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\begin{document}

\title{The relationship between size, abundance, and mass of particles in the
surface and bottom waters of the Chesapeake Bay}



\author[1]{Emily Dougherty}%
\author[1]{Ashley Hollins}%
\author[1]{Jacob Cram}%
\affil[1]{University of Maryland Center for Environmental Science}%


\vspace{-1em}



  
  \date{May 24, 2024}


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\selectlanguage{english}
\begin{abstract}
~ Particulate matter modulates the transport of carbon and nutrients
through estuarine environments. In the Chesapeake Bay, sinking of
particles and their consumption by microbes likely modulates the
emergence of a seasonal oxygen deficient zone. The relationship between
particle size and abundance affects transport dynamics of the particles
and the biology of associated organisms. The variability of particle
characteristics has not previously been characterized across the length
of the Chesapeake Bay, nor has it been compared to the oxygen deficient
zone. Therefore, we measured the size to mass and size to abundance
relationship of suspended particles along the Chesapeake Bay during a
major deoxygenation event. A laser in-situ scattering and
transmissometer measured particle size and abundance at six stations.
Five particle size classes were sampled at surface and bottom depths.
Particles in the less saline northern end of the Bay were less massive
relative to size than particles farther south. Estimates of total
particle mass, calculated by combining particle size to mass and
particle size to abundance data, suggested that the anoxic region has
lower particulate mass than overlying oxic water, perhaps because
stratified water above the oxygen minimum zone keeps particles from the
productive top layer from mixing into this region. Total particle mass
was higher just above the sediment, suggesting resuspension of benthic
particles. Our data provide the first systematic survey of size resolved
particle abundances across the Chesapeake Bay oxygen minimum zone and
provide context to future work in evaluating the biogeochemical role of
particles in this environment.%
\end{abstract}%



