Water Quality Data - RAPID Maria Project

Hurricane Maria is an example of a natural disaster that caused disruptions to infrastructure resulting in concerns with water treatment failures and potential contamination of drinking water supplies. This dataset is focused on the water quality data collected in Puerto Rico after Hurricane Maria and is part of the larger collaborative RAPID Hurricane Maria project.

This resource consists of Excel workbooks and a SQLite database. Both were populated with data and metadata corresponding to discrete water quality analysis of drinking water systems in Puerto Rico impacted by Hurricane Maria collected as part of the RAPID Maria project. Sampling and analysis was performed by a team from Virginia Tech in February-April 2018. Discrete samples were collected and returned to the lab for ICPMS analysis. Sampling was also conducted in the field for temperature, pH, free and total chlorine, turbidity, and dissolved oxygen. Complete method and variable descriptions are contained in the workbooks and database. There are two separate workbooks: one for ICPMS data and one for field data. All results are contained in the single database. Sites were sampled corresponding to several water distribution systems and source streams in southwestern Puerto Rico. Coordinates are included for the stream sites, but to preserve the security of the water distribution sites, the locations are only identified as within Puerto Rico.

The workbooks follow the specifications for YAML Observations Data Archive (YODA) exchange format (https://github.com/ODM2/YODA-File). The workbooks are templates with sheets containing tables that are mapped to entities in the Observations Data Model 2 (ODM2 - https://github.com/ODM2). Each sheet in the workbook contains directions for its completion and brief descriptions of the attributes. The data in the sheets was converted to an SQLite database following the ODM2 schema that is also contained in this resource. Conversion was performed using a prototype Python translation software (https://github.com/ODM2/YODA-Tools).