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FOCUS
ON
EN V IRONMENTAL
ANALYSIS

Identification of Microplastics in Environmental
Monitoring Using Pyrolysis–GC–MS Analysis
Microplastics are now recognized as pervasive in the environment, as well as in stormwater, wastewater, ocean water, coastal
sediments, drinking water, and even animal tissue. The mismanagement of plastic waste throughout the world is a matter ofurgent concern that has led to product bans for small plastics, such as microbeads used in cosmetic products, to larger plastic item,
such as bags and straws, that can degrade into microplastics, This concern has also resulted in new and planned requirements tomonitor microplastics in the environment. The implementation of monitoring programs requires reliable standardized methods
and best practice guidelines, yet environmental measurement and monitoring practitioners still do not have established practices
and methods for collecting and analyzing plastic pollution. This deficiency has resulted in significant challenges in the interpretationof research findings for use in corporate or government policy. A major issue with measurement based upon sampling and analysisof microplastics is their unique behavior; environmental microplastics occur as non-homogeneously distributed particulates andfibers composed of various polymeric materials. This article provides an overview of the environmental and analytical challenges
caused by microplastics; it proposes an analytical measurement approach that enables accurate qualitative and quantitative
analysis of microplastics, and demonstrates some of the advantages of pyrolysis–gas chromatography–mass spectrometry(Py–GC–MS) for the analysis of environmental samples as compared to the other leading analytical methods.

P

lastic waste has proven very difficult tomeasure. In 2019, 368 million tons ofplastic were produced (1), an increase of
more than 600% over the last four decades(2) (Figure 1). Global collection rates ofplastic waste over the same timeframe
remained stagnant, not surpassing 10% of
production worldwide (3). Collection andsorting of plastic waste into recoveredmaterial is not a guarantee that it will berecycled. This low recycling rate has been
exacerbated by implementation of China’s
National Sword Policy on January 1, 2018,with its rejection of imported recovered
plastic waste from Western countries.
Examination of the sources of these
problems can also be misleading. For
example, a leading economic model uses
surrogate factors for plastic waste mismanagement and depicts non-Western countries as being primarily responsible for the
inputs of plastic pollution into the environment (4). These models miss the pointthat countries like the United States havehigh per capita plastic consumption withsporadic and inconsistent waste management infrastructure, often exporting large
portions of recovered plastic waste to nonWestern countries (5). Final disposition ofplastic waste exports from Western countries is now a bigger unknown than it wasprior to 2018, with likely more plastic ending up in landfills, burned, or accumulatingin outdoor heaps and piles and migrating
to watersheds, creeks, streams, rivers, andthe ocean during wind and rain events.
The problem of plastic pollution does notbelong to “other” countries, but is verymuch one of every nation. As plastic debris
enters roadways and watersheds, it threatens our natural environment (Figure 2), pollutes and clogs stormwater conveyances,and can pose significant sanitation andpublic health issues, such as an increase instanding water and mosquito vector growth.
As debris enters streams and rivers, micro-

and nanoparticles are generated at rates
dependent on the forces acting uponthem and their polymer type and plastic form. We anticipate that microplastic
monitoring will become more prevalent asa means for measuring plastic pollution in
the environment.
Plastic debris in the environment is notinert. Plastic debris ultimately fragments
into smaller particles over time as a resultof the effects of weathering, including
UV radiation and wind and wave action.
As debris breaks down into smaller andsmaller particles, polymer surface areas
increase relative to their volume, andthese surfaces can host pathogens suchas bacteria and viruses. These smaller
plastic particles, with their relatively higher
surface area, have a greater propensity tosorb persistent organic pollutants (POPs)such as polychlorinated biphenyls (PCBs)
and pesticides from surrounding air, dust,
and water.

Icon Image: (Columns) Joe Zugcic / Zugcic Photographers, Inc.

William Pipkin, Rojin Belganeh, William Robberson, Harry L. Allen, Anna-Marie Cook, and Atsushi Watanabe

Plastic production, millions of tonnes per year

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400

300

200

100

0
1917 1927 1937 1947 1957 1967 1977 1987 1997 2007 2017

FIGURE 1: Yearly global plastic production.

