UC Berkeley

Drinking water monitoring is essential for identifying health-related risks, as well as for building foundations for management of safe drinking water supplies. However, statistical analyses of drinking water quality monitoring data are challenging because of non-normal (skewed distributions) and missing values. Therefore, a new method combining a water quality index (WQI) with spatial analysis is introduced in this paper to fill the gap between data collection and data analysis. Water constituent concentrations in different seasons and from different water sources were compared based on WQIs. To generate a WQI map covering all of the study areas, predicted WQI values were created for locations in the study area based on spatial interpolation from nearby observed values. The accuracy value of predicted and measured values of our method was 0.99, indicating good predication performance. Overall, the results of this study indicate that this method will help fill the gap between the collection of large amounts of drinking water data and data analysis for drinking water monitoring and process control.