\sloppy


\section*{Introduction}

{\label{337879}}

Particulate Matter (PM) is comprised of both organic (Particulate
Organic Matter -- POM) and inorganic components and is an essential part
of carbon transport in estuarine environments. Estuaries facilitate and
regulate the transport of PM, as well as dissolved carbon, from rivers
into the oceans (Fisher et al. 1998; Loh et al. 2006) and produce PM in
situ (Savoye et al. 2011; Middelburg and Herman 2007). The dynamic
conditions of estuaries create gradients in the abundance and
composition of particles, which vary over spans of hours, seasons, or
years (Canuel and Zimmerman 1999) and between locations (Fisher et al.
1998). The concentration, size distribution, and dynamics (including
aggregation and disaggregation) of PM in estuaries is affected by
factors including turbulence, differential settling, Brownian motion,
salinity gradients, and compounds produced by organisms that cause
particles to aggregate (Eisma et al. 1991). High collision frequency,
which depends on the concentration of particles and the energy of the
water, can lead to particle aggregation, while turbulence breaks up
particles (Fugate and Friedrichs 2003). Near the surface, particle size
may be limited by low collision frequency (Fugate and Friedrichs 2003).
Aggregation and breakup together drive particle size distributions to an
equilibrium distribution, which can vary regionally in response to
variation in turbulence and other factors (Chen et al. 1994). Sinking
speed also affects particle size distributions, with denser faster
sinking particles leaving the pycnocline more quickly than less dense
slowly sinking or non-sinking particles (Fugate and Friedrichs 2003). PM
that reaches the lower water column of estuaries settles into the bed,
where strong turbulence may cause re-suspension of large particles and
more breakup (Hill et al. 2001).The Chesapeake Bay is the largest
estuary in the United States, with the main stem measuring 320 km
(Schubel and Pritchard, 1986). Within the Bay, there are strong salinity
gradients, with a low salinity region (\textless{} 0.5 ppt) in the
northern section, a mesohaline zone (0.5 -- 25 ppt) extending
approximately from 39@\selectlanguage{english}N latitude to the mouth of the Potomac River, and
a high salinity region (\textgreater{} 25 ppt) near the mouth of the Bay
(Maryland Department of the Environment). The Chesapeake Bay has an
expanding region of seasonal anoxia (Testa 2018; Kemp 1992), with
deficits occurring annually in the mesohaline region (Officer et al.
1984). Deoxygenation is driven by microbes at depth consuming the
organic portion of particles that originate in high production surface
waters (Robinson 2019). In the Chesapeake Bay, these particles originate
from surface waters primarily in the mainstem of the Bay (Wang and Hood
2021).~ Anoxic regions are intensified by sewage and agricultural
runoff, which increase the rate of phytoplankton production (Canuel and
Zimmerman 1999). Since the Chesapeake Bay is a region of high biological
productivity and diverse habitats, there is high variability in the
origin and distribution of PM. In the upper Bay, freshwater input mostly
from the Susquehanna River, which deposits nearly one million tons of
sediment into the bay annually (Donoghue et al. 1989), accounts for 83\%
of suspended particles, and shore erosion contributes 13\% (Biggs 1969).
In the middle Bay, 52\% of particles are formed from shore erosion, and
40\% come from phytoplankton, both through primary production and the
production of skeletal material (Biggs 1969). In the southern Bay, most
particulate matter originates in that location from the production and
material of phytoplankton and zooplankton. The study of the southern Bay
did not find significant contribution by or variation between the mouths
of rivers (Canuel and Zimmerman 1999). Particles in the Bay are also
affected by resuspension of bottom sediments. Bay topography and the
composition of the Bay floor interact to cause regionally variable
patterns in sediment resuspension (Xiong et al. 2021). ~Turbidity driven
by resuspension of particles attenuates light in the northern Bay, which
shifts more primary production down toward the central Bay (Moriarty et
al. 2020). Resuspension in the central Bay further increases the
concentration of organic matter in this region, leading to more
remineralization and a decrease in oxygen near the bottom of the water
column (Moriarty et al. 2020).The balance of aggregation,
disaggregation, and particle transport differ between the mouth of the
Bay, the seasonally anoxic mesohaline, and the Upper Bay. Several
studies have characterized particle size distributions near the mouth of
the Bay: One of these studies combined acoustic and optical measurements
of particle properties and identified temporal variability in the
sinking speed and size properties of particles near the mouth of the Bay
(Fugate and Friedrichs 2002). In another site in the lower Bay, it was
found that higher turbulent kinetic energy near the bed is associated
with larger particle sizes (Fugate and Friedrichs 2003). This result
contrasted with other estuarine river environments in this study, where
turbulence near the riverbed fragments particles, keeping their sizes
small. The authors suggested that the Chesapeake Bay has a biologically
