Content uploaded by Emmanuel De-Graft Johnson Owusu-Ansah
Author content
All content in this area was uploaded by Emmanuel De-Graft Johnson Owusu-Ansah on Feb 29, 2016
Content may be subject to copyright.
Research Journal of Applied Sciences,
Engineering and Technology
10(11):
1293
-
1302
, 2015
ISSN: 2040-7459; e-ISSN: 2040-7467
© 2015 Maxwell Scientific Publication Corp.
Submitted: December 20, 2014 Accepted: January 27, 2015 Published: August 15, 2015
Corresponding
Author:
Emmanuel de
-
Graft Johnson Owusu
-
Ansah,
Department of Mathematics, Kwame Nkrumah University
of Science and Technology, Kumasi, Ghana
This work is licensed under a Creative Commons Attribution 4.0 International License (URL: http://creativecommons.org/licenses/by/4.0/).
1293
Research Article
Performance, Compliance and Reliability of Waste Stabilization Pond: Effluent Discharge
Quality and Environmental Protection Agency Standards in Ghana
1
Emmanuel De-Graft Johnson Owusu-Ansah,
2
Angelina Sampson,
1
Samuel K. Amponsah,
3
Robert C. Abaidoo
and
4
Tine Hald
1
Department of Mathematics,
2
Department of Biochemistry,
3
College of Agriculture and Natural Resources, Kwame Nkrumah University of Science and
Technology, Kumasi, Ghana
4
Epidemiology and Risk Assessment, Division of Microbiology and Risk Assessment, National Food
Institute, Technical University of Denmark, Morkhoj Bygade, Soborg, Denmark
Abstract: Measuring performance has been arguerably, one of the metric with many facets with different school of
thoughts, as there exist different approaches of measuring it. Several of the existing approaches measure such metric
by comparison with standards esherined in policy documents and as a result, takes less look to its compliance and
reliability of values being matched to an established standards. This study seeks to integrate reliability and
compliance into measuring of performance of Waste Stabilization Pond (WSP) and Treatment Plant (TP) as well as
to generate the appropriate standard chart tables using the Ghana Environmental Protection Agency (EPA) approved
discharge values for physico-chemical and some biological parameters to account for these shortfalls on over
reliance of EPA discharge standards. Probability distribution density function was applied on the lognormal
distribution function to establish the relationship between the statistical coefficient of variation and the coefficient of
reliability based on r
th
moment about the origin in the moment of generation function to generate the functions of the
mean and standard deviation, properties of the standard Z normal distribution were used to establish the coefficient
of reliability relationship depending on the coefficient of variation influenced by the standard of deviation.
Discharge values of Physico-chemical Parameters measured from the WSP were found be performing acceptably
based on the EPA standards, whereas only four of the TP were acceptable. Discharge Values of physico-chemical
and biological parameters which are found to be accepted under comparison with EPA standards were found to have
compliance levels below what is generally accepted for Waste Stabilization Ponds (WSP) designed compliance.
Based on these shortcomings, reference charts were develop to serve as reference points in assessing the various
characteristics of compliance and performance of WSPs in Ghana on (28) physico-chemical and biological
parameters. These charts are intended to make it easier to assess the performance of WSPs and its corresponding
reliability and compliance level to compensate for overreliance on EPA standards alone.
Keywords: Coefficient of reliability, coefficient of variation, effluent quality discharge, EPA standards, lognormal
distribution, performance and compliance, probability of reliability, waste stabilization ponds
INTRODUCTION
The use of Waste Stabilization Ponds (WSPs) as an
economical and efficient way of re-cycling water has
taken centre stage for the use in agricultural production
over the past two decades, it’s been noted that, WSPs
are usually the most cost effective procedure in dealing
with domestic and municipal wastewater treatment for
agricultural purposes in Africa due to the condition of
favorable climate and low cost of maintenance (Mara,
2004), which are very important factors for tropical
countries such as the sub-Saharan Africa.
Owing to the natural process of treatment of water
in WSP, the treatment processes are highly dependent
on the physical design of the WSPs; conversely these
physical designs do not consider the ecological process,
which takes place in the system. Purposefully, WSP
consist of anaerobic ponds, facultative and maturation
ponds in series, or several of these maturation series
ponds in parallel (Gawasiri, 2003; Mara, 1996, 2004)
with each having a specific purpose, though they
sometimes overlap. Specifically, facultative ponds are
needed for the removal of Biochemical Oxygen
Demand (BOD
5
) when the effluent is meant for
restricted irrigation and fish pond fertilization as well as
for the discharge of treated water into surface water.
For unrestricted irrigation, maturation ponds are needed
for the removal of pathogen in order to meet the WHO
Res. J. Appl. Sci. Eng. Technol., 10(11): 1293-1302, 2015
1294
standard of ‘<1000 fecal coliform bacteria per 100 mL’
(Mara, 2004; WHO, 2006).
Several studies (Haydeh et al., 2013; Shah, 2008;
Oliveira and Sperling, 2008; Mbwele et al., 2003) have
evaluated the performance of the WSPs by comparing
the percentage of removal of Biochemical Oxygen
Demand (BOD
5
), Total Nitrogen (TN), Total
Suspended Solids (TSS), Total Phosphorus (TP), Total
Coliform (TC), Faecal Coliform (FC) and Salmonella
with design standards, with few focusing on the extend
of compliance of the ponds to its design standards.
