This study assessed the baseline water quality and ecology of 7 streams near Geneva Marsh in Crawford County, Pennsylvania before construction of a proposed tire burning plant. Land use in the watersheds was analyzed using GIS and remote sensing. Water quality parameters like phosphorous and macroinvertebrate communities were assessed. Results showed urban land use decreased phosphorous and agricultural land use decreased sensitive macroinvertebrate taxa. The 3 streams with the highest agricultural and urban land use (Towpath, Shafer Run, and Williams Run) were identified for further monitoring after plant construction.

1. Baseline Water Quality in Headwater Streams Near Geneva Marsh Preconstruction of Tire
Burning Plant in Crawford County Pennsylvania
Authors: Joseph Phelps, Ian Dempsey, Emma Fraser, Jared Balik and Prof. Casey Wilson
Stream Ecology Course
Environmental Science Department
520 North Main Street
Allegheny College
Meadville, PA 16335
ABSTRACT
This study was conducted in conjunction with Crawford Area Residents for the Environment (CARE) to
assess the current quality of streams along Geneva Marsh in light of current land-use/land-cover (LULC)
data. The goal was to provide CARE with credible baseline data that substantiates legal claims made
against a proposed tire-to-energy electric generating facility due west of Geneva Marsh, which CARE
deems a threat to ecosystems in the area. Watershed LULC data were assessed using Global Information
Systems (GIS) technology and remote sensing. Seven streams were assessed for physical and chemical
quality, as well as for diversity of benthic macroinvertebrate assemblages, specifically intolerant taxa.
Results indicted urban land use in the area to be a significant factor contributing to decreased soluble
reactive phosphorous (SRP). High agricultural land use was determined to be a cause of decreased
sensitive (EPT) taxa abundance and overall decreased diversity of assemblages. Additionally, high water
pH decreased EPT abundance and increased tolerant taxa abundance. We suggest Towpath, Shafer Run,
and Williams Run watersheds to be monitored closely after construction of the tire plant. More studies
should be conducted using different aquatic assemblages at different sites to give a clearer picture of
water quality impacts.
INTRODUCTION
Anthropogenic activity can have measurable impacts on fluvial ecosystem functioning via
alteration to naturally-occurring abiotic (i.e., temperature, flow, nutrients, light, etc.) and biotic (i.e.,
macroinvertebrate and fish) dynamics. Excessive nutrient additions (e.g., nitrogen and phosphorous) to
lotic systems has frequently been associated with degraded stream and other aquatic habitats through
alteration of food web dynamics and other nutrient concentrations, such as dissolved oxygen (DO), in a
process called eutrophication (Ashton et al., 2014; Chambers et al., 2012; Figueroa-Nieves et al., 2006;
Robertson et al., 2006). By changing certain hydrologic aspects of a stream with varying types of land
cover (“natural”/undisturbed versus urban/agricultural) in varying proportions, land cover can have a
measurable impact on different natural flow regime components (Chang et al., 2011; Poff and
Zimmerman, 2010). Land use legacies will also play a major role in determining the state of streams and

2. whether their impacts are specific to that stream or translatable to other streams with similar regional
characteristics.
Increases in agricultural and developmental land cover (e.g., deforestation, pesticide runoff, etc.)
are associated with changes to channel geomorphology, increased temperature via loss of riparian buffers,
and overall loss of habitat and species diversity (Poff et al., 1997; Allan, 2004). For example, urbanization
can result in less diverse and more tolerant stream macroinvertebrate assemblages via increased sediment
transport, reduced stream bed sediment size and increased solutes (Roy et al., 2003). The natural flow
regime is important for maintaining a “dynamic equilibrium” of physical and biological components of
lotic systems, which increases buffering capacity and thus biological integrity by offering a diverse array
of habitat niches (Karr 1991; Poff et al., 1997; Poff and Zimmerman, 2010). Species extirpations and
disruptions between aquatic, riparian, and terrestrial species that have occurred as a consequence of
natural flow alteration have also proven to be socially (i.e., human health impacts) and economically
detrimental (Poff et al., 1997; Sweeney et al., 2004).
Nonpoint and point source pollution resulting in air and direct water contamination is the largest
current threat to aquatic ecosystem assemblages, namely in terms of species diversity and ecosystem
functioning (Chambers et al., 2012; Roy et al., 2003). Air pollution (e.g., smoke, gases, heavy metals,
VOCs, PAHs, etc.) can directly affect watersheds via deposition into streams while also posing a threat to
human health (Ziadat and Stood, 2014). Globally, degraded stream ecosystems are responding to
synergistic environmental and anthropogenic stressors. Various methods of comparison (i.e., acute
toxicity indices, index of biological integrity or IBIs, riparian, channel and environmental data, or RCE
habitat evaluations, etc.) between “healthy” and degraded lotic systems are being employed by non-profit
organizations and scientific communities alike to assess the biological integrity of streams threatened with
nonpoint and point source pollutants (Hope, 2012; USEPA 2012). Sensitive macroinvertebrates are the
first to respond to water quality or other types of ecosystem disturbance and are thus used to assess
marginal ecosystem changes resulting from various LULC impacts. The absence of sensitive groups, such
as “EPTs,” or mayfly (Ephemeroptera), stonefly (Plecoptera), and caddisfly (Trichoptera) taxa, can

