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Conetal. Microplastics and Nanoplastics (2022) 2:19
https://doi.org/10.1186/s43591-022-00037-z
RESEARCH
Risk characterization ofmicroplastics inSan
Francisco Bay, California
Scott Coffin1*, Stephen B. Weisberg2, Chelsea Rochman3, Merel Kooi4 and Albert A. Koelmans4
Abstract
Assessing microplastics risk to aquatic ecosystems has been limited by lack of holistic exposure data and poor under-
standing of biological response thresholds. Here we take advantage of two recent advances, a toxicological meta-
analysis that produced biotic response thresholds and a method to quantitatively correct exposure data for sampling
methodology biases, to assess microplastic exposure risk in San Francisco Bay, California, USA. Using compartment-
specific particle size abundance data, we rescaled empirical surface water monitoring data obtained from manta
trawls (> 333 μm) to a broader size (1 to 5000 μm) range, corrected for biases in fiber undercounting and spectro-
scopic subsampling, and assessed the introduced uncertainty using probabilistic methods. We then compared these
rescaled concentrations to four risk thresholds developed to inform risk management for California for each of two
effect categories/mechanisms - tissue translocation-mediated effects and food dilution - each aligned to ecologically
relevant dose metrics of surface area and volume, respectively. More than three-quarters of samples exceeded the
most conservative food dilution threshold, which rose to 85% when considering just the Central Bay. Within the Cen-
tral Bay, 38% of the samples exceeded a higher threshold associated with management planning, which was statisti-
cally significant at the 95% confidence interval. For tissue translocation-mediated effects, no samples exceeded any
threshold with statistical significance. The risk associated with food dilution is higher than that found in other systems,
which likely reflects this study having been conducted for an enclosed water body. A sensitivity analysis indicated that
the largest contributor to assessment variability was associated with estimation of ambient concentration exposure
due to correcting for fiber undercounting. Even after compensating for biases associated with fibers and other small
particles, concentrations from the trawl samples were still significantly lower than the 1-L grab samples taken at the
same time, suggesting our SFB risk estimates are an underestimate. We chose to rely on the trawl data because the
1-L grab sample volume was too small to provide accurate spatial representation, but future risk characterization stud-
ies would be improved by using in-line filtration pumps that sample larger volumes while capturing a fuller range of
particle size than a towed net.
Keywords: Microplastics, Environmental risk assessment, Probability density functions, Marine ecosystems, Estuarine
ecosystems, Management framework
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Introduction
Microplastics have been found in a wide array of aquatic
environments, from pristine mountain streams to the
Arctic [23] to deep undersea habitats [1]. Toxicological
studies have determined microplastics can cause adverse
effects, such as tissue inflammation [50], impaired growth
[71], feeding disruption [62], developmental anomalies
[21], and changes in gene expression [69]. However, the
prevalence of those biotic effects in natural aquatic eco-
systems is not well understood [24].
Quantifying the risk of microplastics in aquatic eco-
systems is challenging for two reasons. First, the con-
centrations at which those effects manifest in biota are
not well understood. at uncertainty arises because of
Open Access
Microplastics and
Nanoplastics
*Correspondence: scott.coffin@waterboards.ca.gov
1 California State Water Resources Control Board, Sacramento, California, USA
Full list of author information is available at the end of the article
Page 2 of 18
Conetal. Microplastics and Nanoplastics (2022) 2:19
shortcomings in existing toxicological studies [16], with
fewer than half of the studies conducted to date hav-
ing included more than two exposure concentrations
and many of those exposures at extreme concentrations
well beyond what is typically encountered in the natu-
ral environment [9]. Although this testing provides use-
ful insights into potential effects and mechanisms of
toxicity, testing at multiple relevant concentrations to
establish a dose-response relationship is necessary to
quantitatively characterize risk. Exacerbating this prob-
lem is that microplastics have a diversity of properties,
such as size, shape, and polymer type, that can affect tox-
icity, and few studies have quantified the relative impor-
tance of these factors [8]. Most (72% out of 163) toxicity
studies have been conducted using single-sized beads of
a single polymer type [42] which is a poor representation
of mixtures encountered in the ambient environment
[54].
e second challenge is a lack of holistic exposure data
to compare directly to toxicologically derived response
thresholds. Most ambient microplastics data are collected
by towing ~ 330 μm mesh nets, which underestimates
the abundance of microplastics smaller than the mesh
size [7]. Studies that have sampled from the environ-
ment and report broader size distributions find that the
smaller sized particles are exponentially more abundant
[13, 37], suggesting the need for sampling regimes and/
or estimation methods that capture a more complete size
range of particles. Additionally, field monitoring particle
data often suffers from unquantified biases due to self-
contamination [55], difficulties associated with sampling
and analyzing fibers [43], spectroscopic interferences and
library mischaracterizations [14, 65], spatial and temporal
heterogeneity [32], as well as spectroscopy subsampling
regimes performed to ensure feasibility when particles are
counted manually [70].
Here we take advantage of two recent advances that
address these challenges. e first is a meta-analysis
in which a broad array of toxicological studies were
incorporated into a single risk assessment framework
[42], which produced thresholds for a range of biotic
responses and recommended management actions. is
meta-analysis applied critical quality criteria to screen
reliable toxicity studies and integrated the results into a
combined outcome that transcended shortcomings of
the underlying individual studies. e second is the use
of probability density functions (PDFs) to quantitatively
correct exposure data for biases due to sampling meth-
odologies [33, 36]. Size abundance microplastic particle
data can be used to derive probability density functions
(PDFs) that allow the rescaling of field monitoring data
restricted to a given size range (e.g., > 330 μm) to a more
holistic size range (e.g., 1 to 5000 μm), enabling direct
comparison to toxicity thresholds from laboratory stud-
ies aligned to the same size range [34].
Combining these two advances, we assess the risk to
aquatic ecosystems from microplastic exposure in San
Francisco Bay, California (SFB) where a comprehensive
study of ambient exposure was conducted [70] (Fig.1).
After rescaling to a common size distribution, we com-
pare the monitoring data to aligned risk and management
thresholds from Mehinto etal. [42] to estimate the likeli-
hood and pervasiveness of a local biological response.
Using PDFs and Monte-Carlo modeling we quantify the
uncertainty of the rescaling methods to determine where
the greatest uncertainties in this risk characterization lie
pointing to the science advancements needed to improve
risk assessments in the future.
Materials andmethods
Data quality
Crucial to assessing risks is selecting data fit for that
assessment purpose. Surface water monitoring data for
microplastics in SFB, California, USA reported by Zhu
etal. [70] were quantitatively assessed for quality accord-
ing to the criteria defined for water sampling in Koelmans
etal. [32]. Data reported for other matrices (e.g., storm-
water effluent, fish tissue, sediment) were not scored due
to a lack of established quality criteria for such matri-
ces. Briefly, nine criteria which relate to the reproduc-
ibility and reliability of aqueous microplastics sampling,
contamination mitigation, sample processing/handling,
and chemical analysis were applied. For each criterion, a
score of 0, 1, or 2 was applied and a total accumulated
score was calculated by adding scores for individual cri-
teria (maximum 18 points). Samples that received a ‘zero’
value for any individual score were not considered suffi-
ciently reliable [25].
Microplastics characterization
Blank-corrected environmental microdebris occurrence
data from SFB and outside of SFB, in National Marine
Sanctuaries, were obtained from Zhu et al. [70]. Sam-
pling details can be found in [70]. In short, sampling sites
were picked to represent each region within SFB. Regions
are characterized by differences in population sizes and
point sources upstream, such as wastewater and storm-
water. Surface water concentrations were spectroscopy
corrected. Spectroscopy was only performed on a sub-
set of particles, i.e., roughly 10% of each morphology
within each size fraction (see [70] for more detail). is
methodology was chosen in an attempt to be more rep-
resentative. is led to spectroscopy conducted on 23%
of all particles from surface manta trawls. e system-
atic removal of all fiber particle counts from manta trawl
data in Zhu etal. [70] was corrected for using a subset
Page 3 of 18
Conetal. Microplastics and Nanoplastics (2022) 2:19
of manta samples in which all fibers were counted and
reported in Hung etal. [27]. While Zhu etal. [70] pre-
sented a novel method to correct manta trawl samples
for size, a different size rescaling method was used here
(i.e., [33]). Blank-, fiber-, and plastic polymer-corrected
particle concentrations were rescaled to a common size
distribution (1 to 5000 μm) to allow direct comparison to
hazard thresholds [42] according to the methods in Koe-
lmans etal. [33] using marine surface water size distribu-
tion data from Kooi etal. [37]. Additionally, a statistically
significant outlier was identified based on four-times the
mean Cook’s distance and was removed from the Zhu
etal. [70] dataset, which was a sample collected from a
tidal front and was highly contaminated with micro-
plastics and other debris (sample identification: CB9-
Manta-11 Jan 18).