However, few works (Redda, 2013; Oliveira and
Sperling, 2008) have compared the percentage of
removal of the parameters of evaluation performance of
WSPs to the compliance and reliability of the WSPs
according to the standards set up by the local policy.
However, Oliveira and Sperling (2008) mainly
conducted the reliability concentrated on different
treatment plants by comparing their reliability level of
which a real WSP was excluded. Based on the
shortcomings of over relying on EPA standards without
due inclusion of compliance level of WSP and
treatment plants, performance short fall of accounting
for compliance and hence underestimate the effluent
discharge values deemed to be acceptable. This study
seeks to integrate reliability and compliance into
measuring of performance of WSP and treatment plant
as well as to generate the appropriate standard chart
tables using the Ghana EPA approved discharge values
for physico-chemical and some biological parameters to
account for these shortfalls on over reliance of EPA
discharge standards.
LITERATURE REVIEW
Statistical effluent data distribution: Exploratory
Data Analysis (EDA) gives overview of how data
analysis is done, by first fitting the appropriate
distribution to describe the data through making
distributional assumptions about the data (Thode,
2002); this could be done by plotting the dataset and
finding the classical or traditional distribution, which
fits the dataset. Most statistical tests assume data to be
normally distributed (Ott, 1995), but this is not
necessary true. Failing to understand the appropriate
statistical distribution of data may lead to invalid
assumptions and consequently incorrect conclusions
(Thode, 2002). Several studies (Niku and Schroeder,
1981; Niku et al., 1979, 1981, 1982; Oleiveira and
Sperling, 2006, 2008; Redda, 2013) have all reported
that, lognormal distribution gives a good overall fit to
effluent values, but there are some cases where neither
normal nor lognormal seems to approximately fit the
distribution of the observed data, in such cases the
distribution of effluent values should be treated
independently to fit its own distribution as such (Niku
et al., 1979). Niku et al. (1979) and Hovey et al. (1977)
developed a linear model through the use of simple
regression analysis to determine the percentile values
for the distributions of effluent parameters
concentrations exceeding various percentages of the
time related to the mean values. Their approach could
be used if the effluent concentration is not known or
does not follow a classical distribution function.
Recent work by Redda (2013) attempts to use the
logistic regression model to find the level of
compliance as a function of other independent
parameters of the treatment plants which influences the
parameters of measuring performance and give rise to
the reliability level without using the distribution
function of effluent concentration. Oliviera and
Sperling (2010) explained that due to variation in
performance, treatment plants should be designed to
produce effluent quality below the discharged
standards. This suggests a mean value should be used to
guarantee an effluent concentration consistency and
which should be less than standard with a certain
reliability and compliance level. To meet such high
standards especially with WSPs which do not have any
adjustable controls once in operation, the onus is placed
on the design stage and regular maintenance; hence
design engineers must be able to estimate the expected
effluent quality and its variations for a given time as
well as know the efficiency of each WSP portions.
Besides, Mbwele et al. (2003) and Redda (2013)
concluded that, care should be taken in the
interpretation of performance data, as some
performance variations likely result from biotic and
abiotic factors other than design standard values alone.
Process of reliability: Several studies have defined
reliability as the ability to perform the specified
requirements free from failure (Niku et al., 1979); e.g.,
the percentage of times a wastewater treatment plant
complies to discharge standards (McBride and Ellis,
2001; Smith et al., 2001). The WSPs will be completely
reliable if the process performance does not violate the
target standards of the regulatory bodies specifications
(Oliveira and Sperling, 2008), Hence mathematically:
(1)
Due to the numerous uncertainties underlying the
design and operation of a wastewater treatment plant, a
risk of failure is always unavoidable and the wastewater
treatment plant should be designed based on an
acceptable risk or degree of violation. Hence the mean
operational effluent quality and the coefficient or
reliability developed by Niku et al. (1979) is based on
the assumption of the lognormal distribution to assess
the reliability of the treatment plants. The minimum
reliability requirements must be determined to establish
the failure-probability magnitude that can be accepted’.
Niku et al. (1979) model as follows:
Res. J. Appl. Sci. Eng. Technol., 10(11): 1293-1302, 2015
1295
(2)
By Eq. (1) and (2) becomes:
Reliability = 1-P (effluent concentration > effluent
requirement) (3)
The lognormal distribution owning to deviation in
symmetry measured by the skewness coefficient, has
positive skewness since there is usually a lower bound
for effluent concentration, but there are no upper
bounds:
∞
The probability density function of the lognormal
distribution of effluent quality is given as:
(4)
where,
X = Effluent variable concentration
= Standard deviation of the natural logarithm of X
= Median of X
For the
moment of about the origin in the
moment generation function:
(5)
Hence:
(6)
(7)
where,
and
represent the mean and variance of
the original data, respectively from the moment
function. Re-arranging Eq. (6) and (7) accounting for
the relationship of parameters of probability density
function of lognormal distribution in terms of moment
of variable are:
(8)
(9)
where,
is the average natural logarithm of X.