3. indicate degraded waters, and an abundance of tolerant species (e.g., Chironomid midges (Order Diptera)
or tubifex worms (Family Tubificidae)) may therefore also indicate degraded waters. The condition of a
stream can therefore be determined by sampling macroinvertebrates. Considering the riparian zone
around a stream also helps to determine and mitigate possible non point-source pollution. Riparian zones
of good quality have a few meters thick of vegetation, usually grasses, shrubs and trees. The roots of the
vegetation not only hold the stream banks together to help prevent erosion, but they also filter runoff from
the land beyond the stream. Poor riparian zones allow sedimentation and runoff possibly containing
excess nutrients or other chemicals to run directly into the stream. Riparian zones tie directly to land
cover and land use surrounding a stream. The land cover determines the amount of runoff flowing into a
stream. For example, a field of row crops would have less water uptake by roots than forested land. Land
use also determines the amount of runoff (amount of impervious surfaces) as well as the types of
pollution (fertilizers, salts, contaminated water from natural resource extraction, sewage or other organic
matter). All of these factors are important to consider when collecting baseline data and determining the
health of a stream or watershed.
In Crawford County, Pennsylvania, construction for a tire-to-energy electric generating facility
was announced in December of 2007 by Crawford Renewable Energy (Myers, 2013). In response to
redevelopment of the former International Paper site, Crawford Area Residents for the Environment
(CARE), an organization formed with support of Keep Erie’s Environment Protected, has been concerned
about the development of the tire burning facility and has additionally requested Crawford Renewable
Energy to provide emissions and discharge data, testing protocols and results, and information on permit
violations regarding the ongoing construction of the plant (Myers, 2013). CARE not only objects to the
plant on the basis of air pollution but holds doubts about the validity of numbers submitted on the air plan
approval application now being reviewed by the Pennsylvania Department of Environmental Protection
(Spicer, 2014).

4. The French Creek Watershed is one of the most ecologically diverse and important stream
systems in the Eastern United States. It is home to 27 species of mussels and 89 species of fishes, 15 of
those being darters, which are sensitive to stream changes. Understanding biodiversity (Karr et al. 1991)
is a key factor in determining the flow regime and water quality/chemistry of not only the French Creek
Watershed but the Allegheny River, Ohio River and ultimately the Mississippi River which empties into
the Gulf of Mexico. This implies that the water quality of French Creek has a direct effect downstream
rivers and watersheds. Increased understanding into the importance of the French Creek watershed (as
well as stream systems nationwide) has sparked dozens of local, state, legislated and grassroots
conservation organizations which aim to improve current water quality and prevent it from degrading any
further (Poff and Zimmerman et al. 2013).
Baseline data is very important to have in any field of research, especially environmental
protection and conservation. Baseline data documents environmental conditions before significant change
occurs, so that change, usually due to anthropogenic sources, can be identified, measured and assessed.
Biomonitoring practices are used to detect the health of a stream at its current state.
Our study was conducted as a watershed assessment based on existing LULC practices to provide
baseline data of nearby streams before the construction of the tire burning facility. Our objectives were
(1) to provide current land use information on sub watersheds of French Creek near the plant that empty
into Geneva Marsh by sampling water quality as a result of current point and nonpoint source pollution;
(2) to give an accurate portrayal of physical (stream and riparian habitat assessment) and biological (i.e.,
existing macroinvertebrate taxa) characteristics of sites in order to supplement water quality data.