Due to time-constraints of spectroscopically confirm-
ing the polymer identity of all particles in samples, Zhu
etal. [70] subsampled particles from samples based on
the number of particles of a particular morphology and
size class within a given sample. For each site in the SFB
and compartment (i.e., stormwater, wastewater, fish tis-
sue, sediment, surface water) the proportion of particles
that were spectroscopically determined to be a specific
polymer (e.g., polyester, polyethylene, etc.) were divided
by the total number of particles spectroscopically char-
acterized for that compartment-site combination (Fig.1).
Zhu etal. [70] reported that interferences such as dyes
and carbon black prevented the spectroscopic confirma-
tion of all particles and reported some polymers using
suspected terms such as “anthropogenic (synthetic)” or
“anthropogenic (unknown base)”, etc. To be conserva-
tive, particles that could not be polymerically confirmed
were excluded from the proportion of microdebris parti-
cles considered to be plastic, with a hierarchical schema
developed here and employed to classify particle types
Fig. 1 Flowchart of general steps involved in microplastics risk characterization as employed in this study. Data obtained/derived from respective
studies are annotated using colors; Zhu et al. [70] is green, Mehinto et al. [42] is red, and this study is blue
Page 4 of 18
Conetal. Microplastics and Nanoplastics (2022) 2:19
(Fig. S1). To determine if a single polymer correction
factor should be applied to all matrices or should be per-
formed separately for each matrix, a one-way analysis of
variance (ANOVA) was conducted (Table S3). en, for
surface water data obtained using manta trawl, a one-
way ANOVA was conducted to determine if there are
site-specific differences in plastic proportions of micro-
debris particles, with site-specific correction factors
being applied only if the ANOVA determined statisti-
cally significant differences. Proportions of microdebris
confirmed to be plastic values were multiplied by the
total number of microdebris particles reported by Zhu
etal. [70] for each compartment to obtain microplastic
occurrence data in a probabilistic manner as part of the
Monte-Carlo modeling method (described below).
Due to the mesh size of a manta trawl net (333 μm),
fibers are known to pass through the sampling appara-
tus, leading to a systematic shape-based undercounting
bias. Zhu etal. [70] did not include fibers in total particle
counts in their blank-corrected concentrations for sam-
ples collected using manta trawls to remove that uncer-
tainty with the impact of systematically undercounting
microplastic particles. Hung etal. [27] reported particle
count data for all shapes - including fibers - in 9 manta
trawl samples taken from various sites across the SFB and
Marine Sanctuaries. is manta trawl fiber subsampling
dataset was used to derive a correction factor to estimate
the amount of microplastics that would be present in the
other manta trawl samples from SFB if plastic fibers had
been counted in those samples, with uncertainties propa-
gated probabilistically using the Monte-Carlo method
described below. For each manta trawl sample with fiber
counts, the proportion of particles that were fibers was
calculated and a fiber correction factor was derived as the
inverse of one minus the median fraction of particles that
were fibers. Due to the small sample size, site-specific dif-
ferences in fiber proportions were not considered.
Rescaling ofenvironmental concentrations
Environmental microdebris occurrence data from SFB
reported in Zhu etal. [70] included various size ranges
of particles based on each sampling technique (e.g.,
> 333 μm for manta trawl; > 50 μm for grab samples: see
Table S8) which were rescaled to a common size distri-
bution of 1-5000 μm to compare to ecotoxicity thresholds
aligned to the same size distribution in Mehinto et al.
[42] based on the methods described in Koelmans etal.
[33, 34]. Environmental concentrations were multiplied
by a correction factor derived for each sampling tech-
nique based on their particle size limits (Eq.1) [33].
(1)
Cenv
=
CF meas
∗
Cmeas
In Eq.1, Cenv is the environmentally realistic occurrence
concentration in particles · L− 1 (adjusted for non-align-
ment of mesh sizes), CFmeas is a dimensionless correction
factor for the environmentally monitored concentration
(meas); and Cmeas is the measured environmental concen-
tration, expressed in particles · L− 1 [33]. Environmental
concentrations are rescaled to an upper (UL,D; μm) and
lower default size range (LL,D; μm) (here 5000 and 1 μm
respectively), using the power law slope of microplastic
particle abundance in the environment based on size (a,
unitless), with the upper limit (UL, meas; μm) and lower
limit (LL, meas; μm) defined by the size limits of quanti-
fication of the monitoring method employed (Eq.2) [33].
Kooi et al. [37] derived power slope exponents (a)
based on size for freshwater and marine environments
across several locations in Europe using individual parti-
cle datasets obtained using state-of-the-science Fourier-
transform infrared imaging coupled with a focal-plane
array detector and automated image analysis. When
measured particles length data was pooled across all
samples for each compartment across distinct locations
(e.g., Rhine and Dommel rivers), power law exponent val-
ues contained low variability within each compartment,
but were significantly different between compartments
(e.g., a= 2.64 ± 0.01 for marine and a= 2.07 ± 0.03 for
freshwater surface waters), implying microplastic size
relationships are highly conserved within compartments
[37]. While within-compartment particle size distribu-
tions are not expected to deviate significantly across
regions, site-specific data would be preferable to rescale
environmental concentrations so long as the data is high
resolution and is reliable [37]. Lacking site-specific high-
resolution particle size distribution data, compartment-
specific a values for length were used from Kooi etal. [37]
to rescale environmental concentrations in SFB (Table
S7). To account for within-compartment variability in
rescaling environmental concentrations, uncertainties
in the derived a value were propagated probabilistically
using Monte-Carlo methods described below.
Zhu etal. [70] reported particle length data for SFB for
all compartments (except surface water obtained using
1-L grab samples) which was measured manually and was
not intended to provide high-resolution information on
particle size distributions. Nonetheless, this dataset was
used to derive compartment-specific size a values accord-
ing to the methods described in Kooi & Koelmans [36]
as part of a sensitivity analysis only and were not used to
rescale concentrations for risk characterization purposes.
Briefly, all particle monitoring data from SFB reported on
(2)
CF
meas =
L
1
−
a
UL,D−L
1
−
a
LL,D
L1−a
UL,meas −L1−a
LLmeas
Page 5 of 18
Conetal. Microplastics and Nanoplastics (2022) 2:19
an online open data repository managed by the Califor-
nia Natural Resources Agency (https:// data. cnra. ca. gov/
datas et/ micro plast ic- sf- bay) were aggregated, and only
the measured lengths of individual particles spectro-
scopically confirmed to be plastic were used in estimat-
ing size distributions. Plastic particle data were grouped
by compartment (i.e., surface water, sediment, fish tissue,
wastewater effluent, stormwater runoff), and abundance
particle length-based data were plotted on a log-log scale
with relative abundance on the y-axis and particle size on
the x-axis and a linear trendline fit to the data was used
to derive the a exponent value. Since power laws usually
only apply to values greater than some minimum value
(in this case, particle length), both the minimum appli-
cable value and the final a value were determined using
a maximum likelihood estimation method [10, 48] using
the poweRlaw package [22], and bootstrap 100 times. As
part of the sensitivity analysis only, the a value derived
using manta trawl data (> 333 μm) was used to rescale the
manta trawl-derived surface water monitoring data.
Risk characterization
Aquatic ecotoxicological thresholds were used to char-
acterize risk by deriving the ratio of predicted no-effect
concentration (PNEC) thresholds to predicted environ-
mental concentrations (PEC), with exceedances greater
than one indicative of risk. While data were available for
microplastics concentrations in various matrices (e.g.,
surface water, stormwater, wastewater effluent, sediment,
fish tissue) in the SFB, only surface water concentration
data were used for risk characterization as direct com-
parisons of undiluted stormwater or wastewater are not
representative of environmental exposures, and due to
the lack of hazard thresholds for marine sediment or fish
tissue.