For some probability of failure at, the lognormal
distribution will have a property of , thus:
(10)
Table 1: Value of standard normal distribution
Cumulative
probability
Percentiles
50
0.000
60
0.253
70
0.525
80
0.842
90
1.282
92
1.405
95
1.645
98
2.054
99
2.326
99.9
3.090
where,
is the effluent concentration standard fixed
for policy assessment. Hence choosing the parameters
of the lognormal distribution, Eq. (10) becomes:
(11)
The standard normal distribution can also be
defined from Eq. (10) as:
(12)
Hence at a reliability level of of a failure
level of , a known standard of effluent concentration
level could be calculated given a coefficient variation,
the
values were obtained using the NORMDIST
function in Microsoft excel (Table 1) for the cumulative
probability at and its percentiles. It should be
noted that, the higher the normal variate value the
higher the corresponding compliance level (cumulative
probability) hence by Eq. (8) and (9) into (11) results
in:
(13)
Making the mean value the subject of Eq. (13)
results in the following:
(14)
By simplification, Eq. (13) results:
(15)
The statistical parameters used in the reliability to
relate the mean constituent value
to standard
defines the Coefficient of Variation (CV) as
:
(16)
From Eq. (14), hence Coefficient of Reliability
(COR) is given as:
Res. J. Appl. Sci. Eng. Technol., 10(11): 1293-1302, 2015
1296
Table 2: Coefficient of reliability as a function of coefficient of variation and percentiles
Reliability
(%)
Coefficient of Variation (CV)
---------------------------------------------------------------------------------------------------------------------------------------------------------------
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.2 1.4 1.6 1.8
50 1.00 1.00 1.02 1.04 1.08 1.12 1.17 1.22 1.28 1.35 1.41 1.56 1.72 1.89 2.06
60 1.00 0.98 0.97 0.97 0.98 0.99 1.01 1.04 1.07 1.11 1.15 1.23 1.32 1.42 1.52
70 1.00 0.95 0.92 0.89 0.88 0.87 0.87 0.88 0.89 0.90 0.91 0.95 1.00 1.04 1.10
80 1.00 0.92 0.86 0.82 0.78 0.75 0.73 0.72 0.71 0.70 0.70 0.71 0.72 0.73 0.75
90 1.00 0.88 0.79 0.72 0.66 0.61 0.57 0.54 0.52 0.50 0.49 0.47 0.45 0.44 0.44
92 1.00 0.87 0.77 0.69 0.63 0.58 0.54 0.50 0.48 0.46 0.44 0.41 0.40 0.39 0.38
95 1.00 0.85 0.74 0.64 0.57 0.51 0.47 0.43 0.40 0.38 0.36 0.33 0.31 0.30 0.29
98 1.00 0.82 0.68 0.57 0.49 0.42 0.37 0.33 0.30 0.28 0.26 0.22 0.20 0.19 0.17
99 1.00 0.80 0.64 0.53 0.44 0.37 0.32 0.28 0.25 0.22 0.20 0.17 0.15 0.14 0.13
99.9 1.00 0.74 0.55 0.42 0.33 0.26 0.21 0.17 0.15 0.12 0.11 0.08 0.07 0.06 0.05
Table 3: Environmental protection agency standard values for effluent discharge in Ghana
EPA parameter Standard value Unit
Biochemical Oxygen Demand (BOD
5
), Total Suspended Solids (TSS) and Total Nitrogen (TN),
trichloroethylene, benzene
50 mg/L
Carbon Oxygen Demand (COD), chloride and total residual chlorine 250 mg/L
50 mg/L
Total Phosphate (TP) 2.0 mg/L
Total Coliform (TC) 400
Total Dissolved Solids (TDS) 1000 mg/L
Dissolve oxygen, total cyanide, phenol, selenium and ammonia 1.0 mg/L
Conductivity 1500
µ
pH 6-9
Temperature <30.0 ºC
Turbidity 75 NTU
E.
coli 10
Soluble arsenic, lead and silver 0.1 mg/L
Total arsenic, total chromium and nickel 0.5 mg/L
(17)
Putting Eq. (16) into (17):
(18)
The values are obtained as a function of
coefficient of variation and reliability level (Table 2),
the different values of the coefficient of variation
suggests the different mean and standard deviation
parameters of measuring reliability might produce of
which are mainly less than 1.0 in practices (Niku et al.,
1979; Oliveira and Sperling, 2008). Hence by Eq. (18)
into (14) simplifications becomes:
(19)
where,
= The effluent quality standard
= The coefficient of reliability
= The mean effluent concentration needed to
achieved a certain compliance level of effluent
quality standard
Study site and data: Data was obtained through two
separate studies on each of the two different sites, all
the sites are located within the Kumasi metropolis
which is located latitude (6°35’ to 6°40’N) 1’30 W and
on longitude (1°30’ to 1°35’) 60 W, 40 N. It has a
climate which falls within the wet sub-equatorial type
with an average minimum temperature of 22.5°C and a
maximum average of 30.7°C. The two study sites were
the KNUST treatment plant and Ahinsan WSP. The
KNUST treatment plant was constructed to receive
wastewater from residential facilities within the
university campus and its design was based on the
conventional designs of WSPs. The Ahinsan Estate
WSP was designed to receive wastewater from the
residential areas which includes Ahinsan and Chirapatre
estates.
