1,
2020, pp. 1058–1069.
[45] D. Fu, Q. Zhang, Z. Fan, H. Qi, Z. Wang, L.
Peng, Aged microplastics polyvinyl chloride
interact with copper and cause oxidative stress
towards microalgae Chlorella vulgaris, Aquatic
Toxicology, Vol.216, 2019, 105319.
[46] X. Wu, H. Wu, A. Zhang, K. Sekou, Z. Li, J. Ye,
(2022). Influence of polystyrene microplastics
on levofloxacin removal by microalgae from
freshwater aquaculture wastewater, Journal of
Environmental Management, Vol.301, 2022,
113865.
[47] R.A. Labatut, L.T. Angenent, N.R. Scott,
Conventional mesophilic vs. thermophilic
anaerobic digestion: a trade-off between
performance and stability? Water Research,
Vol.53, 2014, pp. 249-258.
[48] C.G. Golueke, W.J. Oswald, H.B. Gotaas,
Anaerobic digestion of algae, Applied
Microbiology, Vol.5, No.1, 1957, pp. 47-55.
[49] V.H. Varel, T.H. Chen, A.G. Hashimoto,
Thermophilic and mesophilic methane
production from anaerobic degradation of the
cyanobacterium Spirulina maxima, Resources,
Conservation and Recycling, Vol.1, No.1, 1988,
pp. 19-26.
[50] C. Zamalloa, N. Boon, W. Verstraete, Anaerobic
digestibility of Scenedesmus obliquus and
Phaeodactylum tricornutum under mesophilic
and thermophilic conditions, Applied Energy,
Vol.92, 2012, pp. 733-738.
[51] R. Harun, M. Davidson, M. Doyle, R. Gopiraj,
M. Danquah, G. Forde, Technoeconomic
analysis of an integrated microalgae
photobioreactor, biodiesel and biogas
production facility, Biomass and Bioenergy,
Vol.35, 2010, pp. 741-747.
[52] T. Suganya, M. Varman, H.H. Masjuki, S.
Renganathan, Macroalgae and microalgae as a
potential source for commercial applications
along with biofuels production: a biorefinery
approach, Renewable and Sustainable Energy
Reviews, Vol.55(C), 2016, pp. 909-941.
[53] L.M. Chng, K.T. Lee, D.J.C. Chan, Synergistic
effect of pretreatment and fermentation process
on carbohydrate-rich Scenedesmus dimorphus
MOLECULAR SCIENCES AND APPLICATIONS
DOI: 10.37394/232023.2023.3.7
Deli
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ye Özteki
n
E-ISSN: 2732-9992
78
Volume 3, 2023
for bioethanol production, Energy Conversion
and Management, Vol.141, 2016, pp. 410–419.
[54] R. Singh, A.J. Mattam, P. Jutur, S.S. Yazdani,
Synthetic biology in biofuels production. In:
Meyers, R.A. (Ed.), Rev. Cell Biol. Mol.
Medicine: Encyclopedia of Molecular Cell
Biology and Molecular Medicine: Synthetic
Biology, 2(1), pp. 144-176, Publisher: Wiley-
VCH Verlag GmbH & Co. KGaA, New York,
NY, United States, 2016.
[55] D.S. Gorman, R.P. Levine, Cytochrome f and
plastocyanin: their sequence in the
photosynthetic electron transport chain of
Chlamydomonas reinhardii, Proceedings of the
National Academy of Sciences USA, Vol.54,
1965, pp. 1665-1669.
[56] E.H. Harris, The Chlamydomonas sourcebook:
a comprehensive guide to biology and
laboratory use, Academic Press, San Diego,
United States, 1989, 780.
[57] E.H. Harris, The Chlamydomonas Sourcebook,
Academic Press, Oxford, United Kingdom,
2009.
[58] H.W. Nichols, H.C. Bold, Trichosarcina
polymorpha gen. et sp. nov, Journal of
Phycology, Vol.1, No.1, 1965, pp. 34-38.
[59] R.K. Balasubramanian, T.T. Yen Doan, J.P.
Obbard, Factors affecting cellular lipid
extraction from marine microalgae, Chemical
Engineering Journal, Vol.215-216, 2013, pp.
929-936.
[60] W.C. Lipps, E.B. Braun-Howland, T.E. Baxter,
Standard Methods for the Examination of Water
and Wastewater, (24th. Edition). W.C. Lipps,
E.B., Braun-Howland, T.E. Baxter, (editors),
American Public Health Association (APHA),
American Water Works Association (AWWA),
Water Environment Federation (WEF), Elevate
Your Standards. American Public Health
Association 800 I Street, NW Washington DC:
20001-3770, United States, December 1, 2022;
ISBN:9780875532998.
