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Applications Of Statistical Analyses On Water Quality Data And Its Recent

Research Trends

Dr. Nancy Agnes, Head, Technical Operations, Tutorsindia

info@ tutorsindia.com

Keywords:

Regression analysis, Testing Statistical

Hypothesis, Statistical Modelling, Data

collection, statistical analysis

I. INTRODUCTION

Analysing water quality data entails

reviewing and assessing the data to see if

any errors were made during the sampling

or analysis of the water quality sample or

data entry. To detect any issues regarding

data, a series of data checks should be

performed. It includes data checking in the

first stage, i.e. data entry, whether the data

is within the range of parameters, data is

within the detection limits, etc. However,

there are different aspects of water quality,

and the techniques suitable for each field

are tabulated in the following subsection.

One way to analyse the water quality data

is using graphical techniques. The benefits

of graphical representation of data

include finding the data trend, finding

outliers in the data, etc.

II. STATISTICAL TECHNIQUES

The common statistical analysis or

techniques to handle water quality data is

as follows:

Statistical

Technique

Commonly

applied to

Trend analysis Rainfall

Correlation Flowing quality of

water on any

surface, drinking

water quality

Regression analysis Sanitary water

quality

Autocorrelation

analysis

Water quality

measured at

several point of

time

Testing Statistical

Hypothesis

Comparing the

water quality of

two or more rivers

or regions

Statistical Modeling Predicting future

outcome like

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rainfall prediction,

etc.

Control Charts Quality of water is

under control

limits or not.

1. Trend analysis:

It acts as an important factor for

water quality analysis since it

helps the researcher understand the

data's variability. Wang et al.

(2020) proposed an innovative

trend analysis to detect or identify

the annual and seasonal rainfall

pattern. Data collection from

different meteorological stations

and compare the proposed method

with Theil-Sen trend method

Mann-Kendall test.

The result revealed a strong trend

associated with flood and drought

during extreme rainfall. These

methods' validity showed that the

proposed method detects the

seasonal trend accurately than

using the other two test methods.

Our Data collection service help in

collect clean data and maximize

your impact.

2. Correlation:

Correlation is basically to identify

the relationship between two or

more variables. It helps to identify

the variables which control the

variability in water quality data.

For example, consider a study on

water flowing quality on land, i.e.

water quality in the rivers. One can

take different research problem

based on the river data. However,

suppose our interest is to find the

seasonality of water quality in

selected areas and the land usage.

Then common statistical technique

to analyse the data is using

Spearman's rank correlation

coefficients. It can identify the

relationship between the water

quality parameters and the various

land usage at different times. The

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correlation matrix will look like the

following table 1.

Table 1: Sample Correlation Matrix

X/Y A B C

A 1.00 0.34 0.72

B 1.00 0.80

C 1.00

3. Regression analysis: Regression

analysis helps find the average

relationship between the variables

and is useful to predict future

outcomes.

4. Autocorrelation analysis: If we

want to understand the relationship

between two or more similar

attributes measured at different

time points, then autocorrelation

analysis can be used.

5. Testing statistical hypothesis

6. Statistical modelling: It is used to

identify the behaviour and predict

future outcomes through a

mathematical formulation.

7. Control Charts: It is used to

monitor the process and detect the

data variability using control limits.

The most popular is the mean chart

and range chart. If any data points

are scattered away from the limits,

they can be considered defective

and treated as an outlier(s).

However, selecting a suitable statistical

analysis or water quality analysis

technique depends on the data and the

research question. The choice of suitable

analytical technique is based on detection

limits, i.e. range of concentration of the

chemical component in the water, how

much accuracy and precision are needed

for the research problem. The most

important is the sampling strategy.

Statswork provide suitable online

statistical analysis service to get high

quality data.

III. FUTURE SCOPE

There are numerous statistical procedures

or techniques to analyse the water quality

data. Since water scarcity is increasing due

to lack of rainfall in many regions, finding

the water quality for the recycled water,

research related to turning the hard water

to soft water, etc. are considered future

research scope.

REFERENCES:

1. Al Saad Z.A.A., Hamdan A.N. (2020)

Evaluation of Water Treatment Plants Quality

in Basrah Province, by Factor and Cluster

Analysis. Journal of Water and Land

Development. 46 (VII–IX) pp. 10–19.

2. Yuefeng Wang, Youpeng Xu, Hossein Tabari,

Jie Wang, Qiang Wang, Song Song, Zunle Hu.

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(2020). Innovative Trend Analysis of Annual

and Seasonal Rainfall in the Yangtze River

Delta, Eastern China, Atmospheric Research,

231, 104673.

3. Tommaso Caloiero (2020). Evaluation of

Rainfall Trends in the South Island of New

Zealand

through the Innovative Trend Analysis (ITA).

Theoretical and Applied Climatology, 139, pp.

493–504.

4. Fikret Ustaoğlu, Yalçın Tepe, Beyhan Taş,

(2020). Assessment of stream quality and

health risk in a subtropical Turkey river

system: A combined approach using statistical

analysis and water quality index, Ecological

Indicators, 113, pp. 1 – 12.

5. Emma R. Kelly, Ryan Cronk, Emily Kumpel,

Guy Howard, Jamie Bartram, (2020). How we

assess water safety: A critical review of

sanitary inspection and water quality analysis,

Science of The Total Environment, 718, pp. 1 –

9.

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