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Citizen Science for Dissolved Oxygen Monitoring: Case Studies from Georgia and Rhode Island

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Citizen science is emerging as an increasingly viable way to support existing water monitoring efforts. This article assesses whether water quality data collected by large numbers of volunteers are as reliable as data collected under strict oversight of a government agency, and considers the potential of citizen science to expand spatial and temporal coverage of water monitoring networks. The analysis hinges on comparison of data on water temperature and dissolved oxygen (DO) in freshwater streams and rivers collected by four entities: the United States Geological Survey (USGS) network of field scientists, the USGS network of automated sensors, the Georgia Adopt-A-Stream volunteer water monitoring program, and the University of Rhode Island Watershed Watch (URIWW) volunteer water monitoring program. We find that volunteer-collected data exhibit the expected relationship between temperature and DO. Furthermore, we find that volunteer-and USGS-collected data lie in roughly the same range, although volunteer-collected DO measurements are lower on average (by approximately 1 mg/L in Georgia and 1.8 mg/L in Rhode Island). The results indicate that volunteer-collected data can provide reliable information about freshwater DO levels. These data could be useful for informing water management decisions—such as deciding where to focus restoration efforts—but may not be appropriate for applications in which highly precise data are required. We also comment on the growth potential of volunteer water monitoring efforts. Encouraging volunteers to collect data in high-priority or undersampled areas may help expand spatial and temporal coverage of volunteer monitoring networks while retaining high levels of participation.
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ORIGINAL ARTICLE
Citizen Science for Dissolved Oxygen Monitoring:
Case Studies from Georgia and Rhode Island
Hannah Safford*and Catherine A. Peters
Department of Civil and Environmental Engineering, University of California, Davis, California.
Received: May 23, 2017 Accepted in revised form: August 1, 2017
Abstract
Citizen science is emerging as an increasingly viable way to support existing water monitoring efforts. This
article assesses whether water quality data collected by large numbers of volunteers are as reliable as data
collected under strict oversight of a government agency, and considers the potential of citizen science to expand
spatial and temporal coverage of water monitoring networks. The analysis hinges on comparison of data on
water temperature and dissolved oxygen (DO) in freshwater streams and rivers collected by four entities: the
United States Geological Survey (USGS) network of field scientists, the USGS network of automated sensors,
the Georgia Adopt-A-Stream volunteer water monitoring program, and the University of Rhode Island Wa-
tershed Watch (URIWW) volunteer water monitoring program. We find that volunteer-collected data exhibit the
expected relationship between temperature and DO. Furthermore, we find that volunteer- and USGS-collected
data lie in roughly the same range, although volunteer-collected DO measurements are lower on average (by
approximately 1 mg/L in Georgia and 1.8 mg/L in Rhode Island). The results indicate that volunteer-collected
data can provide reliable information about freshwater DO levels. These data could be useful for informing
water management decisions—such as deciding where to focus restoration efforts—but may not be appropriate
for applications in which highly precise data are required. We also comment on the growth potential of
volunteer water monitoring efforts. Encouraging volunteers to collect data in high-priority or undersampled
areas may help expand spatial and temporal coverage of volunteer monitoring networks while retaining high
levels of participation.
Keywords: citizen science; dissolved oxygen; rivers; streams; U.S. Geological Survey; water monitoring
Introduction
All societies and ecosystems depend on clean water to
survive and thrive. Accurate, consistent, and extensive
water monitoring enables early identification of impaired
waters. In the United States, water quality data are collected
and compiled by researchers, nongovernmental organiza-
tions, and government agencies at the local, state, and na-
tional levels. Some of these efforts are quite extensive. The
United States Geological Survey (USGS) National Water
Information System (NWIS), for instance, provides water
quality data from *1.5 million sites across all 50 U.S. states,
the District of Columbia, and U.S. territories. The USGS, the
Environmental Protection Agency (EPA), and the National
Water Quality Monitoring Council (NWQMC) jointly
sponsor the Water Quality Portal, an online repository of over
265 million observations from the NWIS and other water
quality databases.
Yet, even large federal agencies such as the USGS can
provide water quality information for only a small fraction
of the 3.5 million miles of streams and rivers and more than
3 million lakes in the United States. This creates opportuni-
ties for serious adverse impacts on human and environmental
health. Wired magazine has highlighted the huge data gaps
that make it difficult to protect areas at risk of lead poisoning
from contaminated drinking water, the problem at the heart of
the recent water crisis in Flint, Michigan (Lapowsky, 2017).
