Applications of Statistical Analyses on Water Quality data & its recent research trends - Statswork

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

II. STATISTICAL TECHNIQUES The common statistical analysis or techniques to handle water quality data is as follows: Statistical

Commonly

Analysing water quality data entails

Technique

applied to

reviewing and assessing the data to see if

Trend analysis

Rainfall

any errors were made during the sampling

Correlation

Flowing quality of

or analysis of the water quality sample or

water

data entry. To detect any issues regarding

surface,

data, a series of data checks should be

water quality

performed. It includes data checking in the

Regression analysis

on

any

drinking

Sanitary

water

quality

first stage, i.e. data entry, whether the data is within the range of parameters, data is

Autocorrelation

Water

within the detection limits, etc. However,

analysis

measured

quality at

there are different aspects of water quality,

several point of

and the techniques suitable for each field

time

are tabulated in the following subsection.

Testing

One way to analyse the water quality data

Hypothesis

Statistical Comparing

the

water quality of

is using graphical techniques. The benefits

two or more rivers

of graphical representation of data

or regions

include finding the data trend, finding outliers in the data, etc.

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Statistical Modeling

Predicting outcome

future like

1

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.

2. Correlation:

(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

The result revealed a strong trend associated with flood and drought during extreme rainfall. These methods' validity showed that the

seasonal

method trend

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.

Mann-Kendall test.

proposed

Correlation is basically to identify

detects

the

accurately than

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.

using the other two test methods.

Then common statistical technique

Our Data collection service help in

to analyse the data is using

collect clean data and maximize

Spearman's

your impact.

coefficients. It can identify the

rank

correlation

relationship between the water quality parameters and the various land usage at different times. The

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2

correlation matrix will look like the

However, selecting a suitable statistical

following table 1.

analysis

A 1.00

B 0.34 1.00

water

quality

analysis

technique depends on the data and the

Table 1: Sample Correlation Matrix X/Y A B C

or

research question. The choice of suitable

C 0.72 0.80 1.00

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

3. Regression analysis: Regression

for the research problem. The most

analysis helps find the average

important

relationship between the variables

Statswork

and is useful to predict future

statistical analysis service to get high

outcomes.

quality data.

is

the

provide

sampling

strategy.

suitable

online

4. Autocorrelation analysis: If we III. FUTURE SCOPE

want to understand the relationship between two or more similar

There are numerous statistical procedures

attributes measured at different

or techniques to analyse the water quality

time points, then autocorrelation

data. Since water scarcity is increasing due

analysis can be used.

to lack of rainfall in many regions, finding

5. Testing statistical hypothesis

the water quality for the recycled water,

6. Statistical modelling: It is used to

research related to turning the hard water

identify the behaviour and predict

to soft water, etc. are considered future

future

research scope.

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

REFERENCES: 1.

Al

Saad

Z.A.A.,

Hamdan

A.N.

(2020)

and range chart. If any data points

Evaluation of Water Treatment Plants Quality

are scattered away from the limits,

in Basrah Province, by Factor and Cluster Analysis.

they can be considered defective and treated as an outlier(s).

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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3

(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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