This is part 1 of a 5 part series of results from our water monitoring lab. If you haven’t read our watershed report, head over here and check it out. In this first part, we are going to examine the high quality nature of the data generated by the water monitoring program. 
Last month we trained our 700th water monitoring volunteer. I am proud of the work that our many water monitoring volunteers do. Their dedication and skill is admirable to us in the lab and to the rest of the organization.
I am most proud of the high quality data our volunteers are able to produce. The data they generate can stand up on it’s own with any other laboratory. Ensuring high quality data is important to monitoring programs because high quality data tells a better story than questionable data. If you cannot be sure about the accuracy of a dataset, you cannot use it to identify and fix problems.
How do water monitoring volunteers collect good data?
The first step in ensuring high quality data is to have sampling methods that reduce the chance for errors. Any of the field samplers will tell you that the methods we use are a little bit over the top. We use a method developed by the EPA called Clean Hands/Dirty Hands. This method was developed for measuring very, very small amounts of metals in the water. Since the concentrations are so small, even a little bit of contamination can really mess things up. Even though it can be a bit of a pain, it seriously reduces the amount of sample contamination
We also have a pretty stringent Quality Assurance Project Plan , that describes all the field and laboratory process we go through to make sure only good data is kept, and poor quality data is discarded. This plan has been read over and approved by California Department of Water Resources, San Diego County Water Authority, and the San Diego Regional Water Quality Control Board. Among other things, randomly assigned sites have duplicate samples taken, randomly assigned samples are run in the lab twice, and clean distilled water is tested. If these duplicate or blank results show some funny business, we will look hard at the data and throw out possibly questionable data.
Our volunteers generate professional quality research data and should feel proud of the work they do. I know I am.