EPA’s discharge standards used in the study: The
Ghana Environmental Protection Agency discharge
standards were adopted for the Coefficient of
Reliability (COR) study, the parameters included
(Table 3) correspond to other developing countries
standards which are considered as more realistic as well
as areas which falls within the tropical regions such as
Brazil and elsewhere (Oakley et al., 2000; Ragas et al.,
2005; Oliveira and Sperling, 2006). These standards are
set as local guideline for discharge of effluents into
either stream for irrigation or for replenishing aquifers.
RESULTS AND DISCUSSION
Reliability and compliance: Two statistical parameters
characterize the Coefficient of Reliability (COR) the
mean and the standard deviation. These two values
Res. J. Appl. Sci. Eng. Technol., 10(11): 1293-1302, 2015
1297
determines the nature of the coefficient of variation and
hence influences the COR express. As presented in
several studies (Niku et al., 1979; MetCalf and Eddy,
2003; McBride and Ellis, 2001; McBride, 2003;
Olieveira and Sperling, 2008; Oakley et al., 2000;
Redda, 2013) the COR tells the values of the mean
design concentrations to the standard that should be
achieved, on a probability basis according to the desired
level of the operators of the treatment plant, while the
standard normal variate calculates the expected
percentage of compliance with the discharge standards.
The expected percentage of compliance can be used to
access the performance of the different segment of the
plant as well as for the overall performance, which
helps to identify critical points of malfunction i.e.,
points out discharge standards having unexpected
values according to design parameters.
Various standard values of the EPA Ghana were
used to find the different compliance level given
different levels of coefficient of variation. Twenty eight
parameters were selected to develop a reference chart
(See baxzes for charts) and used to ascertain the level of
compliance at different CVs. Result of effluent
discharge performance are much to be desired when the
effluent discharge is compared to the fixed standard
value only and where it is assume that if the discharge
concentration is less than the standard value, the
performance is good and indicates a better compliance
level of the WSPs. However as shown in the reference
chart (See baxzes for charts), such assumption is not
always true.
For example, the required standard of effluent
discharges concentration of BOD
5
or TSS is 50 mg/L
(EPA, Ghana Standard). If a sample taken from a
specified WSP gives a mean effluent quality of 48.00
and with standard deviation of 14.4 will have a CV
value of 0.3, by referencing to Chart 1 (See baxzes for
charts) corresponds to a compliance level (COR) of
60% i.e., less than the required less stringent
compliance level of 80% for WSPs (Oliveira and
Sperling, 2008; Redda, 2013), This is despite the fact
that the mean effluent concentration from the WSP as
compared to the EPA standard falls below the standard
value of 50 mg/L and can be classified as a good
discharged value. Nevertheless, the WSP is
underperforming, its effluent discharge value is just less
than that of a compliance level of 60%, hence its
compliance level is below what’s generally accepted
(even in a less stringent level of 80%), such
information, if available can trigger a further check to
be done to identify the segment of the pond (Anaerobic,
facultative and maturation) that is underperforming,
which could support the routine maintenance of the
ponds.
The same procedure could be used by comparing
the expected mean effluent concentration of the
segments of the pond to its samples using its design
compliance and hence finding the compliance level of
effluent to check for malfunctioning of pond segments,
this is necessary due to the different expected work to
be done by each segment to enhance the maintenance of
the ponds regularly, Nonetheless, a critical look should
be taken because CV values directly relate to reliability
and inversely to COR values, hence CV value with high
standard deviation and lower mean of an effluent can
have the same value as a CV of high mean and low
standard deviation value of an effluent, the later shows
a more consistent discharge. This shows that, a lower
value of CV does not necessarily indicate better results.