FIGURE 2: Plastics on Midway Island and stomach contents of an albatross in the NW
Hawaiian Islands.

M080
PE

Raman identification of plastic particles
harvested from mesopelagic Myctophidae

extent plasticizers from within microplastics
and any sorbed contaminants on the surfaceof particles are able to desorb, and, uponingestion by an organism, be taken up by
the organism’s tissue and organs (Figure3). These contaminated microplastics form
dust pools both indoors and outdoors.
Plastic particles have been documented inevery environmental media, and reports in2019 and 2020 present evidence that plastic
is even raining down on us. This pervasiveness and persistence of microplastics andnanoplastics cause some to speculate thatscientists of the future will use these plastics
as a tool for dating abandoned habitations
and geologic strata.
The behavior of microplastics in environmental media is expected to be somewhat
more predictive than that of larger debris,which is subject to chaotic ebbs and flows
of wind and water. Although plastic fragments may initially float on the water’s surface, they soon become suspended in thewater column as they become covered inbiological growth; biofouling increases the
specific gravity, which decreases buoyancy.
It has been assumed that, ultimately, mostwaterborne microplastics are destined forthe floor of an ocean, lake, or river, perhaps being ingested along the way orresuspended through various mechanisms
on their way to, and once on, the bottom.
However, colloidal suspension and electrostatic interaction between the smallest ofthese plastic particles and the resulting effecton transport and occurrence in the environment is an area of study that has yet to
be explored.
Until recently, calculations of density anddistribution of plastic in the environment
focused almost exclusively on the visible size
range above 300 µm, and employed visualtechniques as a means of identification.
In addition, these samples were collected on
the ocean surface. The majority of microplastics in the environment, both water and air,are now believed to be smaller than 300 µm.

FIGURE 3: Plastic (PE) extracted from a 9 cm lanternfish (myctophid) in the Northern
Pacific Ocean.

You Can’t Manage
What You Can’t Measure

Additionally, the manufacturing processof plastic products typically adds chemical
constituents, or plasticizers, to augment

Rigorous and reproducible identificationof plastic particles is critical to pollution
monitoring. Considering the mantra “youcan’t manage what you can’t measure,”polymer characteristics and performance,and some of these additives are toxins or
carcinogens. It is currently unknown to what

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1,800,000

r = 0.9996
LOQ: 0.51 µg

1,600,000
1,400,000

Peak area

effective upstream policy, including corporate and government action to reduce theoccurrence of plastic particles, must rely
on robust monitoring regimens. Efficacy of
mitigation actions will not be measurable
without such monitoring, and measurement
standards therefore must be agreed on andwidely adopted.
The risk of microplastics to our naturalenvironment, and to public health, can be
described as the likelihood of harm basedon both toxicity and exposure, yet thisunderstanding of risk has been stymied
by the lack of rigor, standardization, andreproducibility of measurement practices
and methods. While spectroscopy techniques measure plastic particle count, theproperties of smaller plastic particles createa sort of fingerprint more easily traced with
mass-based concentrations. Mass-based
concentrations are much more conducive
to benchmark comparisons such as ambientconditions or regulatory thresholds. Measuring concentrations of these tiny plastic
particles and their effects on humans and1,200,000
1,000,000
800,000
600,000
400,000
200,000
0
0

2

4

6

8

10

Amount (µg)
FIGURE 4: Calibration curve for polystyrene.

animals necessitates the global standardization of collection, preparation, and identification procedures and methods.
Standardized methodologies for nondestructive collection, handling, separation,
sample preparation, and positive identification of microplastics are in the early stages ofdevelopment. ASTM International is leading
the way with recently published standards forcollection (D8332) and preparation (D8333)
of microplastic particles. Some of the leading technologies currently available that yield
scientifically defensible and reproducible
results for positive microplastic identification
include focal plane array detection, thermogravimetric analysis–pyrolysis-gas chromatography-mass spectrometry (Py–GC–MS),and infrared (IR) and Raman spectroscopy.