A 2016 report from the Public Policy Institute of California
revealed that about half of the watersheds identified as a
critical aquatic habitat in California lack stream gages,
making it extremely difficult to manage the state’s scarce
water resources (Escriva-Bou et al., 2016).
Citizen science is emerging as an increasingly viable way
to help fill water data gaps. The term citizen science refers to
the voluntary collaboration of interested members of the
public with trained professionals on scientific projects, often
by contributing to data collection and occasionally by as-
sisting in data analysis and experimental design (Follett and
Strezov, 2015). Greater ubiquity of Internet-connected de-
vices, availability of low-cost sensors, and support from the
*Corresponding author: Department of Civil and Environmental
Engineering, University of California, 2001 Ghausi Hall, Davis,
CA 95616. Phone: 503-752-0586; Fax: 503-752-7872; E-mail:
hannahrsafford@gmail.com
ENVIRONMENTAL ENGINEERING SCIENCE
Volume 00, Number 00, 2017
ªMary Ann Liebert, Inc.
DOI: 10.1089/ees.2017.0218
1
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academic (Rise of the citizen scientist, 2015) and govern-
ment* sectors have led to dramatic growth in the number and
scope of citizen science projects over the past two decades
(Loperfido et al., 2010; Follett and Strezov, 2015).
Volunteer water monitoring programs are one of the most
popular forms of citizen science (Deutsch and Ruiz-Co
´rdova,
2015). Today, an estimated 1,700 organizations nationwide
conduct volunteer water quality monitoring activities, en-
gaging schools, neighborhood and civic associations, fami-
lies, and other groups and individuals in collecting and
reporting water quality data (NWQMC, n.d.). Volunteer
water monitoring has attracted particular attention for its
potential to improve monitoring of environmental variables
that are not easily collected or analyzed in an automated
manner, such as the presence of waterborne pathogens
(Farnham et al., 2017).
Although the U.S. EPA encourages and provides some
grants and technical assistance to support volunteer water
monitoring, the federal government does not fund or ad-
minister a national volunteer water monitoring network.
Most large volunteer water monitoring programs are coor-
dinated by universities, cooperative extension system pro-
grams, state or local government agencies, or nonprofit
organizations (Deutsch and Ruiz-Co
´rdova, 2015). Programs
vary in the amount of assistance provided and the level of
commitment expected from participants. Some programs
lend out or subsidize equipment needed to collect water
quality data; others require volunteers to purchase their own
equipment. Some programs require that volunteers attend one
or more training sessions and agree to collect data at desig-
nated time intervals; others allow any member of the public to
participate as frequently or infrequently as desired.
Given the heterogeneity of volunteer water monitoring
programs, there may be concerns about the reliability of
volunteer-collected data and hence reluctance to use
volunteer-collected data in research projects or to inform
decision-making (Shelton, 2013; Storey et al., 2016). Vo-
lunteer monitoring programs administered at the state level
often adopt quality assurance/quality control (QA/QC) mea-
sures to help alleviate these concerns. States receiving grant
funding for volunteer water monitoring from the EPA under
section 319(h) of the Clean Water Act must follow an ap-
proved Quality Assurance Project Plan (QAPP) in accordance
with specific EPA guidelines (40 CR 31.45 and 30.54) (U.S.
EPA, 2003). Some states, including Connecticut (Connecticut
DEEP, 2013), Illinois (Illinois EPA, 2015), and New Jersey
(NJ DEP, n.d.), have established tiered approaches to vol-
unteer monitoring, under which data used for more conse-
quential applications are subject to more rigorous review.
While QA/QC measures may increase confidence in
volunteer-collected water data, the degree to which such
measures improve data quality is unclear. A growing body of
evidence indicates that given moderate training and strict
sampling protocols, amateur volunteers can collect reliable
data and make basic assessments at a level comparable with
professionals (Fore et al., 2001; Fuccillo et al., 2015; Kos-
mala et al., 2016; Farnham et al., 2017). Volunteer accuracy
varies with task difficulty and type (Kosmala et al., 2016).
Multiple studies have found that volunteers are as accurate as
professionals in monitoring benthic macroinvertebrates and
other higher-order organisms in freshwater (Loperfido et al.,
2010; Shelton, 2013; Storey et al., 2016).