Moreover, with the use of the charts (See baxzes
for charts) for various parameters of WSPs in Ghana,
once an effluent concentration average is known and its
compliance level at design is also known, a quick
reference point can be made to find what was expected
to be discharging and compare to its current discharge
to be assure of its compliance without necessary
Table 4: Performance of physical conditions (Ahinsan estate WSP)
pH Temp. Conductivity Total dissolved solids
Total suspendid solids
Anaerobic pond
Influent 7.50 26.80 1419.00 728.00 323.00
Effluent 7.20 26.20 679.00 339.00 91.00
Remove (%) 4.00 2.24 52.15 53.43 71.83
Facultative pond
Influent 7.20 26.20 679.00 339.00 91.00
Effluent 7.10 26.30 605.00 302.00 88.00
Remove (%) 1.39 -0.38 10.90 10.91 3.30
Maturation pond I
Influent 7.10 26.30 605.00 302.00 88.00
Effluent 7.30 26.40 542.00 270.00 38.00
Remove (%) -2.82 -0.38 10.41 10.60 56.82
Maturation pond II
Influent 7.30 26.40 542.00 270.00 38.00
Effluent 7.30 26.60 484.00 242.00 52.00
Remove (%) 0.00 -0.76 10.70 10.37 -36.84
Overall
Influent 7.50 26.80 1419.00 728.00 323.00
Effluent 7.30 26.60 484.00 242.00 52.00
Remove (%) 2.67 0.75 65.89 66.76 83.90
Res. J. Appl. Sci. Eng. Technol., 10(11): 1293-1302, 2015
1298
Table 5: Performance chemical conditions (Ahinsan estate WSP)
NO
3
-N NO
2
-N NH
3
-N TP DO
Anaerobic pond
Influent 0.20 0.47 0.75 18.40 1.40
Effluent 0.05 0.08 0.46 10.40 0.30
Remove (%) 75.00 82.98 38.67 43.48 78.57
Facultative pond
Influent 0.05 0.08 0.46 10.40 0.30
Effluent 0.03 0.06 0.43 11.00 0.30
Remove (%) 40.00 25.00 6.52 -5.77 0.00
Maturation pond I
Influent 0.03 0.06 0.43 11.00 0.30
Effluent 0.02 0.04 0.37 9.40 0.70
Remove (%) 33.33 33.33 13.95 14.55 -133.33
Maturation pond II
Influent 0.02 0.04 0.37 9.40 0.70
Effluent 0.01 0.06 0.36 6.10 0.80
Remove (%) 50.00 -50.00 2.70 35.11 -14.29
Overall
Influent 0.20 0.47 0.75 18.40 1.40
Effluent 0.01 0.06 0.36 6.10 0.80
Remove (%) 95.00 87.23 52.00 66.85 42.86
Table 6: Performance of biological conditions (Ahinsan estate WSP)
E. coli TC BOD
5
COD
Anaerobic pond
Influent 2.3×10
9
6.8×10
9
554.00 933.00
Effluent 3.8×10
7
3.3×10
8
95.00 187.00
Remove (%) 98.35 95.15 82.85 79.96
Facultative pond
Influent 3.8×10
7
3.3×10
8
95.00 187.00
Effluent 3.1×10
7
1.2×10
7
83.00 178.00
Remove (%) 18.42 96.36 12.63 4.81
Maturation pond I
Influent 3.1×10
7
1.2×10
7
83.00 178.00
Effluent 2.4×10
6
4.3×10
7
37.00 68.00
Remove (%) 92.26 -258.33 55.42 61.80
Maturation pond II
Influent 2.4×10
6
4.3×10
7
37.00 68.00
Effluent 7.1×10
5
1.7×10
8
38.00 99.00
Remove (%) 70.42 -295.35 -2.70 -45.59
Overall
Influent 2.3×10
9
6.8×10
9
554.00 933.00
Effluent 7.1×10
5
1.7×10
8
38.00 99.00
Remove (%) 99.97 97.50 93.14 89.39
comparing it to fixed standard values, due to the
unaccounted for information on compliance the EPA
fixed standard gives, These reference charts were
develop to serve as reference points in assessing the
various characteristics of compliance and performance
of WSPs in Ghana. These tables are intended to make it
easier to assess the performance of WSPs and its
corresponding reliability and compliance level without
going through the task of using the log-normal
procedure as shown above.
Performance analysis: The influent and effluent
conditions of the different pond cells or portions of
water quality in terms of physical parameters (Table 4),
chemical parameters (Table 5) and biological
parameters (Table 6) of the Ahinsan WSP are given
below. The physical condition performance (Table 4) in
terms of removal percentage were as follows;
conductivity (52.15%), total dissolved solids (53.43%)
and total suspended solids (71.83%) in the anaerobic
pond, the facultative pond continued the removal
efficiency of the physical parameters except for
temperature which recorded negative percentage (-0.38)
indicating a temperature rise from the anaerobic pond
into the facultative pond and a continued rise in the
maturation pond as well. However, the rise in
temperature was insignificant and fell within the level
essential for algae growth. Moreover, TSS also
increased in the maturation pond as well. Notably, the
cumulative efficiency removal was high for both
anaerobic and facultative ponds which are specially
designed to remove most of the physical properties
WSPs (Mara, 2004). Nevertheless, the maturation pond
also helps in the reduction of all of the physical
parameters of the Ahinsan pond with the exception of
the temperature, pH and TSS.
The chemical parameters saw an efficient removal
in the anaerobic and facultative ponds and most of the
removal was done before the effluent entered the
maturation pond. TP increased in the facultative pond,
Res. J. Appl. Sci. Eng. Technol., 10(11): 1293-1302, 2015
1299
Table 7: Physico-chemical properties including BOD
5
(KNUST PLANT)
Temp. PH TURB TSS TP TN BOD
5
Primary pond
Influent 28.95 7.90 478.38 370.69 44.92 36.65 271.13
Effluent 28.78 7.73 342.69 342.69 41.16 33.29 231.69
Remove (%) 0.59 2.15 28.36 7.55 8.37 9.17 14.55
Dosing and trickling filter
Influent 28.78 7.73 342.69 342.69 41.16 33.29 231.69
Effluent 24.35 7.13 114.06 162.63 26.87 22.21 172.75
Remove (%) 15.39 7.76 66.72 52.54 34.72 33.28 25.44
Secondary pond
Influent 24.35 7.13 114.06 162.63 26.87 22.21 172.75
Effluent 22.99 7.09 84.56 80.00 19.11 12.93 116.13
Remove (%) 5.59 0.56 25.86 50.81 28.88 41.78 32.78
Tertiary pond
Influent 22.99 7.09 84.56 80.00 19.11 12.93 116.13
Effluent 24.66 6.78 59.69 51.63 12.20 10.83 81.75
Remove (%) -7.26 4.37 29.41 35.46 36.16 16.24 29.60
Overall
Influent 28.95 7.90 478.38 370.69 44.92 36.65 271.13
Effluent 24.66 6.78 59.69 51.63 12.20 10.83 81.75
Remove (%) 14.82 14.18 87.52 86.07 72.84 70.45 69.85
but declined in the maturation ponds I and II, On the
other hand, dissolved oxygen removal was predominant
only in the anaerobic pond, whereas the DO content
increased in the maturation pond, leading to the
recording of negative percentage. This is due to the
high removal activity in the anaerobic pond (78.57%)
whereas microbiological activities in the other ponds
use oxygen due to the algae growth. The nitrate family
continued to decline, indicating that there was no
limitation to nitrification in the Ahinsan Estate Pond.
