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Jellyfish Compliance with SQIDEP · MCU15/0122
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Table 1. Influent and effluent data for TSS, TP and TN.
Event TSS EMCi TSS EMCe TSS CRE TP EMCi TP EMCe TN EMCi TN EMCe
TP CRE (%) TN CRE (%)
Number (mg/L) (mg/L) (%) (mg/L) (mg/L) (mg/L) (mg/L)
1 180.0 13.0 93 0.2 0.2 0 1.3 1.3 0
2 170.0 2.0 99 0.4 0.0 93 2.8 0.5 82
3 19.0 2.0 89 0.1 0.1 44 1.1 0.5 55
4 26.0 4.0 85 0.2 0.1 65 3.1 1.1 65
5 25.0 4.0 84 0.2 0.1 29 1.9 1.9 0
6 51.0 10.0 80 0.5 0.2 68 6.0 2.3 62
7 74.0 8.0 89 0.3 0.1 74 3.2 1.6 50
8 21.0 5.0 76 0.5 0.1 80 5.7 2.5 56
9 13.0 1.0 92 0.2 0.1 63 2.0 1.3 35
10 18.0 3.0 83 0.14 0.09 36 1.3 1.0 23
Notes: EMCi: Event mean concentration of influent sample; EMCe: Event mean concentration of effluent sample; CRE: Concentration removal efficiency.
OUTLIER IDENTIFICATION Outlier
An outlier is defined as a data point that Max Observation Below
is distinctly separate from and inconsistent Upper Inner Fence
with the majority of the data points
(Walfish, 2006). Outliers can have negative
Q3
effects on data analyses as they skew and
distort the data set or, conversely, can
provide useful information about abnormal
characteristics of the data (Seo, 2006). Median
Therefore, it is a critical step to identify
Q1
and detect outliers.
A basic approach for identifying outliers Min Observation Above
is outlined in Tukey (1977), and involves Lower Inner Fence
simple numerical and graphical methods Figure 1. Definition of features in a Box
that are used when constructing box plots and Whisker Plot.
of the data (Walfish, 2006). It identifies a As seen in the Box and Whisker Plots in
potential outlier as any data point more Figure 2, a number of influent and effluent
than 1.5 interquartile ranges (IQRs) below
data points fell outside the inner fences,
the first quartile, or above the third
but were within the outer fences (see
quartile; that range is referred to as the
Tukey (1977) for calculation method of the
inner fences. An extreme outlier is a value
inner and outer fences). However, the data
that is outside the outer fences, and can be
between the inner and outer fences all
defined as three interquartile ranges below
fell within referenced parameters, which
the first quartile or above the third quartile
suggest typical concentration occurrences
(Tukey, 1977) and should be thoroughly
in Australia (Duncan, 1999); therefore, there
investigated. The box and whisker plots
is no justifiable reason to exclude any data
graphically display outliers, and examples
points from this data set. The CRE (%) data
can be seen in Figure 2 for the three
was also analysed for outliers, but all CRE
parameters of interest. Figure 1 describes
percentages fell within the inner fences.
the features of the box and whisker plot,
Therefore, all data points have been used.
including all relevant statistical measures.
NORMAL DISTRIBUTION
There is no rigid mathematical
VALIDATION
definition of what constitutes an outlier in
the literature; it is a subjective decision. An Following outlier evaluation, the next
analysis should be conducted to determine step in this approach is to test for data
why the outlier may have occurred, and if a normality using graphical methods
justifiable reason can be identified then the and parametric statistical analysis.
data point may be legitimately excluded The Stormwater Australia protocol calls
(Walfish, 2006). Most importantly, data for log-transformed inlet and outlet-
that have been excluded as outliers must paired samples (Stormwater Australia,
still be reported along with the reasons 2014), however if the baseline data
Figure 2. Box and Whisker Plots for TSS,
for their exclusion, as they still form part is already normally distributed then TP & TN, with outliers labelled with their
of the evaluation process. there is no requirement to conduct a event number.
WATER MAY 2015
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