Surface water concentration data were compared to
ecotoxicological thresholds derived by Mehinto etal. [42]
using species sensitivity distributions (SSDs) based on
chronic no-observed-adverse-effect concentrations for
14-16 freshwater and marine species from 6 to 7 taxo-
nomic groups. Prior to derivation, Mehinto et al. [42]
screened 162 peer-reviewed laboratory toxicity stud-
ies for a set of pre-defined quality criteria based on the
standards defined by de Ruijter etal. [16]. A total of 290
threshold data points were extracted from 21 studies that
met the minimum pre-defined criteria. ese thresholds
were aligned to a common size distribution of 1-5000 μm
using environmental PDFs and based on mechanisms of
action as described in Koelmans etal. [32] and Kooi etal.
[36]. Following Kooi etal. [36], Mehinto etal. [42] derived
thresholds for two effect mechanisms/pathways – food
dilution and tissue translocation, which were aligned by
volume and surface area ecologically relevant metrics,
respectively. e food dilution-based effect considered
particles small enough to be ingested by the organism
of interest to be accessible (i.e., exclude non-accessible
particles), then aligned (both monodisperse and polydis-
perse) laboratory effect concentrations to environmen-
tally realistic concentrations based on particle volume
[42]. e tissue translocation mechanism of action con-
sidered particles wide enough to translocate across tis-
sues (83 μm) following ingestion to be accessible and
aligned laboratory effect concentrations to environmen-
tally realistic concentrations based on translocatable sur-
face area [42]. Although the methodology used to align
thresholds was identical for the mechanism of the food
dilution effect, additional studies were used in Mehinto
etal. [42] compared to Koelmans etal. [32], which only
used effect thresholds data for studies in which the
authors confirmed that a food dilution mechanism was
demonstrated or was plausible. For both effect mecha-
nisms/pathways, four PNEC thresholds were derived
which correspond to different levels of confidence that
microplastics can cause adverse effects to aquatic organ-
isms and call for varying levels of management action
- ranging from increasing monitoring to implementing
Source control measures (Table S9) [42]. At the time of
writing, the risk management actions associated with
thresholds from Mehinto etal. [42] carry no regulatory
or legal authority in California or any other jurisdiction
and are only suggestions.
Sensitivity analysis
Uncertainties were evaluated probabilistically using
Monte Carlo methods based on PDFs derived for each
correction factor, including: manta trawl fiber correc-
tion (shape under-counting bias), plastic fraction of total
microdebris particles (spectroscopic subsampling vari-
ability), and rescaling concentrations to a common size
range (a variability) (Fig.1). To obtain the combined cor-
rection factor with probabilistic propagation of uncer-
tainties, a data frame of 10,000 values was generated for
each correction factor based on their modeled distribu-
tion, and each of these three data frames was multiplied
by one another row-wise. e 50th percentile value from
this combined correction factor distribution was used to
correct the manta trawl surface water monitoring data,
and the 5th and 95th percentile values were used to cal-
culate uncertainty. is methodology accurately accounts
for the underlying distributions of the correction factor
data and is preferable to error propagation techniques
that rely on assumptions of normality.
For each correction factor, a theoretical distribution
was fit to the data based on the shape of the underlying
distribution (see Table S10) - which was evaluated visu-
ally using Cullen and Frey graphs (Fig. S8) and using a
Page 6 of 18
Conetal. Microplastics and Nanoplastics (2022) 2:19
maximum likelihood estimation approach with the fit-
distrplus R package [17]. Due to the highly skewed distri-
bution in the manta trawl fiber correction data (Fig. S6),
these data were log-transformed prior to derivation of a
PDF, then back-transformed into linear space following
the Monte Carlo simulation. Following log-transforma-
tion, fiber correction data (unitless; > 1) were well-fit by
a normal distribution (Fig. S8a). Plastic proportion data
(unitless; 0 - 1) were well-fit by a two-shape beta distribu-
tion (Fig. S8b) that was truncated such that values greater
than one was not produced during the Monte Carlo sim-
ulated due to their theoretical implausibility (i.e., more
than 100% of particles cannot be plastic). Because the
size-based a values used to rescale concentrations (i.e.,
from Kooi etal. 2022) were derived using maximum like-
lihood estimation based on a log-log linear distribution
(Fig. S7; Table S7), a normal distribution was assumed,
and the PDF was approximated using a normal distribu-
tion based on the mean and standard deviation of the lin-
ear regression slope (Table S7). Correction factors were
derived from the Monte-Carlo simulated distribution of
alpha values using Eq.2.
To quantify and compare the relative sensitivity of cor-
rection and rescaling factors applied here on the result-
ing exposure assessment, variability for each parameter
was held constant while uncertainty in the other variables
was calculated. Finally, to assess the relative uncertainty
between rescaled and corrected environmental occur-
rences with modeled risk thresholds, the 95th percentile
of the Monte Carlo simulated occurrence data water was
compared to the 95% confidence intervals for microplas-
tics hazard thresholds reported in Mehinto et al. [42]
based on the SSD model. An additional sensitivity analy-
sis was performed using site-specific a values derived for
SFB using manta trawl particle length data as described
above.
Statistics
All statistical analyses were conducted in R (version 4.1.1;
R Core Team, [53]) and figures were produced using the
package ggplot2 [66]. Base maps sourced from Google
were used for mapping using the ggmap package [30].
One-way ANOVAs were used to determine if plastic cor-
rection factors should be separated for each matrix and
site (for manta trawl only). To determine if bias correc-
tions (i.e., rescaling, fiber correction, plastic correction)
resulted in comparable concentrations between water
matrices, one-way ANOVAs were run for both raw and
rescaled/corrected concentrations. For all hypothesis
tests, statistical significance was determined at an alpha
level of 0.05, and multiple comparisons were performed
using Tukey’s Honest Significant Difference post-hoc
test, when applicable. All Monte-Carlo simulations were
performed with 10,000 iterations with a seed set for
reproducibility.
Results
Data quality
Microplastics monitoring data reported in Zhu et al.
[70] received total accumulated scores of 13, 10, and 14
for manta trawl, grab samples, and wastewater treat-
ment plant effluent samples respectively according to
criteria defined in Koelmans etal. [32] (Table S1). While
manta trawl and wastewater treatment plant effluent data
received a score of at least one for each quality criteria,
grab samples received “zero” scores for several criteria
(sample size and sample treatment) (Table S1). Accord-
ingly, manta trawl and wastewater treatment plant efflu-
ent data from Zhu etal. [70] are considered sufficiently
reliable for the purposes of risk characterization, while
the grab sample data are not, however only manta trawl
data (surface water) were used for risk characteriza-
tion due to the non-applicability of wastewater data
for estimating exposure. Because blank-corrections
were applied based on color-morphology combinations
instead of polymer identification, all matrices received a
score of “1” for negative controls instead of “2”. e blank
correction procedure applied in Zhu etal. [70] may lead
to an underestimation of concentrations if microplastic
particles of the same color have a different polymer iden-
tity - an uncertainty which is not accounted for in this
probabilistic assessment. e grab, wastewater treatment
plant effluent, and manta trawl data scores were higher
than the average score for surface waters (7.9; range 4 to
15; n= 55) reported in Koelmans etal. [32]. While addi-
tional quality criteria are available for biota and sedi-
ment (Bäuerlein PS, Erich MW, van Loon W, Bakker I,
Mintenig SM, Koelmans AA: A monitoring and data
analysis method for microplastics in marine sediments
for OSPAR and MSFD, Submitted) (Redondo- Hasseler-
harm Paula Elisa, AR, Koelmans, AA: Risk assessment of
microplastics for freshwater benthic ecosystems guided
by strict quality criteria and data alignment methods,
Submitted) [25], risk thresholds are unavailable for these
compartments and these data were not quality scored
here. Additionally, stormwater was not scored due to a
lack of established quality criteria.
