nguyen t final research paper

8/6/2019 Nguyen T Final Research Paper

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Tuong Nguyen Fecal Coliform in Areas with Impervious Cover 1

Is the presence of coliform bacteria in waterbodies a result from urbanized area

that has impervious cover?

19 April 2011

Tuong Nguyen

Working group #4: Focus on the rapid increase of impervious land cover due to

urbanization and how it affects water resources around Sunset Valley, TX


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4.1Abstract

The population of Sunset Valley, TX has been increasing over the past decades,

resulting in rapid increase in urbanization and residencies. The first thing that was

altered is land use, which leads to the change in land cover. This affects other

resources such as water quality. I am trying to find a connection between the effect of

increasing urbanization and its impact on water quality. I tested for fecal coliform

population in waterbodies around Sunset Valley and compared that to the percent of

impervious cover. My findings were not as good as I had hoped. I was unable to make

any connection between the percent impervious cover and fecal coliform population due

to many limitations that arose.

4.2 Introduction

The 14th Edition of Standard Methods defines total coliform as a group of

bacteria which is aerobic and facultative anaerobic, gram-negative, nonspore-foaming,

rod shaped bacteria which ferment lactose with gas formation within 48 hours at 35C.

This definition includes the following bacteria: Escherichia coli, Escherichia aurescens,

Escherichia freundii, Escherichia Intermedia, Aerobacter aerogenes, and Enterobacter

cloacae. These bacteria are found in the intestines of warm blooded animals, thus are

present in surface waters, vegetation, soils, and sewage. This group of bacteria is

intended to be an indicator of fecal contamination. The indicator organism by itself is not

considered directly harmful to man or animals, however, its presence usually indicate

the existence of pathogenic or disease-causing organisms. There are problems with


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using the total coliform group as an indicator. Only bacteria of the coliform group that

are of fecal origin may be used for analyses. Fecal coliforms are one of those bacteria

that are of fecal origin, but human feces also include other types from the total group

which are regarded as non-fecal segments of the coliform group. Fecal coliform can be

isolated from other coliforms bacteria by setting temperatures higher than 35C during

incubation. Fecal coliform have the ability to ferment carbohydrates at 44.5C within 24

hours while the rest of the total coliform group will not (Greenberg et al. 1976).

My working group focused on the rapid increase of impervious land cover due to

urbanization and its effects on water resources. My duty is to assess whether or not an

increase in impervious cover affects the population of fecal coliform. According to the

U.S. Census Bureau, the population of City of Sunset Valley, TX has almost tripled from

365 in 2000 to 840 in 2009. The rapid increase of demographics leads to the increase in

urbanization such as more roads and shopping malls. The development of this city is

ideal for my research because I want to address if fecal coliform levels in waterbodies

correlate with increasing level of urbanization. This question is important to ask because

the presence of these organisms, good or bad, has an impact on water quality. Increase

in impervious cover due to urbanization changes the quality of streams. In addition to

imperviousness, runoff from urbanized surfaces as well as municipal and industrial

discharges results in increased loading of nutrients, metals, pesticides, and other

contaminants to streams. These changes result in consistent declines in the richness of

organisms in urban streams (Meyer and Paul 2001). I tested the relationship between

impervious cover percent and fecal coliform population. It is important to know what is

present in the water and what methods are needed to control its contamination level.


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The appearance of fecal coliform in water may not be directly harmful (Erickson and

Doyle 2006); however, it is important to monitor its presence because it may indicate

that the water has been contaminated with the fecal material of humans or other

animals. Fecal coliform bacteria can enter rivers through direct discharge of waste from

mammals and birds, from agricultural and storm runoff, and from human sewage. There

has been some research in the past concerning factors that contribute to the population

of fecal coliform. Higher pH correlated to the lower concentration of coliforms (Chordi et

al. 1991) and higher fecal coliform densities are associated with discharges from

wastewater treatment plants (Hendricks and Psaris 1981).

Here I examine the effect of land cover due to urbanization on fecal coliform

population. I observed the sensitivity of the coliform bacteria to impervious cover

percentages. I took equal water samples at three different sites with different impervious

cover. I quantified and compared the population of the fecal coliform bacteria to the

level of imperviousness of the surrounding land at that particular location. To see if

there is any other correlation to the fecal coliform population other than land cover, I

took note on the pH level, the oxidation reduction potential, and the temperature of the

water sample. Since E. coliis a good fecal coliform indicator, I counted them as a

representation of fecal coliform for this project. The U.S. Environmental Protection

Agency (EPA) determines that the geometric mean ofE. colishould not be over 126

CFU(colony-forming units)/100 mL and not to exceed 235 CFU/100 mL (USEPA 2003)

for healthy recreational water.


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4.3 Materials and Methods

There are three different methods in detecting fecal coliform in water. The

methods are S-MF (standard aerobic incubation of membrane filters), A-MF (anaerobic

incubation of membrane filters), and MPN (most probable number method). S-MF and

A-MF are based on direct estimation while MPN is based on statistical analysis. The S-

MF and A-MF techniques are more sensitive than the MPN technique for counting the

total number of coliforms (Cangamella et al. 1988).