The literature includes examination of the reliability of
volunteer-collected water quality data. Storey et al. (2016)
found that volunteer-collected data were highly correlated
with data collected by local government for water tempera-
ture, electrical conductivity, water clarity, and Escherichia
coli, but more weakly correlated for dissolved oxygen (DO),
nitrate, and pH. Shelton (2013) found that volunteer and
professional measurements of water temperature, pH, con-
ductivity, and discharge were largely similar, but that vol-
unteer measurements of DO were much more variable.
Canfield et al. (2002) and Hoyer et al. (2012) both found that
volunteers participating in the University of Florida’s LA-
KEWATCH program were able to collect data on a host of
water quality parameters that compared favorably with data
collected by professionals.
Previous studies of volunteer-collected water quality data
have focused on highly controlled small-scale comparisons
of data collected by volunteers alongside professionals or
of data collected by volunteers and professionals sampling
at the same or similar sites under similar conditions. This
method, while rigorous, is resource-intensive and limited to
relatively small sample sizes. We take a novel approach to
assessing the value of citizen science to freshwater moni-
toring. Specifically, we exploit the temperature dependence
of dissolved oxygen levels in rivers and streams to com-
pare data collected by participants in two of the largest and
longest-running volunteer water monitoring programs in
the country—Georgia Adopt-A-Stream (AAS) and Univer-
sity of Rhode Island Watershed Watch (URIWW)—with
analogous data collected by USGS scientists and automated
sensors. The objectives are to determine whether data col-
lected by large numbers of volunteers are as reliable as data
collected under strict oversight of an agency such as the
USGS and to gain insight into the potential of citizen science
to expand spatial and temporal coverage of water monitoring
networks.
About the Data
The analysis that follows is based on data collected from
three sources: Georgia AAS, URIWW, and the USGS
NWIS.
Georgia AAS
Georgia AAS, launched in 1992, is Georgia’s statewide
volunteer monitoring program for freshwater streams, rivers,
and lakes. Georgia AAS is part of the Nonpoint Source
Program of the Georgia Environmental Protection Division’s
Water Protection Branch and is funded by an EPA Section
319(h) grant. Georgia AAS requires that volunteers register
their monitoring group and site(s) through the program
*In September 2015, the U.S. government released an online
toolkit (available at: https://crowdsourcing-toolkit.sites.usa.gov) to
help federal employees use crowdsourcing and citizen science in
their work, and Dr. John P. Holdren, Assistant to the President for
Science and Technology and Director of the White House Office
of Science and Technology Policy, issued a memo (available at:
https://obamawhitehouse.archives.gov/sites/default/files/microsites/
ostp/holdren_citizen_science_memo_092915_0.pdf) directing the
heads of Executive Departments and Agencies to use citizen science
and crowdsourcing in addressing societal needs and accelerating
science, technology, and innovation.
2 SAFFORD AND PETERS
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website and select the type(s) of monitoring they are inter-
ested in. To participate in some of these monitoring efforts
(macroinvertebrate monitoring, chemical monitoring, bacterial
monitoring, and coastal monitoring), volunteers must attend a
free QA/QC workshop put on by Georgia AAS and participate
in annual QA/QC recertification workshops. Additional QA/
QC checks are built into the interface that volunteers use to
input their observations into the program database. Volun-
teers are encouraged, but not required, to sample at monthly,
bimonthly, or quarterly intervals depending on the moni-
toring effort(s) they are participating in. Volunteers are typi-
cally responsible for purchasing their own equipment, although
program administrators provide resources, workshops, and
professional support to assist volunteers as needed. All data
collected through the program are freely available online.
University of Rhode Island Watershed Watch
URIWW is a statewide volunteer water monitoring pro-
gram administered by the University of Rhode Island Co-
operative Extension in partnership with the State of Rhode
Island and numerous other entities. Volunteers collect data at
a variety of freshwater and estuarine sites on the following
parameters: water clarity, algal density, DO, water temper-
ature, alkalinity and pH, nutrients, and bacteria. Monitoring
sites and schedules are determined by program administrators
working with local organizations. New volunteers are re-
quired to attend an introductory training workshop, and all
volunteers are required to commit 1–2 h per week to moni-
toring activities during the active season (late April–October).
URIWW provides volunteers with all necessary equipment
and optional refresher training. A subset of data collected
through the program is freely available online, and the re-
mainder is available upon request.
