Introduction:

Reviewing and assessing water quality data involves checking for any errors during sampling, analysis, or data entry. To identify such issues, several data checks should be conducted, including checking for data within parameter ranges, detection limits, and other criteria during the data entry stage. However, various aspects of water quality require different techniques, which are summarized in the following subsection. Graphical techniques are one way to analyze water quality data, offering benefits such as identifying data trends and outliers.

The following are commonly used statistical analysis techniques to analyze water quality data:

Trend analysis for rainfall
Correlation between flowing water quality on surfaces and drinking water quality
Regression analysis for sanitary water quality
Autocorrelation analysis for water quality measured at different points in time
Statistical hypothesis testing for comparing water quality between two or more rivers or regions
Statistical modeling for predicting future outcomes such as rainfall
Control charts to determine if water quality is within acceptable limits.

Trend Analysis:

It plays a crucial role in the investigation of water quality since it enables the researcher to comprehend the variability of the data.
Wang et al. (2020) developed an innovative trend analysis method to identify annual and seasonal rainfall patterns.
The study collected data from different meteorological stations and compared the proposed method to the Theil-Sen trend method and Mann-Kendall test.
The results indicated a strong trend associated with floods and droughts during extreme rainfall.
The validity of these methods showed that the proposed method accurately detects seasonal trends compared to the other two test methods.
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Correlation:

Correlation is a statistical analysis tool used to identify the relationship between two or more variables and to determine the variables that affect the variability in water quality data.
For instance, when studying water quality in rivers, one can investigate different research problems based on river data.
If the interest is to identify the seasonality of water quality in specific areas and its relationship with land usage, then Spearman’s rank correlation coefficient is a common statistical analysis technique used for data analysis.
This technique can determine the relationship between water quality parameters and land usage at different times.

Regression analysis:

Regression analysis is a useful statistical analysis technique that can assist in identifying the average relationship between variables, making it an effective tool for predicting future outcomes. Its primary objective is to calculate the average association between these variables, which can be utilized to make predictions about future outcomes. Regression analysis is widely used in various fields, including finance, economics, social sciences, and engineering, to name a few.

Autocorrelation analysis:

Autocorrelation analysis is a statistical method used to examine the relationship between two or more similar attributes measured at different time points.
Autocorrelation analysis is particularly useful when dealing with time series data, where the variables are observed over time.
By examining the autocorrelation of the variables, one can determine whether there is a relationship between them and how strong that relationship is.

Testing statistical analysis hypothesis

Statistical hypothesis testing is a process of making decisions about a population based on sample data.
The aim is to determine whether the sample data is consistent with the null hypothesis or if it deviates significantly from it.
Hypothesis testing involves specifying a null hypothesis and an alternative hypothesis, collecting data, and then using statistical analysis techniques to test the hypothesis.

Statistical analysis modeling:

Statistical modeling is the process of building mathematical models to describe or predict the behavior of a system or process.
Statistical models use data to describe the relationship between variables and can be used to make predictions about future outcomes.
Statistical analysis modeling is commonly used in fields such as finance, engineering, and social sciences.

Control Charts:

Control charts are used to monitor processes and detect data variability using control limits.
The most frequently used control charts include the mean chart and range chart.
Outliers that fall outside the control limits are considered defective and require treatment.

Choosing the appropriate statistical analysis or water quality analysis technique depends on the data and research question. The selection of a suitable analytical technique is based on factors such as the detection limits, which refer to the range of concentration of the chemical component in the water, and the required accuracy and precision for the research problem. Additionally, the sampling strategy is of utmost importance. For those seeking high-quality data, online statistical analysis services are available through Statistics Consultancy.

Future Scope:

There are numerous statistical analysis procedures and techniques available to analyze water quality data. Given the increasing water scarcity resulting from inadequate rainfall in many regions, future research will likely focus on analyzing the quality of recycled water and exploring methods to convert hard water to soft water. These areas of research hold significant potential for addressing water scarcity challenges and improving overall water quality.