The procedures in each method are not simple. Due to limited resources and

time, I used an alternate method involving the Coliscan Easygel. The Coliscan is

patented by Micrology Laboratories in Goshen, IN. This method incorporates two

special chromogenic substrates which are acted upon by the presence of the enzymes

galactosidase and glucuronidase to produce pigments of contrasting colors. All I have to

do to identify the presence and numbers of coliforms is to add the sample water into the

medium, pour it in a petri dish and incubate it at a controlled temperature. General

coliforms will produce the enzyme galactosidase and the colonies that grow in the

medium will be in pink. E. coliwill produce both galactosidase and glucuronidase and

will produce a dark blue to purple colonies in the medium. I only have to count the

blue/purple colonies which indicate the number ofE. coliper sample. The pink colonies

were omitted because they indicate the general coliform. Any non-colored colonies are

not coliforms, but may come from the family Enterobacteriaceae. Because the Coliscan

contains inhibitors, most other bacterial types did not grow. The results were available

after 24-28 hours of incubation.


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Figure 1: Shows the Coliscan bottle and a sample petri dish after incubation. Note that

the E. coliis in purple-blue color.

I carried out my experiment from March of 2011 to April of 2011. I collected a

total of 60 mL water sample from each site. The original plan was to take water samples

right after rain where runoff carries sediments, nutrients, and other contaminants along

with it into drains and ponds. I wanted to take water samples at the same sites as

Michael. Unfortunately there was not enough rain thus all the water was dried up when I

arrived at the sites. I was fortunate enough to find water from sites in and around

Sunset Valley (Figure 2.0). Water samples were taken from A (Figure 2.2): a drain near

the intersection of Independence Dr and Armur Dr (Southeast of Sunset Valley), B


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(Figure 2.3): from a pond behind of the apartment complex near 2804 W William

Cannon Dr (South of Sunset Valley), and C (Figure 2.4): from a big drain behind

Banfield the Pet Hospital near the intersection of Ernest Robles Way and Sycamore Tr

(East of Sunset Valley). For each of those sites, I took a total of 60 mL of water

samples, measured its pH, ORP (oxidation-reduction potential), and temperature using

the ultrameter. I poured 5 mL of the sample water to each Coliscan bottle and then to

the treated petri dish. This results in 12 petri dishes per site. I allowed the liquid inside

each dish to solidified, and then I cover the edge of the dish with parafilm and incubate

it at approximately 37.5C. After at least 24 hours, I came back and count the total

number of fecal coliform bacteria per 5 mL of water.

Figure 2.0: Shows the City of Sunset Valley and its boundary.


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Figure 2.1: Shows an aerial overview of three sample sites. Site A is Southeast of

Sunset Valley, site B is South of Sunset Valley, and site C is inside of Sunset Valley.

Figure 2.2: Shows an aerial overview of site A.


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Figure 2.3: Shows an aerial overview of site B.

Figure 2.4: Shows an aerial overview of site C.


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4.4 Data/Results

I did not expect the mean value of CFU/5 mL to be that close. The standard

deviation is also higher than my expectation. The standard deviation became even more

problematic when I convert my CFU/5 mL into CFU/100 mL. This means that there is

possible error in my counting or on the quality of water itself. According to Table 1.1, the

UCF/100 mL in all three sites exceeded the EPA standard for healthy recreational

water. Fortunately the samples were taken from drains and pond, thus they were not

meant to be used for recreational purposes. The pH of the three sites is not too far from

each other, they are at around the pH of normal water. Temperature varies a little bit,

but it is nothing too dramatic. The ORP values are also not too different, it ranges from

168 ppm to 230 ppm.


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Site A B C

pH 7.14 6.93 6.73

ORP(ppm) 168 230 189

T(C) 24.2 25.6 23.4

DishCFU/5

mLCFU/ 100

mLCFU/5

mLCFU/ 100

mLCFU/5

mLCFU/ 100

mL

1 34 680 46 920 36 720

2 22 440 39 780 27 540

3 42 840 53 1060 30 600

4 36 720 27 540 29 580

5 23 460 30 600 33 660

6 25 500 23 460 42 840

7 31 620 33 660 30 600

8 24 480 42 840 28 560

9 37 740 31 620 35 700

10 38 760 28 560 26 520

11 25 500 48 960 40 800

12 33 660 35 700 28 560

Average 30.833 616.667 36.250 725.000 32.000 640.000

Std. Dev. 6.807 136.137 9.353 187.059 5.222 104.447

Table 1.1: Shows the water properties from sites A, B and C. ForE. coli, the CFU/5 mL

and CFU/100 mL are also shown.


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4.5 Discussion

From Table 1.1 and figure 3.1, temperature had no effect on the population of E.

coli. This was to be expected because I did not collect enough water samples with a

wide range of temperature. The pH value did not correlate well with the E. coli

population (Figure 3.2). The mean E. colipopulation peaked at approximately 6.9 pH

and decreased when it became more basic or acidic. The average E. colipopulation for

site B was higher than that of site A and C although water at site A has a higher pH and

water at site C has a lower pH, respectively. Once again, I need more water samples to

be able to make a sound conclusion. One other correlation that arose from Table 1.1

and Figure 3.3 was that the ORP value affected the average E. colipopulation by a

small amount. The data suggested that higher ORP value yielded a higher average

fecal coliform population. Although I only had three points to analyze, I can confirm that

ORP does have a positive effect on E. colipopulation.