5. METHODS
i. Site Analysis
Seven, 1st order (headwater) streams were selected for this study and were found to be in close
proximity to the site of the proposed tire burning facility. Each watershed boundary was delineated using
topographical maps, and site boundaries were quantified in terms of area with Global Information
Systems ( ArcGIS) technology software ArcGIS 10.3. Streams entering Geneva Marsh through both the
north and south were selected for the purposes of collecting baseline data for both water and air pollution
from westerly winds. A Riparian, Channel, and Environmental (RCE) Inventory (Appendix 1) was used
to assess the physical and biological condition of small streams (2nd
to 4th
order). Land-use/land-cover
(LULC) analyses were conducted via remote sensing of aerial photographs and GIS technology to
Figure 1. Watershed delineation map indicating sampling sites along Geneva Marsh located in Western Pennsylvania. Left to
right, Shafer Run, Game Lands Run, Towpath Run, and Kebert Run all enter Geneva Marsh from the north. Williams Run,
Marsh’s End Run, and Rock Creek all enter Geneva Marsh from the south. Many of these sites were officially un-named and
therefore given names for the purpose of this study.

6. delineate the percentage of each LULC type (Forest, Developed, Row Crops, Pasture/Hay, Wetlands, and
Waterbodies) within each watershed.
ii. Water Chemistry
Water samples were collected from each stream to quantify baseline data sets for total stream
hardness (Hach Total Hardness Kit Model #HA71A); dissolved oxygen (DO) (Hach Dissolved Oxygen
Test Kit Model #OX-2P); pH (Fisher Accumet Model #10); conductivity (TDS); alkalinity using standard
titrations; and nitrogen using ultraviolet spectroscopy. Based on the optical density (OD) of the samples,
soluble reactive phosphorous (SRP) was measured via “molybdenum-blue” spectroscopy (Strickland and
Parsons, 1968).
iii. Macroinvertebrate Sampling
Using D-frame kick net techniques, macroinvertebrates were collected in Spring of 2015 (in
addition to water samples) from Shafer Run and Game Lands on February 26; Towpath Run and Kebert
Run on March 12; and Williams Run, Rock Creek, and Marsh’s End on March 26. Specimens were
preserved in 70% ethanol solution, sorted and identified to Family under a dissection scope at Allegheny
College. An Index of Biological Integrity (IBI) was used to identify and calculate tolerant and intolerant
groups (Table 1). For the purpose of this study, tolerant groups consisted Chironomidae, or non-biting
midgles (Order Diptera) and Annelida (i.e., tubificid sludge worms). Our intolerant groups consisted of
Ephemeroptera, Plecoptera, and Trichoptera (EPT) taxa.

7. Metric Rating Criteria
5 3 1 0
Total taxa >20 11-20 ≤10 0
Mayfly taxa >3 2 - 3 1 0
Stonefly taxa >3 2 - 3 1 0
Caddisfly taxa >3 2 - 3 1 0
% EPT1 ≤50% 25-49% 10-24% <10%
% Tolerants2 <10% 10-25% 25-35% >35%
iv. Statistical Analysis
All statistical analysis was completed using Statview 5.0.1 software. Simple linear regressions
were used to model relationships between chemistry variables, IBI scores, RCE scores, and watershed
land-cover. As a measure of model quality, a post-hoc regression analysis of variance (ANOVA) was
applied to each test. To standardize the data, land-cover values were expressed as percentages of their
total watershed area. Additional tables/graphs were created in Microsoft Excel 2010.
Table 1. Values used for macroinvertebrate Index of Biological Integrity (IBI) at each site. Note:
1
Does not include hydropsychid
caddisflies;
2
only tolerant taxa used (i.e., red chironomid midges and annelids). Score were rated using the following scales: Good (30
– 24), Average (23 – 15), and Degraded (<15).

