February 2025 monthly summary for dataforgoodfr/13_pollution_eau focusing on PFAS data workflow improvements. Key features delivered: - PFAS Data Retrieval Notebook added to efficiently retrieve and filter PFAS-related data from the database. It includes a method to identify PFAS parameters by querying the database, validates that filtering by the cdparametresiseeaux field starting with 'PF' matches a predefined PFAS list, and provides SQL examples for retrieving PFAS parameters and filtering PFAS analysis results. - Commit reference 590584cce2b39afd1cbb54a4e2b62357ea42c78f documents and supports the PFAS data workflow. Major bugs fixed: - No major bugs reported or fixed this month in relation to PFAS data retrieval workflows. Overall impact and accomplishments: - Enables faster, reproducible PFAS data retrieval and validation, improving data quality for PFAS analysis and downstream decision-making. - Provides clear, reusable SQL templates for parameter extraction and analysis result filtering, reducing ad-hoc querying effort and onboarding time for new analysts. Technologies/skills demonstrated: - Jupyter notebook development, Python scripting, database querying and SQL templating. - Data validation logic for parameter matching (PFAS) and end-to-end PFAS data workflow. - Strong emphasis on business value: faster insights, higher data quality, and scalable analysis pipelines.