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Auto-Shot Sampler

Pyrolyzer

He carrier gas

GC injector

Backflush
Split ventPre-column

Separation column
MS

GC

FIGURE 5: Pyrolysis-GC–MS system configuration using a microfurnace pyrolyzer.

C9’
(PP)

40!JG40-<?DDK?C@L

Capro
(N-6)

CP
(N-66)

BP
(PET)

IPP
(PC)

SAS
(ABS)

C21”
(PE)

Naph
(PVC)

//MMA
(PMMA)MDA(PU)

Signal

Me-Eic(Ref.)

4

6

8

10

12

14

16

SSS
(PS)

18

20

<9E9FE:6FGE:H9GIGH:F
Retention
time (min)FIGURE 6: Typical pyrogram of 11 target polymers using microfurnace technique.

Reference mixture

SMS for reference
mixture

TIC
7

8

9
Retention time (min)

10

11

43
41

EIC

m/z: 41m/z: 41m/z: 41m/z: 43

m/z: 43

m/z: 43

m/z: 70m/z: 55

m/z: 55

43

70

41 55
Mass
39
69 83
spectra

126

m/z

41 43
55
69
83 111
97 125168

43
41 5569

111
126

m/z

55 70

39

210

69

111

126 154

m/z

Similarity
98.4%

43
41 55 69
85
83
71 111 154
97 125 167

m/z

83

m/z

SMS for PP
standard

PP standard

43

meso
racemo

TIC

41

55 70

39

69

83

111 126 154

m/z
7

8

9
Retention time (min)1011

FIGURE 7: Identification process for PP in reference mixture using F-Search automatedalgorithm.

Presently, most researchers attempting
to positively identify polymers are employing IR or Raman spectroscopy for analysis
of particle type and count. Information
on particle polymer type, count (per unit
volume) and size are germane to specificresearch contexts, and some advanced
spectroscopy technologies have reduced
operational procedures from upwards ofa week to only hours; in most instances,
spectroscopy remains time consuming
and costly, and requires highly trained
and skilled technicians with sophisticatedlaboratory equipment. Although a highdegree of skill is also needed for Py–GC–
MS, this technique offers a solution to
speed up sample preparation and analysis
time, while yielding perhaps more practical information on polymer types and mass
per volume.
For water quality monitoring activities
such as discharge management, plastic
particle identification by mass per unit
volume may be more useful for both point
source and non-point source measurement.
We believe that the use of Py–GC–MS forpolymer identification has broader applicability than IR or Raman spectroscopy,
because it is less constrained by particle size
(large or small), requires less preparation,
has fewer interferences, has less potential
for analytical error, enables straightforward calibration, provides consistent andhighly reproducible results, much lower
equipment entry costs, and readily available polymer identification libraries. It isalso conducive to varied matrices such as
water, soil, and biota, enabling more easily
accomplished comparisons.
Here, we outline the development of
the Py–GC–MS method, and explain its
practical application to understanding
the occurrence and prevalence of microplastics, and potentially even nanoplastics,
in the environment.

The Role of Py–GC–MS
Recent studies have discovered theunique capability of the microfurnace
pyrolysis-GC–MS technique in the identification of polymer types and quantities in microplastic particles. This article
describes the study of microplastics inwater samples with high and low sus-APRIL 2021  LCGC NORTH AMERICA  VOLUME 39 NUMBER 4  183

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pended solids using a microfurnace
pyrolyzer directly interfaced with abenchtop gas chromatography–massspectrometry (GC–MS) instrument.
The samples were collected from drinking water, wastewater influent and effluent, surface water, and marine water. The
developed method using the microfurnace Py–GC–MS confirms that the analysis of microplastics in such water samples
is independent of polymer type, and not
limited to any particular water matrices.
The method proves higher analytical sensitivity for both qualitative and quantitative
analyses, including better detection limits.
The inorganic contaminants in the sample
matrices do not impede the identification and specification of the polymers andassociated additives. Detailed information
about the chemical nature of the polymer
and contained organic additives are also
obtained simultaneously.
Most of the initial research on analyzing microplastics was developed using
spectroscopy techniques such as Fouriertransform infrared (FT-IR) and Raman spectroscopy. These traditional techniques arenondestructive, and commonly used inanalytical laboratories. They provide information for functional group identificationand library comparison. Detailed information about the contained organic additives
is usually not obtained because contaminants (organic or inorganic) and additives
can overlap polymer bands. In other words,