active benthic community, which produce compounds that create large
aggregate particles under turbulent conditions (Fugate and Friedrichs
2003). ~Furthermore, particle residence time, the amount of time that a
particle remains in the water column before remineralizing or becoming
incorporated into the sediment also varies across space and time (Xiong
and Shen 2022).No study, to our knowledge, has characterized the
particle size distribution spectrum in the mesohaline region of the Bay.
However, several studies have explored the origin of particles
contributing to the seasonally anoxic region of the Bay. Particle
transport into the mesohaline is driven in large part by advection of
deep water from the high salinity mouth of the Bay and particle sinking
(Jonas 1992). Particle tracking experiments have shown that particles
that ultimately sink into the anoxic region of the Bay vary in their
origin depending on the tidal cycles and corresponding currents (Wang
and Hood 2021). The organic portion of this particulate matter has been
shown to degrade quickly (Jonas and Tuttle 1990), and so fuels the
oxygen removal in this anoxic region.~~~~~~~~~~~ In the Upper Bay, there
is a defined estuarine turbidity maximum (ETM) region, where the
Susquehanna River meets the more brackish waters of the main Bay
(Schubel and Biggs 1969). The ETM is caused by suspension and
entrainment of sediment from the bay floor, which is maintained by
interactions between tidal forces and the steep salinity gradient
(Sanford et al. 2001). This region is characterized by vertical
stratification and seasonal variability in particle concentrations
(Fisher et al. 1998). Particle concentrations are influenced by
particles coming from the Susquehanna River, particularly in spring when
there is more runoff into the river (Schubel and Biggs 1969). Total
particle concentrations in the upper Bay are generally higher than in
the mesohaline region (Biggs 1969). While each of these studies examined
particle distributions at specific regions and sites in the Chesapeake
Bay, no previous study has, to our knowledge, characterized particle
size distribution across the length of the Bay. While comparing the
different papers can give us insight about differences between these
regions, they each use different measurements and are taken at different
times. Furthermore, no study to our knowledge has examined particle size
distributions within, around and above the oxygen deficient zone.
Understanding particle size distribution is important because particles
of different sizes can have different origins, be composed of different
types of organic carbon, and be host to different chemical processes,
all influencing particle dynamics. Large particles have different
biology than small ones, with unique microbial communities (Mestre et
al. 2017). Large particles in particular have been postulated to harbor
anoxic cores which enable unique biogeochemical processes, such as
reduction of nitrogen and sulfur-containing compounds (Bianchi et al.
2018; Fuschman et al. 2011). Size distribution also differs based on the
main composition of particles; inorganic small particles are often
dominated by clay and silt while larger inorganic particles are sand
(Nichols, 1972). However, since sand does not suspend in the water
column, larger particles must be comprised of lighter organic carbon,
and small particles likely also contain organic carbon. Particle size
information may provide clues about how much organic carbon is in
suspension and its quality. Since these different materials associate
with different amounts and sources of organic carbon (Burone et al.
2003), studying the size distribution in the Bay could be used to
identify types of particles and the organic carbon they carry.
Additionally, it has been found in oceanography that particle size
distribution is the main factor determining the transfer of carbon into
the deep ocean (Cram et al. 2018). Large particles sink more quickly
than smaller ones, and so can more efficiently transport organic matter.
Similar phenomena may occur in the Chesapeake Bay, with large and small
particles having different dynamics. ~~~~~~~~ The aim of the study was
to describe how particle abundance and size distribution spectra of
particles vary over space and depth, and in particular to identify
differences between oxygenated and deoxygenated environments. Towards
this end we carried out measurements of the particle size to abundance
distribution and size to mass distribution along the surface and bottom
of the mainstem of the Bay, from the high salinity mouth of the Bay to
the lower salinity waters just below the ETM. Such data will provide
information about the processes that shape particle size and transport.
In particular, we are interested in how the anoxic zone affects particle
dynamics, because particles attenuate slowly in anoxic regions (Rasse
and Dall'Olmo 2019). Exploring the interactions between anoxic
environments and particle size distributions has the potential to
provide clues about how hypoxia relates to the regional carbon cycle.
This study uses a combination of optical and mass measurements to
characterize particle distribution throughout depth and latitude. This
data is then compared with the known location of the oxygen deficient
zone in the Chesapeake Bay to draw conclusions about the relationship of
anoxia and particle distribution.~~~~ ~~