[61] A. Torres, F.G. Fermoso, P. Neumann, L.
Azocar, D. Jeison, Anaerobic digestion as a tool
for resource recovery from a biodiesel
production process from microalgae, Journal of
Biobased Materials and Bioenergy, Vol.9, No.3,
2015, pp. 342-349.
[62] O. García-Depraect, R. Lebrero, S. Rodriguez-
Vega, S. Bordel, F. Santos-Beneit, L.J.
Martínez-Mendoza, R. Aragão Börner, T.
Börner, R. Muñoz, Biodegradation of
bioplastics under aerobic and anaerobic aqueous
conditions: Kinetics, carbon fate and particle
size effect, Bioresource Technology, 2021,
126265.
[63] M. Qin, C. Chen, B. Song, M. Shen, W. Cao, H.
Yang, G. Zeng, J. Gong, A review of
biodegradable plastics to biodegradable
microplastics: another ecological threat to soil
environments? Journal of Cleaner Production,
Vol.312, 2021, 127816.
[64] P. Sarmah, J. Rout, Role of algae and
cyanobacteria in bioremediation: prospects in
polyethylene biodegradation, Advances in
Cyanobacterial Biology, Vol.22, 2020, pp. 333-
349.
[65] C. Song, Z. Liu, C. Wang, S. Li, Y. Kitamura,
Different interaction performance between
microplastics and microalgae: the bio-
elimination potential of Chlorella sp. L38 and
Phaeodactylum tricornutum MASCC-0025,
Science of the Total Environment, Vol.723,
2020, 138146.
[66] S.Y. Teng, G.Y. Yew, K. Sukacová, P.L. Show,
V. Máša, J.S. Chang, Microalgae with artificial
intelligence: A digitalized perspective on
genetics, systems and products, Biotechnology
Advances, Vol.44, 2020, 107631.
[67] S. Dutta, S. Madan, H. Parikh, D. Sundar, An
ensemble micro neural network approach for
elucidating interactions between zinc finger
proteins and their target DNA, BMC Genomics,
Vol.17, 2016, 1033.
[68] J. Linder, N. Bogard, A.B. Rosenberg, G.
Seelig, A Generative Neural Network for
Maximizing Fitness and Diversity of Synthetic
DNA and Protein Sequences, Cell Systems,
Vol.11, 2020, pp. 49–62.
[69] A.M. Zenooz, F.Z. Ashtiani, R. Ranjbar, F.
Nikbakht, O. Bolouri, Comparison of different
artificial neural network architectures in
modeling of Chlorella sp. flocculation,
Preparative Biochemistry and Biotechnology,
Vol.47, 2017, pp. 570–577.
[70] J. Ruiz-Santaquiteria, G. Bueno, O. Deniz, N.
Vallez, G. Cristobal, Semantic versus instance
segmentation in microscopic algae detection,
Engineering Applications of Artificial
Intelligence, Vol.87, 2020, 103271.
[71] N.J. Treloar, A.J.H. Fedorec, B. Ingalls, C.P.
Barnes, Deep reinforcement learning for the
control of microbial co-cultures in bioreactors,
PLoS Computational Biology, Vol.16, 2020,
e1007783.
[72] S.H. Knox, C. Sturtevant, J.H. Matthes, L.
Koteen, J. Verfaillie, D. Baldocchi, Agricultural
peatland restoration: effects of land-use change
on greenhouse gas (CO2 and CH4) fluxes in the
MOLECULAR SCIENCES AND APPLICATIONS
DOI: 10.37394/232023.2023.3.7
Deli
a Teresa Sponza, Ruki
ye Özteki
n
E-ISSN: 2732-9992
79
Volume 3, 2023
Sacramento-San Joaquin Delta, Global Change
Biology, Vol.21, 2015, pp. 750–765.
[73] C. Sturtevant, B.L. Ruddell, S.H. Knox, J.
Verfaillie, J.H. Matthes, P.Y. Oikawa, D.
Baldocchi, Identifying scale-emergent,
nonlinear, asynchronous processes of wetland
methane exchange, Journal of Geophysical
Research: Biogeosciences, Vol.121, 2016, pp.
188–204.
[74] J.P. Goodrich, W.C. Oechel, B. Gioli, V.
Moreaux, P.C. Murphy, G. Burba, D. Zona,
Impact of different eddy covariance sensors, site
set-up, and maintenance on the annual balance
of CO2 and CH4 in the harsh Arctic environment,
Agricultural and Forest Meteorology, Vol.228–
229, 2016, pp. 239–251.