Microplastics characterization
e percentage of analyzed particles spectroscopi-
cally determined to be plastic was significantly differ-
ent between matrices according to a one-way ANOVA
(p <1 × 10− 16; Table S3), so matrix-specific plastic cor-
rection factors were derived accordingly using PDFs
(Figs. S3 and S9; Tables S2 - S4; Table S10). Tukey’s
Page 7 of 18
Conetal. Microplastics and Nanoplastics (2022) 2:19
Table 1 Summary of correction factors applied probabilistically to samples from SFB
a Concentrations were rescaled to 1 to 5000 μm
b Exact mesh size values as reported in Zhu etal. [70] were used to rescale data
c Values from Kooi etal. [37] and derived from various locations in Europe. Alpha values for stormwater have not been reported elsewhere in the literature. Alpha value reported for biota in Kooi etal. [37] are primarily
polychaetes
d Derived using site-specic data for San Francisco Bay obtained from Zhu etal. [70]
e Stormwater particle-level data were not reported in CEDEN and could not be obtained to perform this analysis
f Fibers were only systematically removed from manta trawl samples, so ber correction factors are not applicable for other sampling apparatuses
Sampling Apparatus Matrix Mesh Size
(μm)aAlpha (unitless; median, 5th to
95th percentiles) CFb (unitless;
median, 5th to 95th
percentiles)
Proportion of
Particles Conrmed
Plastic (unitless;
median, 5th to 95th
percentiles; probability
distribution)
Fiber Correction
Factor (unitless;
median, 5th to 95th
percentiles; probabilty
distribution)
Combined
Correction Factors
(unitless; median,
5th to 95th
percentiles)
1-L grab Surface water 50 2.07 (2.02 to 2.12)c
2.15 (1.36 to 2.93)d66 (55 to 80)c
90 (5 to 1988)d0.40 (0.19 to 0.72)
beta: shape 1 = 6.03,
shape 2 = 137
NAf26 (12 to 48)c
35 (1.7 to 823)d
24-hr pipe Stormwater 106 2.97 (1.62 to 4.35)d9361 (20 to 6,120,045)cNAeNAf9361 (20 to
6,120,045)c
Composite WWTP effluent 110 2.54 (2.52 to 2.56)c
2.41 (2.18 to 2.64)d1396 (1292 to 1507)
762 (256 to 2290)d0.27 (0.09 to 0.64)
beta: shape 1 = 3.00,
Shape 2 = 93.5
NAf384 (119 to 888)c
202 (44 to 825)d
Manta Trawl Surface water 333 2.07 (2.02 to 2.12)c
2.15 (1.36 to 2.93)d529 (401 to 704)c
797 (12 to 73,349)d0.72 ± 0.24 (65)
beta: shape 1 = 5.81,
shape 2 = 73.7
9 (1 to 52)
Normal (log10-trans-
formed): mean = 0.965,
stdev = 0.529
2819 (336 to
17,966)c
4103 (41 to
554,392)d
Otter trawl/cast net Biotac / fish tissued25 2.59 (2.52 to 2.66)c
2.26 (1.92 to 2.60)d167 (135 to 2066)
58 (20 to 1750)d0.23 (0.09 to 0.46)
beta: shape 1 = 4.20,
shape 1 = 169)
NAf38 (14 to 80)
13 (3 to 49) d
Van Veen Grab Sediment 45 2.57 (2.24 to 2.90)c
2.90 (2.22 to 3.59)d394 (113 to 1398)c
1342 (103 to 18,861)d0.34 (0.16 to 0.69)
log-normal: mean-
log = −3.39, sdlog = 0.44
NAf133 (32 to 553)c
454 (31 to 6860)d
Page 8 of 18
Conetal. Microplastics and Nanoplastics (2022) 2:19
post-hoc test for significance revealed significant differ-
ences in plastic proportions of total particles between
manta trawl and sediment, fish tissue, wastewater treat-
ment plant (WWTP) effluent, and surface water col-
lected with 1-L grab (Table1 and Table S4). Surface water
samples collected with manta trawl contained the highest
percentages of confirmed microplastics (72% ± 24%), fol-
lowed by surface water collected by 1-L grab (42% ±24),
sediment (37% ± 14%), wastewater treatment plant efflu-
ent (31% ± 18%), and fish tissue (24% ±12%) (Table 1,
Table S2; Fig. S2). Additional significant differences
were found between sediment and fish tissue; sediment
and 1-L grab surface water; and fish tissue and 1-L grab
surface water (Table1 and Table S4). As surface water
data obtained using manta trawl were the only monitor-
ing data used for risk characterization here, site-specific
differences were tested using a one-way ANOVA, which
demonstrated no significant difference in proportions of
plastic particles relative to all spectroscopically charac-
terized particles by location (p =0.12; Table S5; Figs. S3
– S4). Accordingly, a single correction factor for plastic
percentages was applied to all manta trawl data regard-
less of location, which was the median value of 0.63 (0.31
to 0.95: 95% CI) that was derived from a two-shape beta
distribution PDF (Table1 and Table S10; Fig. S9).
To correct for the systematic removal of fiber parti-
cle data from the blank-corrected dataset reported in
Zhu etal. [70], data were used from Hung etal. [27]
for 9 manta trawl samples from SFB in which all fib-
ers were counted. On average, fibers constituted 78%
(± 28% sd) of particles in the manta trawl samples in
which they were counted (Table1 and Table S6). Other
aqueous matrices and sediment contained lower per-
centages of fibers, while fish tissue contained a higher
percentage (Table1 and Table S6, Fig. S5). Based on the
PDF of the fiber proportions, a fiber correction factor
of 8.87 (95% CI: 1.29 to 50.89; Table1 and Table S10)
was calculated and applied to manta trawl monitoring
data as part of the Monte-Carlo analysis. Due to the
relatively small sample size (n = 9) and skewed nature
of the fiber proportion distribution (Fig. S6), the fiber
correction factor contains relatively high uncertainty
compared to the plastic spectroscopy correction factor
and size rescaling correction factor (Fig. S13).
Rescaled environmental occurrence data
Size abundance distributions of microplastics in SFB
were fit by linear regression on a log(10)-log(10) scale
using a maximum likelihood estimation approach [37],
with a exponent values ranging from 2.15 to 3.02 (Fig.
S7, Table S7). Length-based power law exponent values
(a) derived for microplastics in SFB were comparable to
values derived from various locations in Europe reported
by Kooi etal. [37] (Table1 and Table S7). Notably, the
a values for marine surface waters were 2.15 ± 0.48 and
2.07 ± 0.03 (mean ± sd) for SFB and in Europe, respec-
tively, thus representing less than a 5% difference and
are not statistically significant from one another (Table1
and Table S7). Additionally, a law exponents followed the
same rank order by matrix between Kooi etal. [37] and
those derived here for comparable matrices (i.e., marine
surface water < wastewater effluent < marine sediment).
e greatest difference between a law exponents was
for marine sediment (2.90 ± 0.41 and 2.57 ± 0.20 for SFB
and Europe, respectively) (Table1 and Table S7). Direct
comparisons for power law exponents derived for SFB to
other studies/locations were not possible for stormwater
runoff (which has not been reported elsewhere) or fish
tissue (“biota” reported in [37] corresponds to benthic
invertebrates).
Size-based correction factors for matrices ranged
from 58 to 9774 depending on matrix (a value) and
mesh size (Table1 and Table S8). Fish tissue data had
the smallest mesh size (25 μm) and had the smallest cor-
rection factor accordingly (58; 95% CI: 53 to 63) (Table1
and Table S8). Manta trawl data had the largest mesh
size (333 μm) and had the second largest correction fac-
tor (529; 95% CI: 401 to 704 based an a value of 2.07
from [37]) (Table1 and Table S8). Stormwater data had
a smaller mesh size than manta trawl (106 μm), however
the correction factor was over 10x higher (9361; 95%
CI: 20 to 6,120,045) due to the high a value (2.97 ± 0.83)
(Table1 and Table S8).
In theory, rescaling data and correcting for systematic
biases (i.e., fiber correction, spectroscopic subsampling)
should reduce differences in monitoring concentrations
taken at similar times and locations within a given matrix
due to size-differences in mesh sizes of sampling apparatus
[33]. Before rescaling and correcting, surface water con-
centrations collected using manta trawl as well as effluent
from wastewater treatment plants were not significantly
different from one another according to one-way ANOVA
with Tukey’s post-hoc (p > 0.05; Tables S11 – S12) but
were both significantly lower than surface water collected
through other means (stormwater, 1-L grab surface water;
p< 0.001) (Table S12). Additionally, 1-L grab surface water
concentrations were significantly higher than stormwater
concentrations collected with a depth-integrated peristal-
tic pump (p= 0.03; Table S12). Following rescaling and
correcting, manta trawl-collected surface water concentra-
tions were still significantly lower than other surface water
concentrations collected via other methods (i.e., stormwa-
ter, 1-L grab surface water) (p< 0.001; Tables S13 – S14),
however wastewater concentrations were no longer signif-
icantly different from both 1-L grab and manta trawl-col-
lected surface water concentrations (p >0.05) (Fig.2; Table
Page 9 of 18
Conetal. Microplastics and Nanoplastics (2022) 2:19
S14). Despite the combined correction factor to account
for systematic under-counting based on size and fibers as
well as fractions of particle spectroscopically confirmed
to be plastics, rescaled and corrected manta trawl surface
water data were still significantly lower (p =3.4 × 10− 14;
Table S14) than rescaled surface water 1-L grab samples,
of which the majority were taken at similar locations and
times. ese results suggest that additional systematic
biases are present in either the manta trawl (likely under-
counting) or the 1-L grab samples (potentially overcount-
ing). Undercounting in manta trawl samples may be due in
part or in whole to imprecise blank corrections based on
shape-color combinations as opposed to polymer-based
corrections.