TABLE I: Polymers validated for pyrolysis–GC–MS using micro-furnace: showing data
precision for each polymer (n=5)
Polymer Name

RSD%

Polyethene (PE)3.1

Polypropylene (PP)

1.8

Polyvinylchloride (PVC)

1.7

Polycarbonate (PC)

2.8

Polyethylene terephthalate (PET)

3.8

Poly (methyl methacrylate) (PMMA)

2.4

Nylon-6 (N-6)

3.8

Polystyrene (PS)

4.0

Acrylonitrile-butadiene-styrene copolymer (ABS)1.9

Nylon-6,6 (N-6,6)

1.0

MDI-Polyurethane (PU)

7.1

Reference (Ref)

0.0

FT-IR can suffer from peak masking if two
or more compounds in a mixture with thesame dipole moments absorb IR rays at
the same or nearby wavelength. In thiscase, identifying the overlaid peaks will be
extremely difficult. With Raman spectroscopy, measurement of samples that containfluorescent dyes is very challenging.
The need for sample preparation, such as
matrix isolation, solvent extraction, or pretreatment, is another challenge with these
traditional techniques. Typically, it could
take several hours to perform the analysis,and quantitation is challenging. The limit on
sample particle size with these techniques

also must be considered. Micro-Raman
spectroscopy and an optical microscope
are used for particle size analysis that islarger than 1 µm, whereas FT-IR can be used
for particle size larger than 10 µm.
In contrast, microfurnace Py–GC–MS canbe used for both qualitative and quantitative analysis of low microplastics concentrations, because of its high sensitivity capability. Although there is no practical lower
limit in particle size, total polymer mass ina sample constrains the lower limit of themeasurement (see Figure 4). In addition to
microplastics, GC–MS adds the benefit ofdetecting and quantifying both additives

Analytical
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TABLE II: F-Search results obtained for the mixed polymer standard and a marine
microplastic sample
Reference mixture
Polymer
Match

S/N

RSD%

Real Sample
ALD
(μg)

Match

S/N

RSD%

PE

98.5

50

4.1

0.5

98.0

80

7.6

PP

98.4

900

6.9

0.09

97.4

900

1.0

PS

98.4

700

2.5

0.2

17.9

1

-

PVC

99.5

330

6.9

0.1

37.3

1

-

PMMA

98.9

120

5.8

0.08

77.8

1

-

N6

96.7

260

10.3

0.2

84.6

1

-

N66

98.8

80

7.8

3.0

77.3

1

-

ABS

97.0

70

8.0

0.2

60.1

1

-

PET

99.9

2100

18.7

0.02

94.9

50

7.1

PC

99.6

3300

16.9

0.002

99.8

800

8.4

PU

98.6

20

7.0

0.4

89.8

1

-

and contaminants in the sample using interpretation libraries. Sample preparation iseasy and straightforward; micrograms ofa sample (including solids taken “as is”)
are placed into an inert pyrolysis cup, andthe analysis can be performed. The recent
developments in F-Search software formicroplastics data processing, which is an
automated analysis (quantitative and qualitative), has also significantly shortened thetime needed for the analysis.
The microfurnace utilizes a low thermal
mass ceramic heater to quickly heat andcool with thermal uniformity. This technology can be used for multiple analytical techniques in addition to flash pyrolysis, such as
evolved gas analysis (EGA), thermal desorption (TD), double-shot (TD followed by flash
pyrolysis), and heart-cutting. All the surfaces
are quartz or temperature stable deactivated, and there is no cross-contamination.
The microfurnace performs continuous pyrolysis in a single step by directly
depositing all the pyrolyzates on column
as they are generated. The direct connection of the microfurnace to the GC injection port allows this single-step pyrolysis
where reproducibility and temperatureaccuracy are obtained. The sample isheld near ambient temperature and in
an inert atmosphere prior to pyrolysis.
Therefore, there is no chance for evaporation or degradation of the sample before