[75] A.M. Moffat, D. Papale, M. Reichstein, D.Y.
Hollinger, A.D. Richardson, A.G. Barr, C.
Beckstein, B.H. Braswell, G. Churkina, A.R.
Desai, E. Falge, J.H. Gove, M. Heimann, D. Hui,
A.J. Jarvis, J. Kattge, A. Noormets, V.J. Stauch,
Comprehensive comparison of gap-filling
techniques for eddy covariance net carbon
fluxes, Agricultural and Forest Meteorology,
Vol.147, 2007, pp. 209–232.
[76] D. Papale, M. Reichstein, M. Aubinet, E.
Canfora, C. Bernhofer, W. Kutsch, B. Longdoz,
S. Rambal, R. Valentini, T. Vesala, D. Yakir,
Towards a standardized processing of Net
Ecosystem Exchange measured with eddy
covariance technique: algorithms and
uncertainty estimation, Biogeosciences, Vol.3,
2006, 571–583.
Contribution of Individual Authors to the
Creation of a Scientific Article (Ghostwriting
Policy)
Prof. Dr. Delia Teresa Sponza and Post-Dr. Rukiye
Öztekin took an active role in every stage of the
preparation of this article.
The authors equally contributed in the present
research, at all stages from the formulation of the
problem to the final findings and solution.
Sources of Funding for Research Presented in a
Scientific Article or Scientific Article Itself
This research study was undertaken in the
Environmental Microbiology Laboratories at Dokuz
Eylül University Engineering Faculty Environmental
Engineering Department, İzmir, Turkey. The authors
would like to thank this body for providing financial
support.
Conflict of Interest
The authors have no conflicts of interest to declare
that are relevant to the content of this article.
Creative Commons Attribution License 4.0
(Attribution 4.0 International, CC BY 4.0)
This article is published under the terms of the
Creative Commons Attribution License 4.0
https://creativecommons.org/licenses/by/4.0/deed.en
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APPENDIX
Fig. 1. Biofuel production from microalgae through various transformation processes
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Fig. 2. Schematic diagram of 1-butanol production from MPs with microalgae under aerobic conditions.
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Table 1. Results of the elemental analysis of microalgae (Chlamydomonas reinhardtii and Chorella vulgaris)
biomass with oil extracted and dried at 105oC.
Parameters
Microalgae Biomass Compositions (%)
Protein
51.21
Carbohydrates
19.11
Fat
7.23
Moisture
2.47
Na (mg/100 g)
1952
Crude fibre
2.24
Ash
17.64
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Fig. 3. 1-Butanol productions for HDPE, LDPE, PP and PVC MPs during aerobic conditions after different
HRTs, at pH=7.0 and at 35oC.
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Fig. 4. Biochemical CH4(g) productions for HDPE, LDPE, PP and PVC MPs in ABR during anaerobic conditions
after different HRTs, at pH=7.0 and at 35oC.
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Fig. 5. Energy recovery for HDPE, LDPE, PP and PVC MPs after microalgae biomass experiments, at pH=7.0
and at 35oC.
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Table 2. Results of inhibition test for HDPE, LDPE, PP and PVC MPs after microalgae biomass experiments, at
pH=7.0 and at 35oC.
MPs
Biochemical CH4(g) Productions
(ml CH4/g VS)
Energy Recoveries (%)
HDPE
452
91.26
LDPE
510
94.52
PP
529
98.34
PVC
541
96.17
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Table 3. The comparative summary of error analysis results with our study and artificial neural networks
(ANN) example approach (MAE: mean absolute error, RMSE: root mean square error, BE: bias error, Gap-
fill ranges represent the ensemble of budgets derived from bootstrapped datasets using the respective gap-filling
method).
Gap-
fill
metho
d
CH4(g)
analyse
r
MAE
(nmol/m2.s
)
RMSE
(nmol/m2.s
)
BE
(nmol/m2.s
)
R2
Cumulativ
e flux (g-
CH4/m2)
Gap-
fill
rang
e
Relativ
e gap-
fill (%)
In this
study
TGA
9.5
14.1
0.24
0.9
7
63.0
62.6
–
63.4
2
ANN
TGA
7.8
10.3
0.20
0.9
9
62.8
62.4
–
63.3
5
In this
study
LI-7700
8.7
11.1
0.13
0.9
6
64.7
64.2
–
72.1
3
ANN
LI-7700
8.4
9.9
0.11
0.9
9
64.3
64.0
–
64.9
2
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