8. RESULTS
Consideration was given to LULC ratios when interpreting regression analyses, and thus serves as
a determinative data set regarding linear correlations recorded in this study. LULC findings (in hectares)
for each site from maps produced with GIS technology indicated a dominating trend of forest cover
among selected watersheds (Figure 2). Additionally, aerial photographs showed that most sites had more
agricultural (row crop and pasture/hay) land cover than developed land cover (Figure 2).
Among the seven sampling sites IBI indices showed 2 sites considered degraded, 4 were
considered average, and 1 was considered in good condition (Table 2). Towpath, which had the highest
percentage of agricultural and developed land cover and the lowest percentage of forest land cover,
exhibited the lowest benthic IBI score out of all sites (Table 2). This score was followed by Williams,
Shafer, and Game Lands, which also had either low total taxa collected or low sensitive (EPT) to tolerant
ratios (Table 2) coupled with considerable agricultural or urban LULC types (Figure 2).
0
10
20
30
40
50
60
70
80
90
100
Williams Towpath Shaffer
Stream
Marsh's
End
Game
Lands
Kebert
Run
Rock
Creek
%Land-Use/Land-Cover
Forest
Developed
Row Crops
Pasture/Hay
Wetlands
Waterbodies
Figure 2. Percentages of land-use/land-cover (LULC), showing ratios of natural land cover (ha) and human activity per total area for
each watershed site using remote sensing and GIS technology.

9. Stream
Benthic
IBI Score
Total
Taxa
Mayfly
Taxa
Stonefly
Taxa
Caddis
Taxa
% EPT
%
Tolerants
Williams 14 8 1 2 3 77.42% 22.58%
Rock
Creek
22 13 3 2 3 94.86% 1.61%
Marsh's
End
22 10 2 2 8 71.43% 16.33%
Towpath 8 8 0 3 1 25.86% 80.00%
Shafer 15 13 2 2 3 44.44% 53.89%
Game
Lands
18 10 1 3 5 71.88% 25.00%
Kebert 26 16 4 5 4 81.36% 13.56%
The Riparian, Channel, and Environmental (RCE) Inventory indicated that among the seven
sampling sites, zero were deemed Excellent, 3 Very Good, 3 Good, and 1 Fair (Table 3). Sites that were
sampled earlier in the season were more difficult to evaluate due to high snowfall, but most sites had
quality riparian buffers and were not in close proximity to agricultural or intense urban development.
Stream RCE RCE RCE
Riparian 1 - 4 Channel 5 - 12 Total
Williams 85 120 205
Rock Creek 60 140 200
Marsh's End 40 71 111
Towpath 95 130 225
Shafer 65 95 160
Game Lands 110 105 215
Kebert 50 130 180
Regression analyses indicated a significant decrease (p = 0.05 at α = 0.05) in the concentration of
soluble reactive phosphorous (SRP) with an increase in the percentage of LULC developed land (Figure
3). A biologically significant trend (p = 0.07) was found regarding the percentage of LULC row crops and
the percentage of total mayfly (Ephemeroptera), stonefly (Plecoptera), and caddisfly (Trichoptera) (EPT)
Table 2. Benthic IBI scores for each sampling site. Score were rated using the following scales: Good (30 – 24), Average (23 –
15), and Degraded (<15).
Table 3. Riparian, Channel, and Environmental (RCE) Inventory scores for each sampling site. Scores were rated using the
following scales: Excellent (273-340), Very Good (204-242), Good (134-203), Fair (66-133), and Poor (15-65).

10. versus total macroinvertebrate taxa collected. Figure 4 shows that as the percentage of row crops
decreased among sampling sites, the percentage of EPTs collected increased.
A biologically significant trend (p = 0.3) was found regarding the percentage of row crops and
Index of Biological Integrity (IBI) scores. As the percentage of row crops decreased among sampling
sites, IBI scores increased (Figure 5). Additionally, as the percentage of row crops, the percentage of
tolerant taxa, such as Diptera (red chironomid midges) and Annelida (tubificid worms), increased (p =
0.55), indicating biological significance (Figure 6).
The effects of pH on aquatic assemblages in streams were found to be statistically significant in
this study. Regression analyses from Figure 7 indicated a significant negative correlation between pH and
the percentage of EPTs collected. Moreover, regression analyses from Figure 8 indicated a significant
positive correlation between pH and the percentage of tolerant macroinvertebrate taxa collected.
-5
0
5
10
15
20
25
30
35
40
45
%RowCrops
.2 .3 .4 .5 .6 .7 .8 .9 1
% EPT
Y = 43.573 - 40.887 * X; R^2 = .521
Regression Plot
4
6
8
10
12
14
16
18
20
22
24
26
SRPug/L
-2 0 2 4 6 8 10 12 14 16 18
% Developed Land
Y = 24.79 - .927 * X; R^2 = .62
Regression Plot
Figure 3. Regression analysis indicating a (-) correlation (p =
0.0456) between % developed land and soluble reactive
phosphorous (SRP). R
2
= 0.620.
Figure 4. Regression analysis indicating biologically
significant (-) correlation (p = 0.0671) between % row crops
and % of Ephemeropta, Plecoptera, and Trichoptera (EPT)
macroinvertebrates collected.
R
2
= 0.620.

