Water quality index

A water quality index is a synthesis of numerous water quality criteria into a single numerical value. It provides a comprehensive assessment of water quality and can be used to track changes over time.

The steps below can be used to create a water quality index using historical data:

 

1. Determine the criteria governing water quality: Determine the precise parameters pertinent to the water system under consideration. pH, dissolved oxygen, biochemical oxygen demand (BOD), total suspended solids (TSS), nitrogen compounds, phosphorus compounds, and different chemical pollutants are examples of common metrics.

 

2. Establish the rating system: Assign rating values to each parameter depending on concentration or measurement. A grading scale of 1-10, for example, can be employed, with 1 indicating great water quality and 10 indicating bad water quality. Scientific criteria or regulatory standards should be used to determine the rating values.

 

3. Give each parameter a weight based on how important it is to establishing the overall quality of the water. Stakeholder input or expert judgment can be used to calculate the weights.

 

4. Determine the sub-index for each of the following parameters: To get a sub-index value for each parameter, multiply its rating value by its corresponding weight.

 

5. Determine the overall water quality index: Add the sub-index values for each parameter to determine the overall water quality index. This index outputs a single value that measures the total water quality at a given location and time.

 

 

 

6. Interpret and examine the findings: To measure the water quality status, compare the derived water quality index with specified benchmarks or standards. Examine the index's trends over time for any changes or patterns in water quality.

 

It is crucial to remember that the precise formula for generating a water quality index may change depending on regional laws, policies, or particular project requirements.

 

WQI models have been widely used for water quality assessment due to their simplicity and relatable output. However, the structures and mathematical techniques used in these models vary greatly. Most WQI models involve four stages: selection of water quality parameters, determination of parameter sub-indices, determination of parameter weightings, and aggregation of sub-indices to compute the overall water quality index. 

These models are region/site-specific, with selection based on waterbody type, current uses, local water quality guidelines, and data availability. There is significant variability in the number and type of water quality parameters, weightings, and criteria used to develop sub-index values, making it difficult to compare applications across different study areas. Some streamlining of WQI models, such as incorporating international guideline values, and updating models considering new parameters of interest are crucial for increased use. Eclipsing and uncertainty are key issues affecting the accuracy of model outputs. 

Model development has relied heavily on expert panel opinions, but this can introduce uncertainty into the models. More recently, mathematical techniques like principal component analysis and cluster analysis have been used to inform parameter selection and weightings, while computer-based techniques like fuzzy interface systems and artificial neural networks have been used to reduce uncertainty in the final aggregation process.