On the biological parameters (Table 6) BOD
5
and
COD efficiency of removal was very high in the
anaerobic pond as well (82.85 and 79.96%,
respectively). This removal efficiency was also evident
in the facultative pond and maturation pond I. In
maturation pond II, a reverse efficiency was recorded,
confirming that the anaerobic and facultative ponds are
essential for the removal of (TN, TP, BOD
5
, COD,
Conductivity, pH), whereas the anaerobic pond is
essentially for DO. The E. coli and TC were having a
high efficiency of removal in the anaerobic pond and
the TC increased further in the facultative, but
increased considerably in the maturation ponds
resulting in negative percentages. Still, the overall
removal efficiency was high.
In respect of the KNUST plant, the physico-
chemical parameters (Table 7) showed a continuous
removal of BOD
5
, TN, TP, TSS, TURB, pH and
Temperature, indicating that the BOD
5
content declines
as the wastewater passes through the primary chamber
up to the secondary chamber from which wastewater
can be used for restricted irrigation. The percentage
efficiency of the removal trend was not different from
the Ahinsan WSP, though the latter has higher
percentage removal.
The average values of removal efficiency
percentages of the physical, chemical and biological
parameters of the Ahinsan plant as well as the physico-
chemical parameters of the KNUST plant are all
presented in the respective Table 4 to 7. The overall
removal efficiency of the Ahinsan WSP were 2.6% for
pH, 83.90% for TSS, 87.23% for NO
2
-N, 52.00% for
NH
3
-N, 66.86% for TP, 42.86% for DO, 97.50% for
TC, 93.14% for BOD
5
and 89.39% for COD. Whereas
that of the KNUST Plant were 14.18% for pH, 86.07%
for TSS, 72.84%% for TP, 70.45% for TN and 69.85%
for BOD
5
, the removal efficiency of BOD
5
in the
Ahinsan WSP was higher than the KNUST plant.
Effluent discharge and EPA standards: From the
comparison of the various discharge qualities to the
standards of EPA, it is very evident that, some of the
parameters for the KNUST plant do not conform to
EPA standards (Table 8). Though some exceptions like
the temperature (24.66), TN (10.83), TC (79.69), pH
(6.78) and turbidity (59.69) level, which recorded a
lower discharge values than the EPA standard, all other
parameters such as TSS (51.53), TP (12.2), BOD
5
(81.75) and E. coli (26.50) were higher than the
standard. In contrast, the Ahinsan WSP was performing
better in terms of discharge values than the KNUST
plant. This WSP had most of its effluent discharge
values lower than the EPA which included; temperature
(26.6), pH (7.3), TN (0.01), Ammonia (0.36), BOD
5
(38), COD (99), Conductivity (484), TDS (242). The
performing discharge values of the Ahinsan WSP is
attributed to some form of maintenance during the trial
work of aqua-culture in the ponds, whereas, the
KNUST plant did not receive any form of maintenance
over quite a number of years, which can explain its
under-performance.
Compliance and reliability analysis:
EPA’s discharge standards to be achieve in
operation concentration: The current reliability of the
KNUST treatment plant and Ahinsan WSP (Table 9)
Res. J. Appl. Sci. Eng. Technol., 10(11): 1293-1302, 2015
1300
Table 8: Effluent discharge values and the EPA standard
Parameter
EPA standard
KNUST
plant
Ahinsan WSP
Temperature (°C)
<30
24.66
26.60
pH
6
-
9
6.78
7.30
TSS (mg/L)
50
51.63
52.00
TP (mg/L)
2
12.20
6.10
Turbidity (NTU)
75
59.69
-
TN (mg/L)
50
10.83
0.01
Ammonia/
ammonium
(mg/L)
1
-
0.36
BOD
5
(mg/L)
50
81.75
38.00
COD (mg/L)
250
-
99.00
Conductivity (µ
750
-
484.00
TDS (mg/L)
1500
-
242.00
DO (mg/L)
1
-
0.80
TC (MPN/100 mL)
400
1.7×10
8
E.
coli
(MPN/100 mL)
10
26.50
7.1×10
5
Table 9: Actual mean effluent discharge, reliability and its compliance
Parameters
KNUST
treatment
plant
------------------------------------------------------------------
AHINSAN WSP
--------------------------------------------------------------
Mean
effluent
discharge Reliability Compliance
Mean
effluent
discharge Reliability Compliance
Temperature (
°
C)
24.66
0.63
73.60
26.60
0.51
69.50
pH
6.78
-
0.03
-
0.84
48.8
0
-
80.0
0
7.30
-
0.05
-
0.65
48.0
0
-
74.2
0
TSS (mg/L)
51.63
0.35
63.70
52.00
0.36
64.10
TP (mg/L)
12.20
-
2.11
1.70
6.10
-
1.38
8.40
Turbidity (NTU)
59.69
0.68
75.20
-
-
-
TN (mg/L)
10.83
2.21
98.65
0.01
17
.