Figure 3.1: Shows the relationship between temperature and mean CFU/5 mL.


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Figure 3.2: Shows the relationship between pH and mean CFU/5 mL.

Figure 3.3: Shows the relationship between ORP and mean CFU/5 mL.

Site A and B are type III residential areas with an average 9% impervious cover

and site C is a commercial non-residential area with an average 55% impervious cover


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(Table 1.2, last column for average percentage of impervious cover). According to

Figure 3.4, site C has its mean UCF/5 mL close to site A with a much higher impervious

cover percentage. Additionally, site B has higher mean UCF/5 mL than site A and C

despite its low impervious cover. There was not enough evidence to make a solid

conclusion based on my finding above. I expected the average population of fecal

coliform/E. colito be higher with higher impervious cover percentage but this was not

the case.

Single-Family Total

Parcels

AverageImpervious

Area(SqFt)

TotalImpervious

Area(SqFt)

AverageParcel

Size(SqFt)

Total Land

Area(SqFt)

Average

Percentage of

ImperviousCover

265 5,117 1,355,880 46,128 12,223,790 11%

I < 3,253 ft2

44 2,646 116,424 13,593 598,073 19%

II 3,253-6,980 ft2

184 5,035 926,485 44,904 8,262,370 11%

III 6,980 ft2

- 11,966 ft2

37 8,459 312,971 90,901 3,363,347 9%

Non-Residential 26 234,820 6,105,317 429,065 11,155,690 55%

Civic 1 89,000 89,000 469,795 469,795 19%

Commercial 22 169,877 3,737,300 307,010 6,754,216 55%

Multifamily 1 186,360 186,360 937,796 937,796 20%

AISD 2 1,046,329 2,092,657 1,496,942 2,993,883 70%

Total 291 25,640 7,461,197 80,342 23,379,480 32%

Table 1.2: Shows impervious cover and land data in Sunset Valley (taken from the Final

Report of the Drainage Utility Assessment City of Sunset Valley. July 28, 2010).


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Figure 3.4: Shows the relationship between mean UCF/5 mL and percent impervious

cover.


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4.6 Conclusion

Although my research failed to make a connection between fecal coliform

population and impervious cover percentage, I cannot ignore the fact that the highest

concentration of coliform bacteria occurred in the urban headwaters (Andersen et al.

2007). The results should be more conclusive if more sites are available to me. If I still

cannot come up with a firm correlation, it could be due to the fact that coliform and E.

colipopulation estimate techniques measure from 5 to 100% of the potential population

(Dutka, Kuchma, and Kwan 1979). The errors for my findings can be minimal to

substantial. In order to get the best result possible, I may have to use other methods

such as the S-Mf, the A-MF, and MPN with more water samples from each site.

Unfortunately I do not have enough time and resource to work on my project with

extreme precision. This project has potential and it is unlucky that I was not able to see

the results I wanted.

There should still be worries about the issue of contamination in water resources

due to increasing urbanization. There will be build-up of organic matter and nutrients on

soils if urbanization continues to increase. If left unchecked, these organic matter and

nutrients will have an effect on the fecal indicator bacteria including total coliforms and

E. coli. Accumulation and survival of the fecal indicator bacteria has a negative impact

on surface water quality. In surface water, sediments may make up a reservoir of

different pollutants including inorganic and organic compounds and microorganisms.

The survival of fecal bacteria, once released in the aquatic environment, is determined

by many environmental factors including temperature variation, salinity, oxygen levels,


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nutrient deficiencies, predation, and ultraviolet irradiation (McFeters and Singh 1991;

Davies et al. 1995; Thomas et al. 1999; Hughes 2003; Craig et al. 2004). There is a

correlation between the higher growth and lower decay of fecal indicator bacteria in

sediments with high level of organic matter and nutrients (Amedegnato et al. 2009). I

understand that I did not produce the result I wanted, but if I have more opportunity to

test more sites, there will be a positive correlation between percent impervious cover

and fecal coliform population.

For future studies, I will definitely do a more thorough research about my site and

the availability of water in and around it. It is always best to collect samples after a rain

event, but if rain is an issue, there should always be waterbodies nearby to provide

backup samples. Furthermore, I will collect water at as many sites as possible. The

amount of data will determine how strong or weak the correlation between all of the

possible factors that influence the population of fecal coliform.


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Acknowledgement

I thank Nicholas Kuzola, Michael Nelson, Matthew Hubbard, and Ladislaus (Dan)

Perenyi for providing me with information and insights about my project. I also thank

Stephen Bond for providing me with equipment and materials necessary for me to carry

out my experiment. I especially thank Dr. Poteet for her help in guiding my project to the

right direction and also for fully funded my project.


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References

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