Chemistry
Consulting

Independent environmental data validation clarifies the quality and usability of results guiding high-stakes decisions. Field and laboratory quality control are assessed against project requirements, methods, quality assurance project plans (QAPPs) and regulatory expectations, then qualifiers and defensibility are documented.

Evaluation of environmental forensics helps answer what happened, where it occurred and who may be responsible when contamination questions carry business and legal risk. The work focuses on building the right dataset and translating complex information into clear, defensible lines of evidence.

Data management supports end-to-end tracking of sampling events, from planning through laboratory deliverables, validation and reporting. This provides consistent status visibility and supports decision-making across complex programs.

Analytical approaches are evaluated and developed when standard United States Environmental Protection Agency (EPA) methods do not fit project chemicals of concern or site conditions. Support addresses non-routine matrices, low detection limits and interferences so the method approach matches project needs and stands up to scrutiny.

Quality assurance program documents are prepared and reviewed, including quality assurance project plans (QAPPs), sampling and analysis plans (SAPs) and standard operating procedures (SOPs). Project-specific data quality objectives (DQOs) align sampling and analytical design to the decisions the data must support.

PFAS forensics and source allocation support decision-making when PFAS are detected and potential sources are numerous. Targeted data evaluation and site-specific context help differentiate likely sources and support defensible source discussions.

Laboratory audits evaluate quality systems and operations that influence data reliability, with findings that support corrective action and risk reduction, including alignment to International Organization for Standardization (ISO) 17025 expectations when needed.

Field audits review sampling practices and field documentation against project requirements and recognized industry practices, focusing on issues that could affect data quality.

Data audits evaluate dataset structure and data handling for completeness, consistency and traceability so teams can use and share information with confidence.