09
99.90
Ammonia/
ammonium
(mg/L)
-
-
-
0.36
1.84
96.70
BOD
5
(mg/L)
81.75
0.34
63.30
38.00
0.75
77.30
COD (mg/L)
-
-
-
99.00
1.66
95.20
Conductivity (µ
-
-
-
484.00
2.12
98.30
TDS (mg/L)
242.00
2.09
98.20
DO (mg/L)
-
-
-
0.80
0.67
74.90
TC (MPN/100 mL)
1.7×10
8
-
13.17
00.00
E.
coli
(MPN/100 mL)
26.50
-
0.47
31.90
7.1×10
5
-
14.92
00.00
Table 10: Mean design effluent concentration to achieve 95% compliance with the standard and observed actual effluent concentrations
Parameters
KNUST
---------------------------------------------------------------------
AHINSAN
----------------------------------------------------------------------
CV COR
Mean
design
conc.
Observed
actual
mean conc. CV
COR
Mean
design
conc
.
Observed
actual
mean conc.
Temperature (°C)
0.57
0.48
14.40
24.66
0.72
0.43
12.90
26.60
pH
0.49
0.52
3.12
6.78
0.63
0.46
2.76
7.30
TSS (mg/L)
0.92
0.37
18.50
51.63
0.98
0.36
18.00
52.00
TP (mg/L)
0.84
0.39
0.78
12.20
0.73
0.42
0.84
6.10
Turbidity (NTU)
0.73
0.42
31.50
59.69
-
-
-
-
TN (mg/L)
1.04
0.35
17.54
10.83
0.54
0.49
24.50
0.01
Ammonia/
ammonium
(mg/L)
-
-
-
-
0.77
0.41
0.41
0.36
BOD
5
(mg/L)
2.41
0.27
13.50
81.75
0.69
0.44
22.00
38.00
COD (mg/L)
-
-
-
-
0.81
0.40
100.00
99.00
Conductivity (µ
-
-
-
-
0.69
0.44
660.00
484.00
TDS (mg/L)
-
-
-
1.03
0.35
350.00
242
DO (mg/L)
-
0.85
0.39
0.39
0.80
TC (MPN/100 mL)
1.21
0.33
132.00
1.7×10
8
E.
coli
(MPN/100 mL)
1.32
0.32
3.20
26.50
0.84
0.39
3.90
7.1×10
5
shows different compliance level of the discharge
values to the standard values used for the design. Only
three discharge values (TC; 99.4%, TN (98.65%) and
pH; 48.8-80.0%) met the less stringent design
specification of 80% compliance on the KNUST plant
and as well recorded an observed value less than its
mean design concentration value (Table 10). The
different compliance levels of the actual effluent
discharge were: Temperature (73.6%), TSS (63.7%),
TP (1.7%), Turbidity (75.2%), BOD
5
(63.3%) and
E. coli (31.9%) and although discharge values for
temperature, pH, turbidity and TC were all lower than
the EPA standards, but fall short of meeting the design
compliance of 95%.
The Ahinsan WSP had five of its discharge values
(TN, 99.9%; Conductivity 98.3%; Ammonia, 96.7%;
COD, 95.2% and TDS, 98.%) conforming to the
standard compliance of 95% compliance and achieving
its observed effluent discharge being less than the mean
design concentration with the required compliance level
(Table 10), whereas the rest were not complying with
the design compliance level. These included;
Res. J. Appl. Sci. Eng. Technol., 10(11): 1293-1302, 2015
1301
Temperature (69.5%), pH (48.0 to 74.2%), TSS
(64.1%), TP (8.4%), BOD
5
(77.3%), DO (74.9%), TC
(0.00%) and E. coli (0.00%). Again, temperature, pH,
BOD
5
, conductivity and DO effluent discharge meet the
EPA standard but its compliance level does not meet
the design specification.
CONCLUSION
From this study, it was observed that, measuring
performance of WSP and treatment plants using
effluent discharge values in a comparison to standards
alone is sufficient only for knowing effluent quality but
cannot be used to evaluate compliance of the WSP or
treatment plant. It is evident that compliance because it
considers both effluent quality discharge as well as
design capability in its performance measure is more
appropriately than the use of removal efficiency and
fixed standard values alone. In this study, we developed
reference charts (Table 1 to 3: Supplementary Results)
which can be used for assessing effluent discharge
qualities. These were done for different compliance
levels from the Ghana EPA standard discharge values.