United States Geological Survey NWIS
The USGS has been providing water data for the nation
since the 1800s. Data collection and processing during most
of this time were carried out by USGS scientists, which im-
posed practical limitations on the data products the agency
could make available. Data collection efforts focused pri-
marily on the daily value of hydrologic variables such as mean
discharge, water temperature, specific conductance, or water
level in wells. Streamflow records were available only as a
historical product, unavailable to the science and water
management communities until at least several months after
the fact. In 1976, the USGS began to experiment with the use
of satellites and in situ sensors to supplement manual data
collection. These technologies enabled the agency to collect
data at a much finer time step (every 15 min in many loca-
tions) and to transmit data to end users almost instanta-
neously. The number of real-time streamflow sites grew
rapidly in the following decades, from 120 in 1978 to 1,000 in
1982; 5,100 in 1999; and nearly 10,000 today (Hirsch and
Fisher, 2014). Although automated data collection now
makes up a substantial part of the USGS water monitoring
efforts, USGS scientists continue to take field measurements
by hand at sites that lack in situ sensors, on parameters that
sensors do not track, and/or to validate sensor measurements.
The USGS NWIS is an online portal that provides free
access to real-time sensor data as well as historical observa-
tions collected by both USGS scientists and sensors. Real-
time values are available for all sites from October 1, 2007,
onward and, in some cases, before 2007 because of the need
for these data at a finer time step (Hirsch and Fisher, 2014).
Field observations are available for all sites as far back as
records are available (as early as 1831 for the Kickapoo River
site in Wisconsin). NWIS currently contains close to 4 billion
historical real-time observations collected at more than
16,000 sites nationwide and historical field observations
collected at more than 70,000 sites.
Analysis
Parameters of interest
Each of the datasets described in the previous section con-
tains information on a variety of parameters. This analysis
focuses on DO and water temperature measurements. DO
concentration is a critical measure of the health of freshwater
bodies (Penn et al., 2009, p. 278). In addition to causing fish
kills and hypoxic dead zones, low DO concentrations can serve
as indicators of pollution, blocked streamflow, and other
problems in a watershed (USGS, 2017). Furthermore, DO and
water temperature were two of the most commonly tracked
parameters in each of the programs included in this study,
meaning thatsample size was large enough for robust analysis.
Finally, the relationship between oxygen solubility and water
temperature is well understood. Theoretical and empirical
equations are available for predicting oxygen solubility under
specified conditions, making it possible to compare predicted
and observed DO at a given water temperature.
Data samples
Appropriate samples were extracted from each of the base
datasets. For the Georgia AAS and URIWW datasets, the
samples comprised all observations that contained measure-
ments on both water temperature and DO. These included
measurements taken between January 1995 (the first month
when volunteers collected data under a QAPP and according to
QA/QC measures, and the first month for which data are avail-
able online) and October 2016 (the month in which the samples
were prepared) for the Georgia AAS dataset and measurements
taken between May 1992 and April 2014 (the first and last
months for which DO measurements were recorded) for the
URIWW dataset (while the URIWW program is still active,
2015 and 2016 data were not available at the time of analysis).
For the USGS field observations, the samples comprised all
observations that (1) were made on samples of surface water
(i.e., tagged with the USGS ‘‘SW’’ code); (2) were collected by
USGS entities (i.e., excluding measurements in the NWIS da-
tabase collected by the National Park Service, state government
agencies, public organizations, or any other non-USGS insti-
tution); (3) were taken over the same time period covered by the
corresponding volunteer monitoring program; and (4) con-
tained measurements on both water temperature and DO.
USGS sensor observations containing measurements on
both water temperature and DO wereavailable for Georgia,but
not for Rhode Island. The USGS sensor dataset for Georgia
comprised 1,015,300 observations collected at hourly intervals
across 88 sites from October 2007 (the first month for which
historical sensor data were available) through October 2016. A
random selection of 50,000 of these observations was extracted
to form the sample.
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For all samples, observations for which water temperature
was greater than 32C were excluded to account for extreme
measurement errors. This was selected as a reasonable esti-
mate of the maximum temperature that a shallow stream could
plausibly reach in the heat of summer. This maximum was
exceeded for less than 1% of the observations in the Georgia
USGS field and AAS datasets, and not at all for the remaining
datasets. Although a small number of apparently extreme
values were noted in the DO data, no similar maximum was
imposed as naturally occurring processes (e.g., high photo-
synthesis rates in calm waters) have been known to yield above
saturation DO levels (Wagner et al., 2006, p. 8). Extreme
negative values were not observed for either water temperature
or DO. Table 1 contains a summary of the data samples. Be-
cause participants in the URIWW program only sample from
late April to October, the range of water temperatures in the
volunteer-collected Rhode Island data sample is narrower than
the range for the equivalent Georgia data sample.