the analysis, maintaining sample integrity,and contributing to accuracy, precision, andreproducibility of the data set. The microfurnace is preheated to the desired temperature, and precisely measured with a thermal
couple sensor. The sample cup then freefalls into the quartz pyrolysis tube, where thesample is rapidly and reproducibly pyrolyzed.
The pyrolyzates are directly swept onto the
GC column for separation and detection by
MS. A critical factor with the microfurnace is
its ability to control the actual sample temperature to within ±0.1 °C set point of thepyrolysis temperature. This feature guarantees reproducibility and precision, as well astrue flash pyrolysis, where the set temperature in the furnace by the analyst reflects thepyrolysis temperature on the sample.
There are other pyrolysis technologies:
filament, which uses platinum coil wire,
and Curie-point, which utilizes a ferromagnetic foil. These technologies can sufferfrom poor temperature accuracy and poor
reproducibility because of non-uniform
heating. In both systems, to avoid condensation of the pyrolyzates, the sample must
be inserted into a heated manifold before
pyrolysis. This preheating step can cause
degradation or thermosetting of the samples prior to analysis. With filament pyrolyzers, sample placement and heat-transfer
variations also affect the temperature of thesample, depending on where the sample

sits in relation to platinum wire coils andon sample contact. The filament temperature is estimated from the electrical properties of the wire, but the temperature of
the sample is not actually measured as
it changes over time. In other words, theplatinum wire can only control the temperature of its heating element, not the actual
sample pyrolysis temperature, requiring
the user to program the system at a muchhigher temperature than the desired true
pyrolysis temperature on the sample.
In a study of microplastics in surface waterusing a filament pyrolyzer, the pyrolysis temperature was set at 750 °C, with the sample
placed in a narrow quartz tube inside theplatinum wire (6). Based on actual bondenergy strengths, polymers and otherorganic material will typically pyrolyze prior
to a temperature of approximately 700 °C.
Therefore, the only reason to set an initialpyrolysis temperature in excess of 700 °C isthat the actual sample temperature in thepyrolyzer is not accurate. Literature indicating pyrolysis at 1200 or 1300 °C is either
unnecessary, over pyrolysis, or shows poor
actual sample temperature control in theparticular pyrolysis device.
It is critical to note that some filament pyrolyzers perform 2-step pyrolysis. First, some ofthe pyrolyzates are collected on a trap, andthen a switching valve sends the pyrolyzatesfrom the trap through a long transfer line
into the GC column. In this configuration,
the pyrolyzates are indirectly placed on column. During this two-step process, polarand heavy compounds may cause crosscontamination, or become irreversibly stuckin the trap or the long transfer line, and are
not transferred for analysis because of thepotential of cold spots in the system.

Materials and Methods
Figure 5 shows the system configuration of
the microfurnace pyrolysis- GC–MS used for
microplastic analysis in the water samples.
Experiments are performed with a microfurnace pyrolyzer (EGA/PY-3030D, Frontier
Laboratories Ltd.) with Auto-Shot Sampler(AS-1020E, Frontier Laboratories Ltd.) coupled to a GC–MS system (5977B MDS, Agilent Technologies, or GCMS QP-2020NX,
Shimadzu Corporation,). Chromatographic
separation is achieved using a metal capillary