\section*{Methods}

Samples and observations were collected July 22, 23, and 24, 2019, on
the R/V \emph{Rachel Carson} from six stations along the main stem of
the Chesapeake Bay, corresponding to the Maryland Department of the
Environment's water quality monitoring stations CB3.1 (39.24\selectlanguage{ngerman}°N,76.24°W,
corresponding to 13.3m water column depth), CB3.2 (39.16°N, 76.30 °W,
12.2 m), CB3.3C (39.00°N, 76.36°W, 24.1 m), CB4.3C (38.56°N, 76.43°W,
27.1 m), CB5.1 (38.32°N, 76.29°W, 34.3 m), and CB5.5 (37.69°N, 76.19°W,
17.7 m) (Fig. 1A).

\par\null

\textbf{Fig. 1} Sample Collection locations (A) and corresponding
oceanographic data (B-F) \textbf{} measured by a seabird CTD\textbf{.}
These included Temperature (B), Salinity (C), Fluorescence (D), pH (E),
and oxygen (F). Shapes in the CTD profiles indicate the locations where
particle samples were collected. Lines indicate the corresponding CTD
profiles. All stations have two samples, one in the upper mixed layer,
and one below, except station 4.3 which also has a sample taken at the
oxycline. CTD profiles all extend from the surface to approximately 5 m
above the seafloor, except station 5.5, which only extended to 15 m

\par\null\par\null

A Seabird CTD (Conductivity, Temperature, and Depth), mounted on the
CTD-rosette measured water Temperature, Salinity, Fluorescence, and pH
throughout the water column. At each station, a laser \emph{in-situ}
scattering and transmissometry (LISST-100X) instrument (Sequoia
Scientific, Inc.) was lowered into the water to measure a vertical
profile of the particle size distribution spectrum. The LISST uses the
laser light diffracted by particles to provide a reading of the total
volume concentration (\selectlanguage{greek}µ\selectlanguage{english}L Particles/L Water) of particles in several
bins, each represented by a minimum particle diameter (LISST 100X Manual
2015). The LISST was factory calibrated and calibrated on filtered
particle-free seawater before use. Particles were assumed to be
spherical in shape, so the diameters were used to calculate the average
volume of an individual particle in each bin. From the total volume
sampled and the individual particle volumes, the number of particles per
liter of water was calculated for each size bin. For purposes of
comparison to particle mass measurements, the LISST size data were
grouped into the filter size fractions of 1.2 \selectlanguage{greek}µ\selectlanguage{english}m, 5 \selectlanguage{greek}µ\selectlanguage{english}m, 20 \selectlanguage{greek}µ\selectlanguage{english}m, 53 \selectlanguage{greek}µ\selectlanguage{english}m,
and 180 \selectlanguage{greek}µ\selectlanguage{english}m, each corresponding to our filter size fractions, by summing
particle abundances of all LISST size bins that fell within each filter
size bin. No particle number was obtained for the 0.2 \selectlanguage{greek}µ\selectlanguage{english}m filter size,
since this size is below the LISST detection threshold of 1 \selectlanguage{greek}μ\selectlanguage{english}m.
Similarly, LISST measurements were not recorded for the 500 \selectlanguage{greek}μ\selectlanguage{english}m size
fraction as we found measurements above 200 \selectlanguage{greek}μ\selectlanguage{english}m to be inconsistent.
Initial data processing was carried out by the proprietary LISST-SOP
software provided for the LISST-100X by Sequoia scientific. All
subsequent data analysis was performed in the R statistical programming
language (R Core Team 2019).

At each station, the LISST measurement was completed in approximately
five minutes. The time of day of measurement varied for each station. On
the first day, low tide was at 3:03 pm (NOAA Tides and Currents), and
measurements started at 12:31 pm at station 5.5 and 4:52 pm at station
5.1. The second day, high tide was at 8:55 am, and measurements started
at 8:36 am at station 4.3 and 11:14 am at station 3.3. The third day,
low tide was at 4:17 pm, and measurements started at 12:47 pm at station
3.1 and 3:21 pm at station 3.2.~

Water samples were collected in the surface mixed layer and five meters
above the floor of the Bay at each station. At station 4.3C a sample was
also taken at the oxycline in the mid water column (Fig. 1). For each
sample, between 13 and 20 liters of water were collected with Niskin
bottles and gravity filtered, in sequence, through five nylon filters
with diameters of 142 mm and decreasing pore sizes of 500 \selectlanguage{greek}µ\selectlanguage{english}m, 180 \selectlanguage{greek}µ\selectlanguage{english}m, 53
\selectlanguage{greek}µ\selectlanguage{english}m, 20 \selectlanguage{greek}µ\selectlanguage{english}m, and 5 \selectlanguage{greek}µ\selectlanguage{english}m. Each filter was rinsed with 0.2 \selectlanguage{greek}µ\selectlanguage{english}m filtered
seawater from the same station. An aliquot of this rinse water was
vacuum filtered through a pre-combusted, pre-weighed, 25 mm diameter 1.2
\selectlanguage{greek}µ\selectlanguage{english}m pore size glass fiber filter (Whatman 16936209) and was saved for
analysis of particle mass.