Risk characterization
Depending on the postulated effect mechanism/path-
way (i.e., food-dilution or tissue translocation), risk
exceedances of microplastics in SFB vary significantly.
For all comparisons of PNECs (i.e., hazard thresholds
from Mehinto etal. [42]) with PECs (i.e., corrected, and
rescaled surface water concentrations in SFB) stated
throughout this manuscript, only those in which the 95%
CI does not include ‘0%’ represent statistically significant
exceedances. Accordingly, only food-dilution thresh-
olds one, two, and three have statistically significant
exceedances in the SFB, while all other thresholds (i.e.,
food-dilution threshold four, and all tissue translocation
thresholds) do not.
Fig. 2 Unadjusted (blue) and rescaled (1 to 5000 μm; red) aqueous microplastics concentrations in SFB for A) aquatic matrices, B) sediment,
and C) fish. Data are presented as box and whisker plots, with the center lines representing the median values (50th percentiles), while the box
contains the 25th to 75th percentiles. The whiskers mark the 5th and 95th percentiles, and values beyond those upper and lower bounds are
considered outliers, marked with dots. All monitoring data were rescaled for size using matrix-specific PDFs derived for SFB and were corrected for
plastic proportions due to spectroscopic subsampling. Manta trawl data were further corrected to account for systematic removal of fibers from
blank-corrected data in Zhu et al. [70]. For each matrix, sampling apparatus are defined in parentheses
Page 10 of 18
Conetal. Microplastics and Nanoplastics (2022) 2:19
Comparison of corrected and rescaled manta-trawl
collected surface water samples with food-dilution
thresholds derived by Mehinto etal. [42] resulted in 82%
(95% CI: 27% to 100%) of samples exceeding the most
conservative risk threshold (i.e. “Investigative monitor-
ing” threshold one), 27% (95% CI: 3% to 73%) of samples
exceeding threshold two (“Discharge monitoring”), 21%
(95% CI: 3% to 58%) of samples exceeding threshold three
(“Management planning”), and 3% (95% CI: 0% to 18%) of
samples exceeding threshold four (“Source control meas-
ures”) (Fig.3;Table S15).
Comparison of surface water samples with tissue trans-
location-based thresholds derived by Mehinto etal. [42]
resulted in 3% (95% CI: 0% to 9%) of samples exceeding
the most conservative risk threshold (i.e., “Investiga-
tive monitoring” threshold one), 0% (95% CI: 0 to 3%) of
samples exceeding threshold two (“Discharge monitor-
ing”), 0% (95% CI: 0 to 3%) of samples exceeding thresh-
old three (“Management planning”), and 0% (95% CI: 0 to
0%) of samples exceeding threshold four (“Source control
measures”) (Table S15).
Risk exceedances were higher during the rainy season,
with 94% (95% CI: 41% to 100%) of surface water samples
collected following a storm event exceeding food dilution
threshold one compared with 71% (95% CI: 12% to 100%)
of samples collected during the dry season (Fig. S10).
Rainy season samples exceeded food dilution threshold
three within confidence limits (29%; 95% CI: 6% to 71%),
Fig. 3 Comparison of corrected and rescaled surface water concentrations of microplastics in SFB collected using manta trawl with food-dilution
threshold derived by Mehinto et al. [42]. A percentages of samples exceeding each threshold are shown as bar plots, with solid-line error bars
reflecting the 25th and 75th percentiles and dashed-line error bars reflecting the 95th percentile confidence intervals derived using Monte
Carlo simulations (n = 10,000) based on probability density functions derived from the combined variability of correction factors and rescaling. B
Empirical cumulative density plot of surface water concentrations and 25th and 95th percentile confidence intervals of correction factors compared
to food dilution thresholds. Exceedances are only considered statistically significant when the 95% confidence interval does not include ‘0%’
Page 11 of 18
Conetal. Microplastics and Nanoplastics (2022) 2:19
however dry season samples did not (12%; 95% CI: 0% to
47%) (Fig. S10).
Risk exceedances varied by location within the SFB
(Fig. 4, Table S16). The Central Bay had the high-
est proportion of samples exceeding risk thresholds,
with 85% (95% CI: 38% to 100%) exceeding Mehinto
etal. [42]‘s most conservative food dilution thresh-
old one (“Investigate monitoring”), 38% (95% CI:
8% to 85%) exceeding food-dilution threshold two
(“Discharge monitoring”), 38% (95% CI: 8% to 77%)
exceeding threshold three (“Management planning”),
and 8% exceeding food dilution threshold four
(“Source control measures”), however exceedances of
threshold four were not statistically significant (95%
CI: 0 to 31%) (Table S16). Additionally, the Central
Bay was the only location with any samples exceed-
ing a tissue translocation-based threshold at the 50th
percentile, with 8% exceeding threshold one – how-
ever these exceedances were not statistically signifi-
cant (95% CI: 0 to 23%) (Table S16).
Fig. 4 Map of San Francisco Bay showing food dilution threshold risk exceedances based on corrected and rescaled surface water concentrations
of microplastics collected using manta trawl. Points represent approximate coordinates of manta trawl sampling locations. Colors represent risk in
relation to food dilution thresholds in Mehinto et al. ([42]; re-produced in Table S9). Greater risk can be seen within SFB and San Pablo Bay relative to
open-ocean waters outside of the SFB
Page 12 of 18
Conetal. Microplastics and Nanoplastics (2022) 2:19
Comparison of SFB samples to samples taken from
outside of the bay demonstrated substantially higher risk
within the bay. Samples taken from the National Marine
Sanctuaries - which is an open-ocean location with mini-
mal inputs from wastewater discharge or stormwater
runoff and was selected as a reference location as part of
the study design [70] did not have any samples exceed-
ing the most conservative threshold (i.e., food-dilution
threshold one) with statistical significance (i.e., 35%; 95%
CI: 0% to 91%) (Table S16; Fig.4). Of the samples in the
National Marine Sanctuaries exceeding food dilution
threshold one at 50th percentile, the three highest were
at the mouth of the bay just West of the Golden Gate
Bridge, suggesting rapid dilution of microplastic particle
concentrations outside of the SFB (Fig.4).
Sensitivity analysis
Comparison of the influence of factors in estimating
environmental occurrence from manta trawl data reveals
that the fiber correction factor contains the highest rela-
tive uncertainty compared to the spectroscopic sub-
sampling correction factor for plastics and the size-based
alignment correction factor (Fig. S13). Holding variabil-
ity for all correction/rescaling factors constant except for
the fiber correction, the 95% confidence interval for per-
centage of samples in SFB exceeding Mehinto etal. [42]‘s
food-dilution threshold one is (29% to 100%), compared
with (76% to 88%) for size rescaling, (65% to 94%) for the
plastic-proportion due to spectroscopic subsampling
correction, and (26% to 100%) for combined rescaling
and corrections (Fig. S13). If the fiber correction factor is
omitted from the analysis entirely, uncertainty decreases
substantially in the risk characterization, and the over-
all number of statistically significant risk exceedances
decreases as well (Fig. S11). If the fiber correction factor
is not applied, 27% of SFB samples would exceed food-
dilution threshold one (95% CI: 18% to 39%) compared to
82% of samples when the fiber correction is applied (95%
CI: 27% to 100%) (Fig. S11 and Table S15).
While the size distribution value (a) has a substantial
impact on the outcome of the risk characterization due
to the high correction factor values derived for manta
trawls (529; 95% CI: 401 to 704, Fig. S9), the site-specific
values for marine surface waters for SFB were of minimal
difference from those derived elsewhere and applied for
this risk characterization (i.e. [37]) and therefore had lim-
ited uncertainty in this assessment (Figs. S13 and S14).
However, larger mesh sizes correspond to exponentially
larger correction factors (Eq.2) and are therefore highly
influential in the case of manta trawl data (333 μm mesh).
For example, the correction factor for 1-L grab samples
would be 66 (95% CI: 55 to 80), using the same a value
and uncertainty applied for manta trawl here, indicating
the higher uncertainty and influence of rescaling manta
trawl data compared to grab samples.