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column (UA-5, 0.5 μm film of 5% diphenyl-95% dimethylpolysiloxane,
30 m x 0.25 mm, Frontier Laboratories Ltd.) with helium (He) as
the carrier gas (1 mL/min). Reduced analysis time can be obtainedby utilizing a backflush system, especially when dealing with highboiling point matrix interference or high molecular weight pyrolysis
products. This system consists of a precolumn (UA-50, 1 μm film
50% diphenyl 50% dimethylpolysiloxane, 2 m x 0.25 mm, Frontier
Laboratories, Ltd.) connecting the injector and the separation column with a three-way split (7).
Using the flash pyrolysis mode of operation (the single-shot mode)of the microfurnace, the chemical composition analysis is performedon microplastic samples. The microfurnace is programmed at 600°C to break down the organic bonds of the samples into smaller
and more stable molecules, referred to as pyrolyzates. The pyrolyzates are directly swept from the pyrolysis tube into a GC column,
via a needle fixed in a split injection port. The column is interfaced
directly to an MS instrument. The chemical species are ionized andsorted, based on their mass-to-charge ratio. Compounds eluted
from the GC column are identified by comparing the mass spectra
at a predetermined retention time to reference spectra and retention index (RI) to those contained in a database.

Results
Table I shows some of the polymers that can be identified using
microfurnace Py–GC–MS. The validation is performed using a pyrolyzate primary quantitative ion and identification ion(s) representative of a specific polymer. It is important to note that the developed
method is not limited to the polymers listed in the table below.
Figure 6 demonstrates a typical pyrogram of the 11 target pyrolyzates and their associated polymer (in parenthesis), using the developed method described above.
The concentration of each identified polymer is determinedusing either internal or external standard calibration. Using a multicomponent standard (8), the calibration curves can be obtained.
Table I lists the data precision of each polymer for n = 5.

Data Processing Using F-Search
As mentioned earlier, F-Search, an automated search program, hasbeen augmented for the fast identification and quantitation (in therange of seconds) of microplastics. The search algorithm is basedon the identification of the selected number of characteristic pyrolyzates of each polymer in the sample. Each selected pyrolyzate is only
obtained from the pyrolysis of a polymer, and not from the pyrolysisof all other investigated polymers. For each characteristic pyrolyzate,
a set of specific m/z signal intensities is also selected. A normalizedsummated mass spectra (SMS) is obtained from the selected m/z
signal intensities so that the mass spectra are cleared from any interfering signals due to co-eluted species. The similarity of the obtained
SMS with the one in the library is calculated and expressed as a percentage. A signal-to-noise ratio (S/N) is calculated for each polymer
by extracting a characteristic m/z signal chosen from one of the main
pyrolyzates. If the S/N is lower than a set threshold, the presence ofthe corresponding polymer is excluded, regardless of the similarity
index obtained by the algorithm.

Figure 7 demonstrates an example of the identification processof polypropylene (PP) in the reference mixture using the automatedF-Search program. The ions at m/z 41, 43, 55, and 70 were selected
as the representative four pyrolyzates of PP. The peaks of each characteristic pyrolyzate identified in the extracted ion chromatograms(EICs) are marked with dots. The SMS of these peaks was then compared with that obtained for reference PP. Because a similarity of
98.4% was obtained, the presence of PP in the reference mixture
was confirmed. The results obtained with the algorithm for the two
samples are summarized in Table II (5), along with the calculated
S/N values and the limit of detection of the algorithm. Both a highmatch and S/N greater than the set threshold were obtained for allpolymers in the reference mixture.

Summary
The microfurnace Py–GC–MS technique provides detailed information about polymers, additives, and even contaminants.
The identification of polymers is rapid and automated, easy to use indifferent laboratory setups, and less costly compared to traditional
spectroscopy techniques. Neither the presence of biofouling northat of contaminants or additives impedes the identification andquantitation of polymer types.
The sample preparation is easy and straightforward; the sample
is placed into an inert sample cup, and held in the ambient atmosphere before the analysis starts. Once the furnace achieves the186  LCGC NORTH AMERICA  VOLUME 39 NUMBER 4  APRIL 2021WWW.CHROMATOGRAPHYONLINE.COM