In the lab, particle mass was measured for each size fraction by drying
and re-weighing the pre-weighed glass fiber filters and calculating its
change in mass. This value was divided by the number of particles
corresponding to this size bin to find the average mass per particle in
each size class.

The slope and intercept of the particle size to abundance relationship
and size to mass relationship were calculated on the log of the values
of particle size, abundance and mass. The slope of the size to abundance
relationship is called the particle size distribution slope (Jackson et
al. 1997), and the slope of the size to mass relationship is the
particle fractal dimension (Jackson et al. 1997). Intercepts correspond
to the predicted abundance and mass of 1 \selectlanguage{greek}μ\selectlanguage{english}m particles. Total particle
mass profiles throughout the water column were estimated by multiplying
particle abundances in each size class, measured by the LISST, by the
empirically derived size to mass relationships determined by the
filtration method. Data from the top meter of the water column was
removed from the plots for particle mass and abundance profiles, as
light from the surface is known to create artifactually high estimates
of particle abundance in these samples (L. Sanford pers. comm.).

~

\section*{Results}

\emph{Physics and Chemistry of the Bay}

Stations followed a salinity gradient, with the lower salinity
associated with northern stations near the mouth of the Susquehanna
River and higher salinity with stations closer to the mouth of the Bay
(Fig. 1A-B). While station 3.1 was fully mixed, all remaining stations
had an oxygenated mixed layer, followed by a pycnocline, below which
water was cooler and more saline (Fig. 1B, C, F). All stations except
3.1 and 5.5 were anoxic below the pycnocline. The deepest samples at
station 4.3 and 5.1 were sulfidic, as evidenced by a sulfurous smell to
the water (M. Gomes Pers. Comm.). Chlorophyll fluorescence was present
at all stations through the pycnocline (Fig. 1D). pH was lower in the
two upper-most stations than in the others (Fig. 1E).

\emph{Total Particle Abundances}

The LISST detected on the order of 10\textsuperscript{7} --
10\textsuperscript{8} particles per liter at most stations through most
of the water column (Fig. 2). At most stations, there was an increase in
particle abundance, usually to around 10\textsuperscript{8} particles
per liter, just above the floor of the Bay. In the anoxic water,
particle abundance was generally lower than elsewhere, often around
10\textsuperscript{7} particles per liter. There were regions of
apparently very low particle numbers in the oxycline, in stations where
an oxycline was

Total Particles =~ \selectlanguage{greek}α\selectlanguage{english}~ + S(Pressure) + S(Pressure, Station) + \selectlanguage{greek}ε \selectlanguage{english}~ ~ ~ ~ ~
~ ~ ~ ~ ~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~Eqn. 1

In this equation \emph{S} indicates a smooth ``thin plate'' regression
spline function, which is the MGCV packages default smooth function
(Wood 2003). When the S term contains more than one variable, it is a
multidimensional smooth that considers the interaction between the
terms. Station is treated as a categorical variable. \selectlanguage{greek}\emph{ε} \selectlanguage{english}is a
normally distributed error term. The fit of this function to our data
indicated that across all stations, this variability with depth was
statistically detectable, and that there was statistically significant
station to station variability (R\textsuperscript{2} = 0.78 (overall
model), \emph{p} \textless{} 0.001 (for all interaction terms but one
(pressure * station 3.3))). Particle abundance normalized to LISST size
bins decreased as particle size increased (Fig. 3).

\emph{Particle Size to Abundance Relationship}

At all stations, there was a negative power law relationship between
particle size and particle abundance.~ The slope of the power law
distribution, which is the slope of the relationship between log
transformed particle abundance and log transformed particle size, ranged
at most stations and depths from -3.5 to -4. However, several depths at
some stations had anomalously large negative particle size distribution
slopes (Fig. 4). A general additive model of form shown in Eqn. 2
suggested that that there was statistically significant variability in
the particle size distribution between depths, and that this
relationship varied between stations (R\textsuperscript{2} = 0.167,
\emph{p} \textless{} 0.01).