Data analysis
For the analysis, stream temperature was used as a basis
of comparison among the volunteer-collected and USGS-
collected data. As stated above, there are several sets of
equations relating DO equilibrium concentrations to water
temperature under specified conditions. One of the most
widely accepted was developed by Benson and Krause (1976,
1980, 1984). Combining thermodynamic principles and ex-
perimental data, Benson and Krause developed the following
baseline equation to predict DO concentration in water at
zero salinity and one atmosphere:
ln DOðÞ¼139:34411 þ1:575701 ·105
T
6:642308 ·107
T2þ1:243800 ·1010
T3
8:621949 ·1011
T4,
(1)
where Tis the water temperature in Kelvin and DO is DO
concentration in mg/L. Benson and Krause also developed
equations that correct for salinity and atmospheric pressure.
The Benson–Krause equations have gained wide acceptance
and have been used by the USGS since August 2011 (USGS,
2011).
Table 1. Summary of Data Samples
Dataset Number of observations Time span
Georgia Volunteer (Georgia AAS) 17,975 January 1995–October 2016
USGS field 59,004 January 1995–October 2016
USGS sensor 50,000 October 2007–2016
Rhode Island Volunteer (URIWW) 5607 May 1992–April 2014
USGS field 1014 May 1992–April 2014
USGS sensor
AAS, Adopt-A-Stream; URIWW, University of Rhode Island Watershed Watch; USGS, United States Geological Survey.
FIG. 1. Dissolved oxygen (DO) versus water temperature
data for Georgia data samples. Dots show individual data
points. The blue,green, and pink lines show the fitted curves
of the form ln (DO) ¼aþb
T

for each of the Georgia data
samples. The red line shows the predicted equilibrium DO
versus water temperature curve generated by the baseline
Benson–Krause equation.
FIG. 2. DO versus water temperature data for Rhode
Island data samples. Dots show individual data points.
The green and pink lines show the fitted curves
of the form ln (DO) ¼aþb
T

for each of the Rhode Island
data samples. The red line shows the predicted equilib-
rium DO versus water temperature curve generated by the
baseline Benson–Krause equation.
4 SAFFORD AND PETERS
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The baseline Benson–Krause equation was used to
construct a predicted curve for equilibrium DO at fresh-
water temperatures ranging from 0Cto35C. In this
analysis, pressure corrections were neglected because all
observations were collected at sites located close to sea
level. Salinity corrections were neglected because salinity
measurements were sparse or nonexistent for the datasets
examined and because nearly all of the observations in-
cluded in the data samples were taken at freshwater
streams and rivers.
Table 2. Fit Results
Dataset
a [95% confidence
bounds]
b [95% confidence
bounds]
DDO (mg/L)
1
nP
n
1
(DOiDO(Ti))
[standard deviation]
Georgia Volunteer (Georgia AAS) 7.432 [7.219–7.644] 2719 [2658–2781] -2.4072 [1.6997]
USGS field 6.497 [6.379–6.615] 2484 [2449–2518] -1.4857 [4.6444]
USGS sensor 6.671 [6.612–6.729] 2542 [2525–2559] -1.4836 [0.9821]
Rhode Island Volunteer (URIWW) 5.809 [5.177–6.440] 2235 [2051–2418] -2.7133 [1.7260]
USGS field 7.511 [6.776–8.245] 2784 [2574–2994] -0.9194 [1.7799]
USGS sensor
FIG. 3. Histograms showing the number of observations collected at each hour of the day for each of the four data
samples for which these data were available. Binning was performed by rounding times of collection down to the nearest
hour (i.e., 4:01 AM and 4:59 AM would both be tallied in the 4:00 AM bin). Note that time-of-day data were available for
only 2,546 of the 5,607 observations in the URIWW data sample. URIWW, University of Rhode Island Watershed Watch.
CITIZEN SCIENCE FOR DISSOLVED OXYGEN MONITORING 5
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Each data sample was fitted to this simplified equation:
ln DOðÞ¼aþb
T, (2)
where Tis the water temperature in Kelvin and aand bare
fitting parameters. For each data sample, the average differ-
ence between the observed and predicted equilibrium DO
values was computed according to the formula:
DDO ¼1
nX
n
1
DOiDO Ti
ðÞðÞ, (3)
where DDO is the average difference, DOiis the observed
DO value, and DO(Ti) is the Benson–Krause predicted DO
value for each observed water temperature.