Comparison of the total uncertainties associated with
estimating environmental surface water concentrations
with the uncertainties in risk thresholds from Mehinto
etal. [42] reveals comparable levels of uncertainty, with
food dilution thresholds spanning ~ 2 to 5 orders of mag-
nitude between 95% confidence intervals depending on
the tier (Table S10) while estimated environmental con-
centrations for manta trawl surface samples span ~ 2.5
orders of magnitude between 95th percentiles based on
the combined correction and rescaling uncertainties
(Table S11).
Discussion
Here, we combine occurrence data from SFB, California
with a risk assessment framework to estimate the risk to
local aquatic ecosystems. e risk framework includes
hazard thresholds for two ecologically relevant categories
of effect mechanisms - food dilution and mechanisms
triggered upon tissue translocation. Based on the best
available toxicological evidence and monitoring data, our
results suggest that microplastic exposure in SFB in 2017
was high enough to cause biological perturbation through
the food dilution effect mechanism. Eighty-two percent
of the SFB had concentrations that exceeded Mehinto
etal. ‘s [42] tier one food-dilution threshold (“Investiga-
tive monitoring”) with statistical significance, with the
highest percentages of statistical exceedances occurring
within the Central Bay. Furthermore, the Central Bay was
the only region within the SFB with any samples exceed-
ing the third food dilution threshold (“Management plan-
ning”) with statistical significance, however no samples
exceeded the highest food dilution threshold (need of
immediate source control measures) with statistical sig-
nificance. Because samples were not taken with the goal
of being spatially or temporally representative of the SFB
[70], additional monitoring is suggested to improve con-
fidence in risk characterizations.
Our analysis suggests that the risk associated with tis-
sue translocation-mediated effects is substantially less
than that for the food-dilution endpoint in SFB. While
there were a few samples with concentrations greater than
the first threshold at the 50th percentile, the exceedances
were not statistically significant, and there were no sam-
ples above any of the other three thresholds at the 50th
percentile. is lesser effect likely reflects tissue translo-
cation-mediated effects being initiated by the subset of
particles that are small enough (< 83 μm) to permeate the
intestinal wall [28, 42, 52], whereas food-dilution is caused
by a wider spectrum of particle sizes - based on ingestibil-
ity - that artificially fill the gut and lead to reduced food
assimilation by blocked food passage [5, 12, 39, 47]. Still,
Page 13 of 18
Conetal. Microplastics and Nanoplastics (2022) 2:19
our analysis suggests that early stages of tissue transloca-
tion-mediated toxicities (e.g., oxidative stress, inflamma-
tion [40, 67];) are possible in SFB, a finding that would not
have been apparent without the rescaling procedures used
to correct the underestimation of small particles captured
in trawl nets [33]. Rescaling particle counts based on size
to correct for sampling bias in combination with toxico-
logical thresholds aligned to ecologically relevant metrics
provides the opportunity to compare exposure and hazard
more appropriately [34].
Based on the species sensitivity distributions used to
derive the food-dilution thresholds in Mehinto etal. [42],
the most sensitive species are the black-lip pearl oyster
(Pinctada margaritifera), the marine medaka fish (Ory-
zias melastigma), and a water flea (Ceriodaphnia dubia),
thus representing a diversity of taxonomic groups (mol-
lusk, fish, and crustacea). Like many productive marine/
freshwater estuarine systems, these three taxa are pre-
sent in the SFB, with some similar species for which these
laboratory model organisms may be suitable indicators.
For instance, the Olympia oyster (Ostrea conchaphila) is
native to the SFB and has experienced declining abun-
dance which has been primarily attributed to loss of
habitat and other factors [49]. Experiments using model
species within the Pinctada genus (e.g., Pinctada mazat-
lanica) have been used to inform risk management of the
Olympia oyster in SFB [64].
e ecological risk that we found for SFB was larger
than that for several previous risk characterizations
conducted for other geographies (e.g., [2, 19, 33]). Key
reasons for these differences are the use of different haz-
ard threshold values and alignment procedures (or lack
thereof), and that this sampling effort focused inside an
urban enclosed water body with limited circulation. e
SFB area has a large population of over 7 million peo-
ple [45], and 39 WWTPs feeding into the bay [26]. is
difference is confirmed by the application of our risk
characterization methodology in the National Marine
Sanctuary areas in the open ocean. Here, we did not find
any samples exceeding any thresholds with statistical
significance (Table S16), with the three highest samples
taken at the mouth of the SFB (Fig.4, interactive map in
SI). Comparison of SFB concentrations to global marine
surface water concentrations rescaled by Everaert etal.
[19] to the same size range used here (1 to 5000 μm)
using similar methods, reveals higher concentrations
within SFB than in ~ 75% of global marine locations [19].
While most marine monitoring data has been conducted
in the open ocean, measurements in enclosed areas near
urban centers indicate higher contamination. Everaert
etal. [19] reported 50 microplastics·L− 1 in the Yellow Sea
near China - an enclosed water body adjacent to a popu-
lation of ~ 600 million people [61] - which is higher than
~ 98% of rescaled surface water samples in SFB. e high
concentrations in the SFB and other enclosed water bod-
ies demonstrates the importance of targeted monitoring
to protect coastal resources globally.
While our probabilistic 95th percentiles based on
Monte Carlo modeling and PDFs were sufficiently small
to confidently state SFB contains microplastics at con-
centrations of biological concern, there is room for
improvement in future risk characterizations to reduce
uncertainties. Despite the research advancements that
allowed us to compare exposure data to hazard data with
higher certainty than what has been previously possible
without the rescaling and realignment procedures devel-
oped in Koelmans etal. [33], our risk estimates for SFB
still contain substantial uncertainty and understanding
the factors that contribute to that variability will help
focus advancements needed to improve future estimates.
Here we employed four key factors to calculate risk:
ambient concentration measurements, critical thresh-
olds at which biological effects manifest, size rescaling
and other correction procedures used to correct for data
collection biases, and the alignments of the biological
thresholds [34]. Understanding the relative contribution
of these factors to variability will help improve precision
of future studies.
Variability associated with the fiber correction factor
represented the largest uncertainty in this risk charac-
terization (Fig. S13). e ambient concentrations used
to characterize risk were collected using surface manta
trawls [70], which are known to significantly undercount
particles smaller than the mesh size (~ 333 μm), which
includes the width-measurement of fibers allowing them
to pass through the mesh like spaghetti [, 18, 70]. For
this reason, even after correcting for the uncounted fib-
ers and rescaling concentrations to a common size range
using compartment-specific size distributions, estimates
generated by the 1-L grab samples taken at the same time
as the trawl remained several orders of magnitude higher
(Fig.2), suggesting our SFB risk estimates based on cor-
rected and rescaled manta trawl data were either an
underestimate, or that the 1-L grab samples overcounted
particles. Another possible cause for underestimation of
number concentrations is the fact that the blank correc-
tions were made based on color and morphology com-
binations, rather than polymer identity due to the use of
spectroscopic subsampling as opposed to identification
of all particles. is leads to underestimation of con-
centrations if microplastic particles of the same or simi-
lar color have a different polymer identity. We chose to
rely on the manta trawl data because the 1-L grab sample
volume was too small to provide accurate representation
[32], with the higher percent relative standard deviations
for duplicate manta samples (up to 13.3) being much
Page 14 of 18
Conetal. Microplastics and Nanoplastics (2022) 2:19
lower than 4 duplicate grab samples (up to 46.9 [27];).
is variability was so great that the manta and grab sam-
ples were not even significantly correlated (R2 = 0.04),
even though they were paired closely in space and time
(Fig. S12). Future risk characterization studies would be
improved by applying polymer-specific data and blank
corrections and using pumps with in-line filtration that
include small mesh sizes (< 300 μm) to reduce small-scale
spatial variability while capturing a fuller range of parti-
cle sizes than a towed net can [41, 44, 58].
e rescaling procedures described in Koelmans etal.
[33] help with addressing some of the size undercount-
ing biases associated with use of 333 μm mesh manta
trawl nets, but these corrections yield additional sources
of uncertainty, and complicate comparisons to other risk
characterizations that do not perform rescaling pro-
cedures. e large mesh size of a manta trawl (333 μm)
means that the size-based correction factor is relatively
sensitive to the size exponent value used (see Table S8).
