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 An Academic presentation by Dr. Nancy Agnes, Head, Technical Operations, Statswork Group

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TODAY'S DISCUSSION Outline Introduction Statistical Techniques Future Scope

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.

STATISTICAL

The common statistical analysis or techniques to handle water quality data is as follows:

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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.

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These methods' validity showed that the proposed method detects the seasonal trend accurately than using the other two test methods.

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2. CORRELATION Correlation variables. quality data.

is basically to identify the relationship between two or more It helps to identify the variables which control the variability in water

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.

The correlation matrix will look like the following table 1.

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Table 1: Sample Correlation Matrix

3. REGRESSION ANALYSIS Regression analysis helps find the average relationship between the variables and is useful to predict future outcomes.

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4. AUTOCORRELATION ANALYSIS

Ifwewanttounderstandtherelationshipbetweentwoormoresimilarattributes measured at different time points, then autocorrelation analysis can be used.

5. TESTING STATISTICAL HYPOTHESIS 6. STATISTICAL MODELLING

It is used to identify the behavior and predict future outcomes through a mathematical formulation.

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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.

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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.

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FUTURE SCOPE There are numerous statistical procedures techniques to analyse the water quality data.

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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.

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