To assess the potential of citizen science to expand spatial
and temporal coverage of water monitoring networks, all
observations contained in each data sample were geograph-
ically mapped as well as plotted as histograms showing the
number of observations collected each year.
Results and Discussion
Comparison of volunteer- and USGS-collected data
The first objective of this article was to determine whether
volunteer-collected data are as reliable as data collected un-
der strict oversight of an agency such as the USGS. Figures 1
and 2 contain the predicted equilibrium DO curve and the
fitted curves. Table 2 contains values for the fitted parameters
and the mean differences between observed and predicted
FIG. 4. Maps showing sampling sites for Georgia data. Each circle represents a location at which at least one observation
(containing both DO and water temperature measurements) was made between January 1995 and October 2016. The size of
the circles represents the number of observations collected at each location (note different scales used for circle size for each of
the three data samples). The locations of major cities are also marked. Clustering of volunteer monitoring sites near population
centers is likely reflective of the fact that participants in the Georgia AAS program are allowed to freely select where and how
often to sample. Animated versions of these maps are available online at https://goo.gl/z96V9Q. AAS, Adopt-A-Stream.
6 SAFFORD AND PETERS
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equilibrium DO values. The results show that the volunteer-
collected data, USGS sensor data, and USGS field data
for Georgia and Rhode Island lie roughly within the same
range. For the Georgia data, the average difference between
volunteer-collected DO measurements and USGS field
DO measurements and the average difference between
volunteer-collected DO measurements and USGS sensor DO
measurements were both -0.92 mg/L. For the Rhode Island
data, the average difference between volunteer-collected DO
measurements and USGS field DO measurements was
-1.79 mg/L. These results indicate that volunteer-collected
data can provide reliable information about freshwater
DO levels. Volunteer-collected data could be useful for in-
forming water management decisions, such as deciding
where to focus restoration efforts. However, the fact that
there is a consistent—although small—discrepancy between
the volunteer-collected data and the USGS-collected data
suggests that volunteer-collected data may not be appropriate
for applications in which highly precise data are required.
There are a number of possible explanations for the dis-
crepancies between volunteer-collected and USGS-collected
data. Some of the most plausible are discussed below.
Time of day. Photosynthesis and respiration cause diel
(24-h) variation in DO concentration in most freshwater
bodies. Water temperature serves as a partial proxy for time
of day. Within a given 24-h period, water temperature will be
higher during the daytime and lower at night, causing an
inverse trend in DO levels (Kelly et al., 2007, p. 40). How-
ever, water temperature does not account for seasonal vari-
ation in average water temperatures. A stream at 10Cona
winter day will, all else equal, exhibit higher DO levels than
FIG. 5. Maps showing sampling sites for Rhode Island data. Each circle represents a location at which at least one
observation (containing both DO and water temperature measurements) was made between May 1992 and April 2014. The
size of the circles represents the number of observations collected at each location (note different scales used for circle size
for each of the three data samples). The locations of major cities are also marked. The relatively even spatial distribution of
volunteer monitoring sites is likely reflective of the fact that participants in the URIWW program are told where and how
often to sample. Animated versions of these maps are available online at https://goo.gl/n090qE.
CITIZEN SCIENCE FOR DISSOLVED OXYGEN MONITORING 7
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the same stream at 10C on a spring night. Credible com-
parison of the data samples used in this analysis requires
consideration of the times of day at which the observations in
each sample were collected.
Time-of-day data were available for all observations in the
USGS-collected data samples and for 2,546 of the 5,607
observations in the URIWW data sample. Time-of-day data
were not available for the Georgia AAS data sample. The
histograms in Figure 3 show the frequency of observations in
each sample by time of day, rounded down to the nearest
hourly bin (i.e., 4:01 AM and 4:59 AM would both be tallied
in the 4:00 AM bin). The results show that while USGS
scientists in both Georgia and Rhode Island collected the bulk
of their data slightly later in the day than URIWW volunteers,
the time-of-day distribution is relatively consistent across all
three human-collected data samples. It is therefore unlikely
that time of day is a major contributor to the observed dis-
crepancies between the data collected by USGS scientists and
the data collected by volunteers.
The distribution of the random sample of sensor-collected
data is, as expected, much more evenly distributed across the
time-of-day spectrum. To determine whether or not the
nighttime sensor observations substantially affected the
central tendency of the data, a new random sample of 50,000
observations was generated from the subset of observations
collected between 5 AM and 7 PM (the time interval that
contains almost all of the human-collected data), and the
analysis described in the Data Analysis section was repeated.