\par\null

~ Particle Size Distribution Slope =~ \selectlanguage{greek}α\selectlanguage{english}~ + S(Pressure) + S(Pressure,
Station) + \selectlanguage{greek}ε \selectlanguage{english}~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~~~Eqn. 2~~

\emph{Total Particle Mass Patterns}

Estimated total particle mass per liter of all particles \textgreater{}
1.2 \selectlanguage{greek}μ\selectlanguage{english}m ranged from 10 to 100 mg /L (Fig. 5). Calculated particulate
matter concentrations were higher in the bottom sample than the surface
sample at every station except 4.3 (OLS log(Mass) \textasciitilde{}
Depth {[}Surface or Bottom, excludes Oxycline{]}, F = 7.6, \emph{p} =
0.02). At station 4.3 the sample taken in the oxycline had the highest
particle concentrations, followed by the surface sample, and then the
bottom sample. Particle concentrations estimated by LISST measurements
were generally higher in the surface than in the bottom, except at
stations 3.1 and 3.2. There was no detectable relationship between
station latitude and observed particle mass (Ordinary Least Squares
regression of form `log(Mass) \textasciitilde{} Latitude`; F = 0.001,
\emph{p} = 0.97).

\emph{Particle Mass to Size Relationship}

Mass per particle increased with particle size, following a power law
(Fig. 6).~ The masses of particles of each size class were similar at
each depth, ranging from about 10\textsuperscript{-9} mg/particle in the
1.2 \selectlanguage{greek}µ\selectlanguage{english}m class to about 10\textsuperscript{-3} mg/particle in the 500 \selectlanguage{greek}µ\selectlanguage{english}m
class. There did not appear to be statistically significant differences
between the slopes of the relationship between particle size and
particle mass (Fig. 6). A linear model that explored interactions
between size, station and depth on particle mass, of form `ln(Mass)
\textasciitilde{} ln(Size) * Station * Depth`, where ``ln'' indicates
the natural logarithm function, found that while there was a
relationship between size and mass (\emph{p} \textless{}
10\textsuperscript{-10}), neither station, depth, nor any interaction
between size, station and or depth had any statistically significant
relationship to particle mass. However, a linear model of form `ln(Mass)
\textasciitilde{} ln(Size) + Station` suggested that there was station
to station variability in the intercept of the size to mass relationship
(\emph{p} \textless{} 0.01 for all stations, with the exception of
stations 3.2 and 3.1 which had statistically identical intercepts). The
`eemeans` package was used to compare the y intercepts of the size to
mass relationship at each station. It was found that station 3.1 had
statistically significantly lighter particles, adjusted for size, than
stations 4.3 (difference = -2.5 +- 0.7 (1 standard error)
log(mg/Particle), t-ratio = -3.86, \emph{p}~ = 0.012) or 5.1 (\emph{p} =
2.5 +/- 0.7 log(mg/Particle), t-ratio = -3.79, \emph{p} = 0.014). All
other differences were found to be not statistically significant, after
adjusting for multiple comparisons (FDR \textless{} 0.05). (Fig. 7).

\emph{Calculated Total Particle Mass Profiles}

By combining the information from the mean particle size to mass
relationship with the abundances of particles at each size, we were able
to calculate expected particle mass throughout the water column at each
station (Fig. 5; black circles). A general additive model of form shown
in Eqn. 3 suggested that particle mass varied statistically
significantly between depths (F = 9.4, \emph{p} \textless{}
10\textsuperscript{-10}), with all stations except 3.3 and 5.5 showing
statistically significant deviations from the main profile (F
\textgreater{}= 3.1 \emph{p} \textless{}0.003 for all remaining
stations).

Total Particle Mass =~ \selectlanguage{greek}α\selectlanguage{english}~ + S(Pressure) + S(Pressure, Station) + \selectlanguage{greek}ε\selectlanguage{english}~ Eqn.
3

Calculated total particle mass appeared to be related to, but was often
an underestimate of, observed total particle mass (Fig. 5).