Nevertheless, the importance of a stable operation and
thus low CV should be remembered at all time, so that
the WSP or treatment plant should not need to be
designed to achieve very low mean effluent
concentration. The effluent discharge values of the sites
used for the study were not complying fully with the
design specification (for the less stringent specifications
of WSP and treatment plant). However, the Ahisan
WSP had some of its water quality parameters (TN,
Ammonia, TDS and COD) meeting both the
compliance level of 95% and the EPA discharge
standards. However, irrespective of the presence of two
maturation ponds in series for the Ahinsan WSP, it
could not meet the pathogen reduction standard values
expected.
REFERENCES
Gawasiri, C.B., 2003. Modern design of waste
stabilization ponds in warm climates: Comparison
with traditional design methods. M.Sc. Thesis,
University of Leeds, UK.
Haydeh, H., M. Doosti and M. Sayadi, 2013.
Performance evaluation of waste stabilization
pond in Birjand, Iran for the treatment of
municipal sewage. Int. Acad. Ecol. Environ. Sci.,
3(1): 52-58.
Hovey, W.H., E.D. Schroeder and G. Tschobanoglous,
1977. Optimal Size of Regional Wastewater
Treatment Plants. California Water Resources
Centre, Contribution No. 161, University of
California, Davis.
Mara, D., 1996. Waste stabilization ponds: Effluent
quality requirements and implications for process
design. Water Sci. Technol., 33(7): 23-33.
Mara, D., 2004. Domestic Wastewater Treatment in
Developing Countries. Earthscan, USA.
Mbwele, L., M. Rubindamayugi, A. Kivaisi and
G. Dalhammar, 2003. Performance of a samll
wastewater stabilization system in tropical climate
in Dar Es Salaam, Tanzania. Water Sci. Technol.,
48(11-12): 187-191.
McBride, G.B., 2003. Confidence of compliance:
Parametric versus nonparametric approaches.
Water Res., 37: 3666-3671.
McBride, G.B. and J.C. Ellis, 2001. Confidence of
compliance: A Bayesian approach for percentile
standards. Water Res., 35(5): 1117-1124.
Metcalf and Eddy, 2003. Wastewater Engineering:
Treatment and Reuse. 4th Edn., Metcalf & Eddy
Inc., New York, pp: 1819.
Niku, S. and E.D. Schroeder, 1981. Factors affecting
effluent variability from activated sludge
processes. J. Water Pollut. Control Assoc., 53(5):
546-559.
Niku, S., E.D. Schroeder and F.J. Samaniego, 1979.
Performance of activated sludge process and
reliability-based design. J. Water Pollut. Control
Assoc., 51(12): 2841-2857.
Niku, S., E.D. Schroeder and R.S. Haugh, 1982.
Reliability and stability of trickling filter processes.
J. Water Pollut. Control Assoc., 54(2): 129-134.
Niku, S., E.D. Schroeder, G. Tchobanoglous and
F.J. Samaniego, 1981. Performance of activated
sludge process: Reliability, stability and variability.
Environmental Protection Agency, EPA Grant No.
R805097-01, pp: 1-124.
Oakley, S.M., A. Pocasangre, C. Flores, J. Monge and
M. Estrada, 2000. Waste stabilization pond use in
Central America: The experiences of El Salvador,
Guatemala, Honduras and Nicaragua. Water Sci.
Technol., 42(10/11): 51-58.
Oliveira, S.C. and M.V. Sperling, 2008. Reliability
analysis of wastewater treatment plants. J. Water
Res., 42: 1182-1194.
Oliveira, S.M.A.C. and M.V. Sperling, 2006.
Wastewater characteristics in a developing country,
based on a large survey (166 treatment plants).
Proceeding of the 5th IWAWorld Water Congress,
Beijing, China.
Ott, W.R., 1995. Environmental Statistics and Data
Analysis. Lewis Publishers, New York, pp: 313.
Ragas, A.M.J., P.A.G.M. Scheren, H.I. Konterman,
R.S.E.W. Leuven, P. Vugteveen, H.J. Lubberding,
G. Niebeek and P.B.M. Stortelder, 2005. Effluent
standards for developing countries: Combining the
technology- and water quality-based approach.
Water Sci. Technol., 52(9): 133-144.
Res. J. Appl. Sci. Eng. Technol., 10(11): 1293-1302, 2015
1302
Redda, M.A., 2013. Studies of the performance,
stability and reliability of various configuration of
the activated sludge process at full scale municipal
wastewater treatment plants. Ph.D. Thesis,
University of Texas at Arlington, US.
Shah, T.S.K., 2008. Performance evaluation of central
wastewater treatment plant: A case study of
Hetauda industrial district, Nepal. Environ. Nat.
Resour. J., 6(2): 36-51.
Smith, E.P., K. Ye, C. Hughes and L. Shabman, 2001.
Statistical assessment of violations of water quality
standards under section 303(d) of the clean water
act. Environ Sci. Technol., 35:606-612.
Thode, H.C., 2002. Testing for Normality. Marcel
Dekker, New York.
WHO, 2006. Guidelines for the Safe Use of Watewater,
Excreta and Greywater. Volume 2: Wastewater
Use in Agricluture. WHO, Geneva.
