Water Quality Modelling Paper

Running head: WATER QUALITY MODELLING 1
Water Quality Modelling Paper
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WATER QUALITY MODELLING 2
Water Quality Modelling
Question 1
The treatment plant records are for the period between 2010 and 2017. Although the data is
available for all years between the specified period; 2010 and 2017 only have data for 3 months
and 5 months respectively. The data is presented in different categories depending on the
quantity being assessed. For instance, in order to obtain highly quality drinking water the unclean
water is subjected through as series of purification steps that are classified under several
categories such as, raw water, coagulated water, floated water, filtered water, transfer water, and
treated water. The columns of data do not have the same number of values with some being
highly populated and others having considerably few values in them e.g. Manganese columns
have very few values in them. The assessment of some parameters is differentiated into
laboratory and online; denoting the source that was used to get the values presented in the rows.
Question 2
Part a
From the data presented in the excel document it is clear there are some data values that are more
important than others. As such, the necessary parameters to be used in the WTC-Coag
assessment are as follows: Colour (COLOUR), Turbidity (TURB), Target coagulation pH,
Enhance Coagulation (EC), UV absorbance at 254nm (UV), Aluminium Sulphate (ALUM). The
parameter aforementioned at the most important with regard to the computations relating to
water purification The three parameters that will be employed in the calculation of WTC-Coag
are UV, COLOUR, and TURB. Other parameters will be used in conjunction with the ones
mentioned above to compute alkalinity, alum dose rate, % removal of coagulable DOC, and pH
changes.

WATER QUALITY MODELLING 3
Part b
There are several data errors but the most notable ones relate to matters of data entry where none
numeric values are entered e.g. "*" and "plant full". The use of non-numeric values could also be
a misplacement error where a statement like "plant full" is supposed to be in the comment
column as a justification of why not records are present for a given date. Overestimation error
results in the entry of considerably large data values that are not consistent with other data values
in the same category or column. Another error is the failure to use consistent units of measure:
for instance, water is supposed to be assessed in mega litres there are however some instances
where it is represented in litres, even thou it can be easily transcribed into mega litres. Lastly,
there are numerous missing values; in some categories the missing values are considerably large,
but in others they are few.
Part c
In the cases that none numeric values have been entered they should all be replaced by a value
that is an average of the four neighbouring values in the same column or category (preferably the
values should be from the same year). For large missing sets of data, specific column should be
ignored completely. For individual missing values the specific row of that column can be ignored
to avoid overestimation or underestimation of the actual value.
Part d
Irrelevant data: I will delete all rows with data for the period between 2010 and May 20 of 2014;
so that we are left with three years of data for the period between May 21of 2014 and May 21 of
2017. This is appropriate to do because the assessment of the data needs to be done with regard
to most recent three years of data.