For instance, the manta-trawl collected rescaled concen-
trations in SFB are nearly an order of magnitude higher
than similarly rescaled data from manta-trawls reported
in Everaert etal. [19] due to the larger size power expo-
nent value used here. Everaert etal. [19] used a power
exponent of 1.6 (± 0.5) corresponding to a correction
factor of 40x for a 333 μm mesh, while a power expo-
nent of 2.07 (± 0.03) was used here and corresponds to
a correction factor of 530x for a 333 μm mesh. e power
exponent used in Everaert etal. [19] was derived by Kooi
and Koelmans [32] using the best available data at the
time - which was arbitrarily size binned data extracted
from tables and graphs from other studies - and is there-
fore less accurate than the values derived in Kooi etal.
[37] which used high-resolution datasets at the individual
particle level. Using data from five studies that used state-
of-the-art Fourier-transform infrared imaging and auto-
mated analysis, Kooi et al. [37] derived a length-based
power exponent value of 2.07 (±0.03) for marine surface
waters, which is slightly smaller and substantially more
certain than the site-specific value derived here using
low-resolution manta trawl data measured using manual
techniques (i.e., 2.15 ± 0.48). To reduce uncertainty due
to size rescaling in SFB, small mesh-size samples could
be obtained using in-line filtration and be analyzed using
state-of-the-science analytical techniques to derive local
data (e.g., [44, 51]).
A minor point of uncertainty was the spectroscopy cor-
rection factor applied to concentration data to ensure
that only plastic particles were used to characterize risk
(Fig. S13). We did not consider particles that were clearly
anthropogenic but were not unequivocally plastic, due
to spectroscopic interferences from dyes and/or plastic
additives ([70]; see Fig. S13). As such, the concentrations
used here are also an underestimation for this reason.
Additionally, unquantified inaccuracies in blank correc-
tions based on color-shape combinations are expected
to result in further underestimations of exposure and
therefore risk. To reduce this uncertainty, future stud-
ies should use microplastic-specific spectral libraries to
reduce the proportion of spectra that are less polymer-
specific [,15, 46] as well as automation to allow for chem-
ical confirmation of all particles and polymer-specific
blank corrections [51].
Uncertainties in this risk characterization due to
the selection of concentration thresholds at which
effects manifest are both clearly illustrated and robust,
as Mehinto et al. [42] identified four thresholds that
bracket the severity of response whereas other risk
assessments have relied on single thresholds and tested
the sensitivity based on relevant factors. While the var-
iability associated with the distribution modeling com-
ponent of the SSDs in Mehinto etal. [42] spanned ~ 2
to 5 orders of magnitude, the median threshold values
were insensitive to individual studies - which had only
approximately a two-fold influence. Furthermore, the
eight threshold values used in this study from Mehinto
etal. [42] bracket the published range for microplas-
tics (see [34]), with 95% confidence intervals based on
SSDs being smaller in the food dilution thresholds in
Mehinto et al. [42] than those of thresholds derived
in previous thresholds. Koelmans etal. [33] developed
and applied the same rescaling and alignment meth-
odology to obtain an SSD using studies which dem-
onstrated ingestion and suggested food dilution as the
effect mechanism and derived a hazard concentration
for 5% of species (HC5) of 76 particles/L (95% CI: 11
to 521 particles/L). Additional microplastics HC5 val-
ues which have not been rescaled or aligned span
several orders of magnitude, however six out of nine
published values are within confidence intervals of 76
particles/L [19, 20, 29, 56], with three exceeding the
range due to their inclusion of nanoplastics data [2–4]
as demonstrated in a review by Koelmans et al. [34].
ere is room for additional studies to improve the
threshold values [59], with specific attention to experi-
mental design to assess risk more accurately [16] and
additional studies using fibers (which were highly abun-
dant in the SFB), however the uncertainty associated
with the threshold values is still smaller than that of the
exposure data.
While the Zhu et al. [70] monitoring study in SFB
included more than just surface water samples, here
our risk assessment was limited to surface water expo-
sure. At present, quantitative risk thresholds have not
yet been developed for marine sediment exposure.
However, microplastics are known to accumulate in
Page 15 of 18
Conetal. Microplastics and Nanoplastics (2022) 2:19
sediments, and the SFB had relatively high concentra-
tions of microplastics in the sediments. Moreover, we
only considered the particle-induced effects of micro-
plastics and did not account for additional potential
risks resulting from pathogens [6, 68], or potential
chemical-mediated effects (see [31] for summary of
sorbed contaminants), including the leaching of chemi-
cal additives [71] that preferentially desorb follow-
ing ingestion [11]. In particular, tire wear particles
were a large portion of the microplastics found in SFB
sediments and chemical derivatives from those tire
products have been found to cause acute mortality in
salmon [60]. Furthermore, microplastics are anticipated
to interact with and exacerbate effects from additional
stressors such as thermal stress due to climate change
[35, 63]. Future work should seek to look holistically at
risk in the SFB, including comparing relative risks from
microplastics particles and other stressors such as dis-
solved and sorbed chemicals, for which risk assessment
frameworks already exist.
Conclusion
Overall, our results indicate that SFB has regions where
present exposure concentrations are above thresh-
olds for risk with statistical confidence based on the
best available ecotoxicological hazard thresholds. If we
continue business as usual, inputs of microplastics to
coastal environments are anticipated to triple over the
next 20 years [38]. As such, the region might consider
management actions now to prevent greater risk in the
future. Beyond San Francisco Bay, our study can inform
risk assessments in local regions across the globe. Com-
bined these methods can be used globally to inform
management locally.
Abbreviations
ANOVA: Analysis of variance; PDF: Probability density function; SFB: San Fran-
cisco Bay, California; PNEC: Predicted no-effect concentration; PEC: Predicted
environmental concentration; SSD: Species sensitivity distribution.
Supplementary Information
The online version contains supplementary material available at https:// doi.
org/ 10. 1186/ s43591- 022- 00037-z.
Additional le1: TableS1. Quality scoringa of microplastics concentra-
tion data reported by Zhu et al. [70] in SFB. TableS2. Proportion of
sub-sampled particles spectroscopically confirmed to be plastic by
matrix and location in SFB. TableS3. One-way analysis of variance of
percentages of particles spectroscopically confirmed to be plastic by
matrix for surface water monitoring in SFB. TableS4. Tukey’s multiple
comparison of means for percentages of particles spectroscopically
confirmed to be plastic by matrix for surface water monitoring in SFB.
TableS5. One-way analysis of variance of percentages of particles
spectroscopically confirmed to be plastic by site for manta-trawl data
surface water monitoring in SFB. TableS6. Relative particle shape
proportions of particles spectroscopically confirmed to be plastic in SFB
by matrix/sampling apparatusa. TableS7. Fitted minimum size values
(‘min’) and power law exponents (𝞪) (mean ± standard deviation) for
microplastics by particle length (L) compared to values derived from
Kooi et al. [37]. TableS8. Mesh sizes and site-specific correction factorsa
for occurrence data from SFB. TableS9. Ecotoxicological risk thresholds
for food dilution and tissue translocation from Mehinto et al. (2021).
TableS10. Probability density functions for rescaling and correction
factors to estimate manta trawl monitoring concentrations in SFB.
TableS11. One-way analysis of variance of non-rescaled microplastics
water concentrations by sampling apparatus/matrix. TableS12. Tuke y’s
multiple comparison of differences between non-rescaled microplastic
water concentrations by sampling apparatus/matrix. TableS13.
One-way analysis of variance of rescaled and corrected microplastics
water concentrations by sampling apparatus/matrix. TableS14. Tuke y’s
multiple comparison of differences between rescaled microplastic water
concentrations by sampling apparatus/matrix. TableS15. Number and
percentage of rescaled manta-trawl surface water samples in SFB
exceeding risk thresholds. TableS16. Location-specific probabilistic risk
characterization of microplastics in SFB for correcteda manta trawl
samples (number of samples exceeding food-dilution thresholds by
rescaling confidence interval). Figure S1. Hierarchical tree of spectro-
scopically identified particles from monitoring samples in SFB reported
by Zhu et al. [70]. Only polymers grouped within the ‘plastic’ tier are used
for risk characterization in this study. Plastic polymers operationally
defined using the California definition [57]. Figure S2. Proportions of
particles spectroscopically confirmed to be plastic by compartment and
location (A) and overall, by matrix (B) based on sub-sampling of particles
as reported in Zhu et al. [70]. When site-specific data were unavailable
for a given compartment, the average plastic percentage from all sites
was used to correct microplastic particle counts. Figure S3. Proportions
of particles spectroscopically confirmed to be plastic by compartment
and location (A) and overall, by matrix (mean + − standard deviation);
(B) based on sub-sampling of particles as reported in Zhu et al. [70].