11. Figure 5. Regression analysis indicating a biologically
significant (-) correlation (p = 0.3146) between % row crops
and Index of Biological Integrity (IBI) scores. R
2
= 0.200.
Figure 6. Regression analysis indicating a biologically
significant (+) correlation (p = 0.550) between % row crops
and % tolerant macroinvertebrates collected. R
2
= 0.554.
-5
0
5
10
15
20
25
30
35
40
45
%RowCrops
6 8 10 12 14 16 18 20 22 24 26 28
IBI Score
Y = 33.855 - .984 * X; R^2 = .2
Regression Plot
-5
0
5
10
15
20
25
30
35
40
45
%RowCrops
0 .1 .2 .3 .4 .5 .6 .7 .8 .9
% Tolerant
Y = 5.129 + 36.66 * X; R^2 = .554
Regression Plot
Figure 7. Regression analysis indicating a (-) correlation (p =
0.0225) between pH and % of Ephemeropta, Plecoptera, and
Trichoptera (EPT) macroinvertebrates collected. R
2
= 0.680.
8
8.2
8.4
8.6
8.8
9
9.2
9.4
pH
.2 .3 .4 .5 .6 .7 .8 .9 1
% EPT
Y = 9.262 - 1.335 * X; R^2 = .68
Regression Plot
8
8.2
8.4
8.6
8.8
9
9.2
9.4
pH
.2 .3 .4 .5 .6 .7 .8 .9 1
% EPT
Y = 9.262 - 1.335 * X; R^2 = .68
Regression Plot
Figure 8. Regression analysis indicating a (+) correlation (p =
0.0153) between pH and % tolerant macroinvertebrates
collected. R
2
= 0.723.

12. DISCUSSION
This water quality assessment was conducted to assist environmental protection groups in
opposition of a proposed tire-to-energy electric generating facility, and it has provided a suitable baseline
data set that can be expanded upon and refined in later studies of the potentially affected watersheds.
Although Towpath and Williams Run received the lowest benthic IBI scores (Table 2), they had some of
the highest RCE scores (Table 3), indicating the possibility of nonpoint source pollution from surrounding
development, especially agricultural LULC types. These two streams should be highly monitored after
construction of the power plant. A study conducted in Michigan (Roth et al., 1996) concluded that basin
land use was a prime determinant of stream water quality, indicating a negative correlation between
agricultural land use and IBI scores. Another stream with confounding scores was Marsh’s End, which
received the lowest RCE value (Table 3) but the highest benthic IBI score (Table 2) which could indicate
lesser impact of nonpoint source pollutants on benthic macroinvertebrates as a result of relatively low
agricultural land cover (Figure 2). This trend coincides with data collected from Shafer, which had the
second lowest RCE value (Table 3) and a marginally degraded benthic IBI score (Table 2) coupled with
relatively high agricultural and urban land cover (Figure 2). This stream should also be highly monitored
after construction of the power plant.
IBI and RCE scores were taken into consideration when interpreting regression analyses on a
regional scale (i.e., among all sampling sites), especially those related to agricultural/urban LULC values
and pH. A statistically significant negative correlation (p = 0.0456) was found between the concentration
of soluble reactive phosphorous (SRP) and the percentage of developed land within all watersheds (Figure
3). At first this trend appears to be confounding (high percentages of agricultural land cover also existed
in areas with development), but upon further analysis of LULC values it is possible that watersheds with
high percentages of developed land had altered the natural flow regime by changing minimum and
maximum flows in addition to increased flashiness from channelization and more likely, higher degrees
of impervious surfaces (Poff et al., 1997; Poff et a., 2006). During high flow events, these areas may have