DO levels in the daytime sensor data sample were found to be
an average of 0.1258 mg/L higher than DO levels in the
original data sample. This difference is not insignificant, but
nevertheless explains only a small portion of the observed
discrepancies between the data collected by USGS sensors
and the data collected by volunteers.
Site characteristics. The concentration of DO in a river or
stream is affected by many site-specific characteristics, in-
cluding ambient temperature and pressure, aeration, ion ac-
tivity, aerobic decomposition, ammonia nitrification, and
other aquatic chemical and biological reactions (Rounds
et al., 2013). This analysis relies on large data sample sizes to
limit the effect of random variation in site characteristics on
observed DO levels and facilitate credible comparison of
observations across data samples. We acknowledge, how-
ever, that large sample sizes cannot eliminate systemic bias
for one or more of the data samples. Perhaps volunteers more
often sample sites characterized by lower DO levels, such as
polluted waters near highly populated areas. Perhaps USGS
scientists more often sample sites with unusual characteris-
tics. More work should be done to investigate the risk of
systemic bias in site selection. As a first step, the location of
each observation contained in the data samples was geo-
graphically mapped (Figs. 4 and 5). It is clear that volunteer
and USGS field sampling sites alike cover a broad geographic
range in Georgia and Rhode Island. The regions that account
for the bulk of volunteer-collected observations are also
heavily sampled by the USGS, and the volunteer-collected
observations that report particularly low DO were taken at
many different sites. Indeed, if anything, site selection would
be expected to push average DO levels recorded in the USGS
field data sample downward because more of the USGS field
sampling sites are located in Georgia’s low-DO Coastal
Plain.
Instrumentation, sampling procedure, and personnel train-
ing. The USGS uses a variety of optical (luminescence),
amperometric, and spectrophotometric sensors to collect
both discrete and continuous DO measurements and trains its
scientists to follow rigorous calibration, sampling, and re-
porting protocols in collecting data.
{
Instrumentation used by
the USGS can be quite expensive, with individual sensors
costing thousands of dollars or more. Such detailed protocols
and expensive equipment are clearly infeasible for large-
scale volunteer monitoring programs such as Georgia AAS
and URIWW. These programs instead rely on simpler in-
strumentation and QA/QC measures. Georgia AAS and
URIWW both recommend that participants measure DO
using inexpensive, easily obtainable, iodometric (Winkler)
titration kits and provide nontechnical step-by-step manuals
to guide DO data collection.
{
Iodometric test kits are inex-
pensive and easy to obtain, making them well suited for use in
large volunteer monitoring programs. However, the USGS
has noted several problems with the iodometric method,
namely that (1) the accuracy and reproducibility achievable
are dependent on the experience and expertise of the data
collector, (2) potential environmental interferences (for ex-
ample, the presence of nitrite, ferrous and ferric iron, and
organic matter) require advanced knowledge of the chemistry
of the sample, and (3) field conditions can make preventing
FIG. 6. Map indicating locations of low-DO freshwater in
Georgia. Red lines are streams and rivers listed as impaired
by the Georgia Department of Environmental Protection for
DO violations.
{
Protocols for field DO measurements are documented by
Rounds et al., 2013; protocols for data collection from continuous
water-quality monitors are documented by Wagner et al., 2006.
{
See the Georgia AAS ‘‘Physical/Chemical Monitoring’’ manual
(available at: http://adoptastream.georgia.gov/manuals) and the
URIWW ‘‘Specific Monitoring Techniques’’ manual (available at:
http://web.uri.edu/watershedwatch/uri-watershed-watch-monitoring-
manuals).
8 SAFFORD AND PETERS
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FIG. 7. Histograms showing the number of observations collected each year for each of the five data samples. The
apparent sudden drop in the number of observations collected by URIWW participants reflects the time range of the data
sample.
CITIZEN SCIENCE FOR DISSOLVED OXYGEN MONITORING 9
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exposure of the sample to atmospheric oxygen difficult
(Rounds et al., 2013). Indeed, Georgia AAS staff have ob-
served that even when performed by trained personnel, DO
measurements made using Winkler titration tend to read
lower than measurements made on identical samples using
more sophisticated equipment (Harold Harbert, personal
communication, June 1, 2017).