Blank tiles in heatmap correspond to missing data. When site-specific
data were unavailable for a given compartment, the average plastic
percentage from all sites was used to correct microplastic particle
counts. Figure S4. Percentages of total particles spectroscopically
identified that were confirmed to be plastic by location in SFB. Bars and
error bars represent average proportions and standard error, respec-
tively. “General” refers to various locations. Figure S5. Stacked proportion
graph demonstrating relative proportions of shapes of confirmed plastic
particles by matrix across all samples in SFB. Fibers were removed from
blank-corrected manta trawl data in Zhu et al. [70] on the second row
can be seen here, compared with manta trawl samples in which all fibers
were counted as reported by Hung et al. [27]. Figure S6. Box and
whisker plot showing relative proportions of shapes in manta trawl
surface water samples in SFB that counted fibers and fiber bundles
(n = 10). The center lines representing the median values (50th
percentiles), while the box contains the 25th to 75th percentiles. The
whiskers mark the 5th and 95th percentiles, and values beyond those
upper and lower bounds are considered outliers, marked with dots. Of
the 65 manta trawl samples from SFB, fibers were only counted in 9
samples. All fibers were removed from particle counts in blank-corrected
in Zhu et al. [70]. Manta trawl fiber count data from Hung et al. [27].
Figure S7. Particle length distributions in SFB for different compart-
ments. The blue vertical segments indicate the minimum size for which
the fitted power law is valid. The brown slopes represent the fitted
power law distributions. The mean (solid line) and standard deviation
(shaded area) are based on n = 100 bootstraps. The dotted brown line
shows the continuation of the fitted slope beyond the minimum size.
Figure S8. Cullen and Frey graphs of A) proportion of total particles
spectroscopically determined to be plastic based on subsampling in
Zhu et al. [70], and B) log(10)-transformed fiber correction factors of
manta trawl data based on 9 manta trawl samples in which all fibers
were counted (data from [27]). Data were bootstrapped 1000 times
(shown in yellow). Choice of distributions was based on proximity of
observation (blue dot) to theoretical distribution based on kurtosis and
square of skewness. Figure S9. Monte-Carlo simulated probability
distributions (n = 10,000) of correction factors used to model
Page 16 of 18
Conetal. Microplastics and Nanoplastics (2022) 2:19
environmental concentrations of microplastics with manta trawl data
from Zhu et al. [70]. The median value is annotated with a solid vertical
line, and the 95th percentiles are annotated with dashed vertical lines.
A) A normal distribution was fit to the log10-transformed manta trawl
fiber correction factors derived using data from Hung et al. [27]. The
simulated distribution is shown here for the linear-transformed fiber
correction factors. B) The correction factor for the plastic proportion of
total particles (assessed using spectroscopy subsampling) reported in
Zhu et al. [70] were fit by a two-shape beta distribution truncated at
1.00. C) Variability for the size-based alpha distribution was modeled
using a normal distribution. D) the rescaling correction factor for manta
trawl (i.e., 333 to 5000 um) to a common size distribution (i.e., 1 to 5000
um) was derived directly from the alpha distribution. E) The fiber
correction factor, plastic proportion factor, and rescaling correction
factor distributions were multiplied by one another to derive the
combined correction factor distribution. Figure S10. Cumulative
probability curves of aligned surface water microplastic concentrations
collected using manta trawl in SFB by season compared with food
dilution thresholds from Mehinto et al. [42]. “Wet” season samples were
collected between November and April – which are the rainiest times in
the region, while “dry” samples were collected May – October, in which
the region typically receives minimal rain. Dotted lines represent 5th and
95th percentiles based on Monte Carlo simulations based on the
combined variability in the combined correction factor for plastic
proportion due to spectroscopy subsampling and size rescaling. Figure
S11. Comparison of surface water concentrations of microplastics in SFB
collected using manta trawl with food-dilution threshold derived by
Mehinto et al. [42] without corrections for subtracted fiber concentra-
tions from manta trawl data. A) percentages of samples exceeding each
threshold are shown as bar plots, with solid-line error bars reflecting the
25th and 75th percentiles and dashed-line error bars reflecting the 95th
percentile confidence intervals derived using Monte Carlo simulations
based on the combined variability in the combined correction factor for
plastic proportion due to spectroscopy subsampling and size rescaling.
B) empirical cumulative density plot of surface water concentrations and
confidence intervals compared to food dilution thresholds. Figure S12.
Scatterplot and linear regression of raw (uncorrected)) anthropogenic
particle concentrations in 1 L-grab samples with corrected and rescaled
anthropogenic particle concentrations in manta trawls taken at A)
identical locations during the same day; and B) identical locations and
similar time points (within 1 week of each other). Linear relationships
were not statistically significant for any trend (p > 0.05). Figure S13.
Sensitivity analysis of risk characterization based on quantifiable
uncertainties for correction factors and rescaling used to estimate
environmental concentrations of microplastics in SFB based on manta
trawl monitoring data. For each variable shown, the median values are
used for the other correction/rescaling factors to demonstrate the
relative variability of each factor individually. Values shown are the total
percentage of corrected/rescaled manta trawl samples in SFB exceeding
threshold one (food dilution). Black lines represent 95% confidence
intervals, while the dashed red line represents the median value. Figure
S14. Risk characterization based on site-specific size distribution data
obtained from measured particle-length data from manta trawl samples
analyzed using the methods in Kooi et al. [37]. Due to the low-resolution
particle distribution data, uncertainties in the rescaled concentrations
are significantly greater than the concentrations rescaled using the best
available surface water particle distribution data reported in Kooi et al.
[37] and presented in the main manuscript. A) percentages of samples
exceeding each threshold are shown as bar plots, with solid-line error
bars reflecting the 25th and 75th percentiles and dashed-line error bars
reflecting the 95th percentile confidence intervals derived using Monte
Carlo simulations based on the combined variability in the combined
correction factor for plastic proportion due to spectroscopy subsam-
pling and size rescaling. B) empirical cumulative density plot of surface
water concentrations and confidence intervals compared to food
dilution thresholds.
Acknowledgments
The authors are grateful to Dr. Diana Lin and other staff at the San Francisco
Estuary Institute for assisting in accessing data, advising on methods, and
reviewing the manuscript.
Peer review
This paper, in keeping with research integrity principles, was not handled or
edited by Dr. Steve Weisberg, Dr. Albert Koelmans or any other Microplastics
and Nanoplastics editor involved in the workshop.
Authors’ contributions
S.C. wrote the main manuscript text, conceptualized, developed, and performed
the methodology, conducted the formal analysis, curated the data, produced
visualizations, and administered the project. S.B.W. wrote and edited the main
manuscript text and conceptualized the project. C.R. wrote and edited the main
manuscript text, curated the data, and conceptualized the project. M.K. edited
the main manuscript and conducted formal analysis for power laws. A.A.K. wrote
and edited the main manuscript and conceptualized the project. All authors
reviewed the manuscript. The author(s) read and approved the final manuscript.
Author’s information
The views and opinions expressed in this article are those of the authors and
do not necessarily reflect the official policy or position of any government
agency or institution.
Funding
Scott Coffin was supported through funding from the California State Water
Resources Control Board. Chelsea Rochman and Stephen Weisberg did not
receive funding for this work. Albert Koelmans and Merel Kooi were supported
through funding from Wageningen University & Research.
Availability of data and materials
Code used for modeling is available in https:// github. com/ Scott Coffin/ SFBay
MPRis kChar acter izati on/ tree/ Manus cript
Size-rescaled, blank-corrected, fiber fraction-corrected, and plastic spectro-
scopic sub-sampling corrected microplastics occurrence data are available in
the supplemental information as a comma-separated value file (.csv).
An interactive map showing concentrations of microplastics and risk exceed-
ances is available in the supplemental information as a HyperText Markup
Language (.html) file.
Declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
All authors have reviewed the final manuscript and consent to its publication.
Competing interests
The authors declare that they have no known competing financial interests
or personal relationships that could have appeared to influence the work
reported in this paper.
Author details
1 California State Water Resources Control Board, Sacramento, California, USA.
2 Southern California Coastal Water Research Project Authority, Costa Mesa,
California, USA. 3 Department of Ecology and Evolutionary Biology, University
of Toronto, Toronto, Ontario, Canada. 4 Aquatic Ecology and Water Quality Man-
agement Group, Wageningen University, Wageningen, The Netherlands.
Received: 30 March 2022 Accepted: 1 June 2022
Page 17 of 18
Conetal. Microplastics and Nanoplastics (2022) 2:19
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