~

\section*{Discussion}

Measurements of physical and chemical parameters (Fig. 1) showed depth
profiles typical of previous measurements of the Chesapeake Bay at this
time of year (Pritchard 1952; Murphy et al. 2011). The location of the
stations arranged along the length of the Bay allowed for gradients to
be observed. The salinity gradient in the Chesapeake is formed as
colder, denser saline water enters the mouth of the Bay and flows
northward, while warmer, less dense, freshwater enters from rivers and
tributaries and moves south (Pritchard 1952). The density difference in
these two layers forms a pycnocline, which was observed at all stations.
The pycnocline blocks the vertical transfer of oxygen, creating the
anoxic zones that were seen in most stations. Large anoxic and hypoxic
zones form during summer in the Chesapeake Bay and were clearly seen in
July when measurements were taken. Anoxic bottom waters have been shown
to lead to increases in sulfide concentrations (Roden and Tuttle 1992),
as seen in the sulfidic samples collected in stations 4.3 and 5.1.

As samples were collected at various times throughout the day, it is
possible that changing tidal currents could have affected particle
distribution, although tidal and wind current speed was not measured.
The collection at station 4.3 occurred close to high tide, when stronger
currents could potentially cause more particle resuspension. However, a
study of tidal currents in the middle Chesapeake Bay found them too weak
to resuspend bottom sediments significantly (Ward 1985).

Throughout the Chesapeake Bay, particle size distribution profiles
displayed a power law relationship between size and abundance, with
slope usually between -3.5 and -4, which is within the range of values
seen in open ocean locations (Sheldon et al. 1972; Kostadinov et al.
2009; Cram et al. 2018). The slopes generally did not show much change
with depth. This pattern is consistent with the findings of a study that
size distribution does not change significantly with depth across the
Atlantic Ocean (Gordon 1970), though it contrasts with measurements of
an oxygen deficient zone in the Eastern Tropical North Pacific that
found changes in the particle size slope with depth (Cram et al. 2022).
The anomalous spikes of particularly negative slopes, seen especially in
stations 4.3, 5.1, and 5.5, could indicate a lack of large particles in
the oxycline, as the spikes occurred at approximately the same depth.
These spikes could also be artifacts, perhaps induced by changes in
salinity or temperature or introduced by the LISST's electronics. Due to
this possibility, we do not emphasize the spikes in our results and our
overall conclusion remains that the particle size distribution slopes
stayed mostly constant with changing depths.

While particle size spectra have been measured in the Chesapeake Bay,
the particle size distribution slope is often not reported for the Bay
(Schubel 1968; Schubel and Nelson 1973) or for estuaries in general.
These measurements are more common in open ocean studies, thus providing
values with which to compare our PSD measurements. However, it is
expected that due to the much different conditions of open ocean water,
there may be significant differences in PSD in estuaries that are yet
unknown.~

The particle size to mass relationship also stayed consistent throughout
stations and depths, with mass increasing and density decreasing in
larger particles. This result suggests that particles of the same size
may also have similar composition, leading to similar mass and density.
The slopes of the size to mass relationship, or fractal dimension, at
each station and sample depth (Figure 7) were similar to the values
calculated in other particle studies. For instance, Fall et al. (2021)
calculated fractal dimension in the York River as the size to density
relationship with a bulk value of 2.25. Other studies have quantified
fractal dimensions from the size to density or size to settling velocity
relationship in the Chesapeake Bay (Sanford et al. 2004) and other
estuaries and marine environments (Hill et al. 1998; Guidi et al. 2008;
Jackson et al. 1997). These previous measurements of fractal dimension
values for particles typically fall somewhere between 1.3 and 2.5, and
the values for this study are on the low end of that range. Aggregation
and disaggregation of particles affect their fractal dimension, with
larger aggregates having lower fractal dimensions than small particles.
Li and Logan (1995) found fractal dimension to decrease from 2.49 to
1.68 as particles coagulated during a phytoplankton bloom in a
laboratory mesocosm. It is possible that collection methods in our study
could lead to disaggregation of particles; however, the fractal
dimensions' consistency with other studies lend confidence to our
observations.