The discrepancy between volunteer-collected data and
USGS-collected data is larger for the URIWW program. This
is notable because URIWW provides stricter oversight of
volunteers. URIWW specifies monitoring sites and sched-
ules, while Georgia AAS does not. URIWW also requires
volunteers to commit to regular participation, while Georgia
AAS allows volunteers to sample as frequently or infre-
quently as they wish. The fact that there is still such a large
discrepancy between USGS-collected data and data collected
by URIWW volunteers suggests that modest increases in
training and supervision have little or no effect on volunteer
ability to accurately perform tasks (such as carrying out io-
dometric titration) characterized by relatively large oppor-
tunities for human error. This is consistent with previous
findings (e.g., Kosmala et al., 2016) that volunteer accuracy
is correlated with task difficulty. More research should be
done to determine the extent to which significantly more
rigorous training, more detailed sampling protocols, and/or
more sophisticated instrumentation can improve volunteer
accuracy in DO monitoring.
Comparison of observed and predicted equilibrium DO
The results show that all three data sources report consis-
tently lower DO than the temperature-dependent equilibrium
prediction, with the difference largest for volunteer-collected
data. This difference is likely attributable to environmental
factors not represented in the Benson–Krause model. In
Georgia, for instance, many streams and rivers lie in the
state’s southeastern Coastal Plain region. The Coastal Plain is
characterized by slow-moving water bodies that lie adjacent
to wetlands and have high sediment oxygen demand and
concentrations of organic matter—conditions that result in
low DO (Todd et al., 2007). Indeed, as shown in Fig. 6, most
of the streams and rivers listed as impaired by the Georgia
Department of Environmental Protection due to DO viola-
tions lie in the Coastal Plain. In Rhode Island, conditions are
not as conducive to naturally low DO (hence the smaller
mean difference between USGS field observations and pre-
dicted values in Rhode Island relative to Georgia), but sedi-
ment oxygen demand, the presence of dissolved organic
matter, and other complicating factors may still cause ob-
served DO values to be lower than predicted.
Spatial and temporal distribution of observations
The second objective of this article was to assess the po-
tential of citizen science to expand spatial and temporal
coverage of water monitoring networks. Figures 4 and 5 show
the spatial distribution of volunteer sampling sites. In Geor-
gia, results are mixed. The spatial distribution of volunteer
sampling sites is much greater than the spatial distribution of
USGS sensor sites, but is still concentrated near urban areas
and not as great as the spatial distribution of USGS field
sampling sites. The number of observations per site also tends
to be smaller for volunteer sampling sites than for USGS field
sampling sites and much smaller than for USGS sensor sites.
In Rhode Island, there is less overlap between volunteer and
USGS field sampling sites, and the number of observations
per site is about the same. It is also notable that Georgia
AAS and URIWW have grown over time. The histograms
contained in Fig. 7 show a steady increase in the number of
observations collected by volunteers in both programs,
and the map animations
x
show that this trend has been
driven not only by increases in the number of observations
per site but also by increases in the number of sites being
sampled.
These results suggest that when volunteers are allowed to
freely select where and how often to sample (as is the case for
Georgia AAS), monitoring sites will be clustered close to
population centers and will tend to be sampled less frequently
over time, and a relatively large number of people will par-
ticipate. When volunteers are told where and how often to
sample (as is the case for URIWW), the spatial and temporal
distribution of observations will be more even, but partici-
pation will be more limited. A hybrid of these approaches,
such as a volunteer monitoring program in which participants
are encouraged, but not required, to collect data in high-
priority or undersampled areas, could provide a valuable
complement to USGS field monitoring networks and to
support scientific and management efforts for which periodic
observations are sufficient. This is especially important given
that the number of observations collected by USGS scientists
in the two states has remained stagnant over the past two
decades. Further work should be done to assess the potential
of citizen science to support applications for which contin-
uous data are required.
Author Disclosure Statement
No competing financial interests exist.
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CITIZEN SCIENCE FOR DISSOLVED OXYGEN MONITORING 11
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... Many of these volunteer monitoring programs have created large and extensive long-term data sets. Some federal, state, and local groups in the United States embrace the water quality data collected by volunteers, using it for science and management (Hoyer et al. 2014, Quinlivan et al. 2020b, Thornhill et al. 2019, Krabbenhoft and Kashian 2020), but there are agencies and scientists that still do not trust the quality of volunteer-generated data (Loperfido et al. 2010, Burgess et al. 2017, Safford and Peters 2018. To address these concerns, many volunteer monitoring programs have conducted studies comparing data collected by their volunteers with professional data collected during the same time period and location (Stepenuck et al. 2011, Dyer et al. 2014, Storey et al. 2016, Albus et al. 2020a. ...
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