desired temperature as programmed by
the user, the sample cup freefalls into themicrofurnace, where rapid and reproducible pyrolysis happens. All the generated
pyrolyzates are directly placed on column
in a single step, foregoing the need for atransfer line or chemical trap.
The F-Search automated program significantly increases analytical efficiency byproviding polymer identification in a matterof seconds. Microplastics are often a mixture of polymers and additives, and in mostcases, they are co-eluted, so identification
using spectroscopy techniques is challenging. However, the co-elution of compounds
and contamination in the microplastic mixture samples does not affect the ability of
microfurnace Py–GC–MS to provide simultaneous identification and specification ofpolymers and additives.
Another advantage of this technique isthe elimination of solvent extraction or sample pretreatment before analysis. The microfurnace can be used for thermal desorption
analysis where the additives and light compounds are thermally extracted from a mixture. Laboratories save money over time byusing fewer solvents, while also protecting
analysts and the environment from solvent
exposure and hazards.
The microfurnace produces reproducible data, and controls the actual sample
temperature to within ±0.1 °C of the pyrolysis temperature. Other types of pyrolyzer
devices can only control the temperatureof the heating element, and not the actual
sample temperature. Also, the ability ofthe microfurnace to protect the sample
from any heat prior to the analysis and theabsence of the transfer line makes it possible to analyze a wide range of compounds,
including additives, volatiles, light, heavy,polymeric, and polar compounds.
The collection approach and sample
number must always be considered in theanalysis of microplastics. Often, water samples are filtered to collect the microplastic mixture. With Py–GC–MS, only a small
amount of sample (microgram quantities)
is needed, and multiple analyses can be
performed with one-time sampling. This
new Py–GC–MS method is currently underdevelopment as an official ASTM methodby an ASTM International workgroup.

A concerted effort must be made tostandardize and make widely available
these types of sampling and analyticalmethodologies, as well as streamline
methodologies and reduce their costs.
As the demand for microplastics measurement and monitoring increases,
practitioners must have access to reliable and repeatable methods. Ideally, these methods would provide both
qualitative and quantitative information,and be comparable to other standardized and commonly used environmental
measurement techniques.
The ability to adapt these technologies for field deployment is essential togain a better understanding of the quantity, distribution, and behavior of plastic
particles in all environmental matrices.
Common reporting of microplastic concentration data will support both government and industry responses to thegrowing crisis of plastic pollution. As
described, microplastic measurement provides advantages over traditional debris
monitoring. Mass-based analysis is directlycomparable to other environmental contaminant analysis methods, and has anadvantage over spectroscopy techniques
because it avoids complicated preparationand the need to count individual particles
after identification.
New frontiers in microplastic measurement and monitoring require robust andrepeatable analytical techniques. Analytical information should be the cornerstone of corporate and government
pollution mitigation efforts, and Py–GC–
MS will be critical in the evaluation ofefforts to curb plastic pollution aroundthe world.

Acknowledgments
The authors wish to thank the scientists at
Frontier Laboratories whose work madethis article possible: T. Ishimura, I. Iwai, M.
Matsueda, A. Shiono, K. Matsui, N. Teramane, and C. Watanabe.

Disclaimer
The views expressed in this article are thoseof the authors and do not necessarily reflect
the views or policies of the U.S. Environmental Protection Agency.

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ABOUT THE AUTHORS

William Pipkin is the President of ATRq, in Orem, Utah.

Rojin Belganeh is theTechnical Director of Frontier
Americas, in Houston, Texas.
William Robberson is the founderand Principal Engineer at Ocean P3 Systems, in San Francisco, California, and aformer engineer for the U.S. Environmental Protection Agency (USEPA), Region
9, in San Francisco, California. Harry L.
Allen is an environmental scientist withEPA Region 9 Superfund Division and isbased in Los Angeles, California. AnnaMarie Cook is Co-founder & CEO of
Kamilo, Inc. in Oakland, California, anda former engineer for USEPA Region 9.
Atsushi Watanabe is the Vice President and Manager of Global ProductMarketing at Frontier Laboratories, inKoriyama, Japan. Direct correspondence to: william.pipkin@gmail.com