Although particle size to mass relationships stayed consistent across
stations and depth, total particle abundance and mass both varied by
depth. Particle abundance profiles generally tracked total particle mass
profiles (Fig. 2 and 5). The calculated total mass values from collected
particles were consistently higher than the estimated mass based on
LISST measurements, especially in station 4.3 (Fig. 5). This disparity
could be caused by the assumption that the power law relationship
between size and mass is the same at each station. Particle abundance
and total particle mass both increased near the bottom of the water
column, suggesting that the current is resuspending sediment from the
floor of the Bay. In station 4.3 (Fig. 5), this increase at the bottom
was seen in the mass from the samples (observation), but not in the
LISST calculations, likely because the LISST measurements do not reach
as deep as the sample, so the effect was not detected by the LISST. In
the anoxic water below the pycnocline, particle abundance and total mass
was lower than in surface waters. This scarcity of particle mass below
the pycnocline suggests either fast removal of particles or low
transport into this region from the more productive surface waters, as
the pycnocline separates anoxic water from surface water. We argue that
since particle remineralization is thought to be slow in anoxic water
(Cram et al. 2018), it is likely the latter process, low in situ
production and low flux from the surface that leads to the lower anoxic
particle mass. Particle abundance and particle mass profiles diverged
near the middle of the depth profiles, where abundance sharply decreased
in most stations, but mass did not. The sharp decreases in particle
abundance could be the same artifacts that may be seen in the particle
distribution slopes. Again, these anomalies do not change the overall
conclusion, with abundance and mass following similar patterns, highest
near the surface and bottom of the water column.

This study was the first characterization of particle size to abundance
and size to mass distribution across the length of the Chesapeake Bay as
well as with depth. Overall, there was little variation by latitude,
with particle size, abundance, and mass mostly following the same
patterns at each station. This scarcity of variation pattern persists
despite variations in particle source, with terrestrial sources in the
north and in-situ production being more important in the south. We note
that here we have only measured size and mass, and variability in other
particle properties such as biology and chemistry may differentiate
particles latitudinally. The sample stations covered the upper (CB3.1
and CB3.2) and middle Bay (CB3.3C, CB4.3, CB5.1) as well as the upper
stream of the Rappannock Shoal (CB5.1) which falls just north of the
lower Bay. The results suggest that within the areas studied, in the
deep channel, it is factors other than latitude that lead to variability
of particle characteristics. We note that the Estuarine turbidity
maximum, characterized by higher particle loading and different dynamics
than elsewhere (Malpezzi et al. 2013; Sanford et al., 2001; Keller et
al. 2014), is north of our northernmost station CB 3.1 and therefore not
included in this analysis. Significant differences were observed
vertically, with particle mass and abundance higher just above the floor
and low in the body of the anoxic layer. This low total particle mass in
the anoxic water suggests that the influx of particles from the photic
zone sinking and mixing into the anoxic water occurs more slowly than
carbon removal from the anoxic layer, which would occur by particles
settling into the sediment or by decay and remineralization. The low
particle abundances could thus indicate the presence of particle
remineralization despite the lack of oxygen, because if remineralization
was absent, we would expect carbon accumulation in this region even if
particle flux into the region was low. A low input of carbon into this
region would also suggest carbon limitation of microorganisms in the
anoxic region.

\section*{Conclusion}

{\label{228984}}

This analysis of particulate organic matter provides data for particle
size distribution and particle mass at surface and bottom depths across
various stations in the mesohaline region of the Chesapeake Bay. {This
study was the first to analyze particle distribution at multiple
locations in the Bay.} Generally, particle size/abundance and size/mass
relationships were similar between stations and depths. Particle
abundance and mass mostly followed similar patterns to each other,
decreasing in the anoxic zones, with an increase near the bottom of the
Bay. The results show the influence of depth on particle distribution,
while patterns stayed consistent throughout station latitudes at the
time of sample collection in July.

\par\null

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