13. deposited excess runoff into riparian buffer zones or may have swept sticky phosphorous downstream
after sediment loading events (Jones et al., 2001).
A biologically significant negative correlation (p = 0.0671) was found between the percentage of
sensitive (EPT) taxa collected and the percentage of row crops (Figure 4). There was also biological
significance found between decreased percentage of row crops with increased IBI scores (p = 0.3146) and
increased percentage of row crops with increased percentage of tolerant taxa collected (p = 0.550), all
three of which report the same trend (Figures 5 and 6). This correlates to numerous studies regarding
agricultural land cover and sensitive versus tolerant taxa ratios. Earlier analyses of sites throughout the
United States have found that streams draining agricultural runoff versus forest runoff are more likely to
result in degraded aquatic habitat and lower abundance of sensitive versus tolerant macroinvertebrates
(Omernik, 1976). The amount of agricultural land use in a given watershed is positively correlated with
the concentration of soluble nitrogen and phosphorous and thus eutrophication in streams (Allan, 2004;
Figueroa-Nieves, 2006; Chambers et al., 2012).
Significant results regarding pH were found in Figures 7 and 8, which indicated that a decreased
pH (on a scale of 8 to 9.4) resulted in an increased percentage of sensitive (EPT) taxa (p = 0.02) and that
an increased pH resulting in an increased percentage of tolerant taxa (p = 0.02). This indicates sensitive
taxa were more abundant in waters that were closer to a pH of 7, whereas tolerant were able to survive in
water with pH greater than 7. This alkaline system can be a result of naturally-occurring dolomite or
calcite deposits leaching into water from nearby soil erosion via agricultural land use and limestone from
urban development (Curtis et al., 1986; Keener and Sharpe, 2005). Further decreases in pH from the
development of the tire burning facility may require increasing acid neutralizing capacity (ANC) via
limestone remediation projects to protect Geneva Marsh and adjacent headwater stream assemblages from
diversity loss (Keener and Sharpe, 2005).
This study gives clearer insight into the current quality of Geneva Marsh, a rich 500+ acre
ecosystem that is extremely valuable to many breeding and migrating birds, and its surrounding
headwater streams (Gross and Korber, 2011). Further studies may focus on improving data collection

14. from this assessment by examining water quality and aquatic assemblages in other streams. Different
habitat evaluations, such as a Water Quality Habitat Network Assessment (WQHNA), and more extensive
testing for soluble levels of nitrogen, phosphorous, dissolved oxygen (DO), and total dissolved solids
(TDS) with higher sample sizes may prove beneficial. Fish should also be sampled and fish diversity
indices calculated to gain a better understanding and monitoring of long-term changes in these headwater
streams.
The goal is to gather as much data on the streams as possible but also to evaluable the current
condition of Geneva Marsh itself, which holds a great variety of different aquatic assemblages not
included in this study. It will be important to see the gradient of human impact as one travels from
headwaters the mouth of each stream and into the center axis of Geneva Marsh. To do this, different IBI
indices should be used, such as other sensitive taxa (Megaloptera, Molluska, etc.) and taxa used for
diversity counts (Coleoptera, Hemiptera, Odonata, Crustacea, etc.) year round. Overall, the Allegheny
College ES 344 stream quality assessment team encourages further testing to be conducted with the
synergistic effects of current land use in mind. Even streams with seemingly higher quality can quickly
become degraded when terrestrial land use is changing within the watershed and altering ground water
and surface run-off that can cause non-point source pollution in these headwater streams, Geneva Marsh
and the French Creek watershed.
Acknowledgements:
This project was conducted in Allegheny College's Stream Ecology course, and would not have been
possible without every student's hard work and participation throughout the entire semester. The authors
listed were a part of this course and contributed to the final written report, however, every student in the
course worked on this project and should be sincerely thanked. Additional students include: Corey R.
Baumgardner, Ronald A. Borne, II , Naisy E. Flannery , Ryan L. Koerbel, Charles J. Miller, Josie R.
Niovich, Kirsten A. Oravec, Meghan E. Pierce, Julia M. Schock, Joseph W. Schultz, Katherine R.
Snively and Kaitlin M. Walsh.

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17. Appendix 1: RCE Data Sheet Used for Habitat Evaluations.