Pacific Ecological Services Division Project (PESD)

Since 1996 CSS has conducted field and lab-based research in support of the Pacific Ecological Services Division Project

Analytical Chemistry Support for the US EPA National Aquatic Resource Survey

CSS chemically analyzes hundreds of water samples collected annually from the US EPA’s National Aquatic Resource Survey (NARS), a critical contribution to the EPA assessments of the condition of the nation’s water bodies.

IT Support to NOAA Fisheries

Our IT services support included developing and integrating information management systems central to NOAA Fisheries’ management and protection of living marine resources and their habitats

Expert Guidance to EPA’s Consequence Management Advisory Division

Technical Support for EPA’s Consequence Management Advisory Division (CMAD) – CSS provides expert technical guidance and field support related to chemical, biological, radiological, and nuclear (CBRN) decontamination.

Biogeographic Assessment of the Main Hawaiian Islands (MHI)

CSS compiled and visualized data showing the distribution of habitats and marine organisms, to establish a baseline for assessing and monitoring potential environmental impacts from renewable energy projects.

Social Values Relative to Wind Energy

CSS assisted in the assessment of social values related to understanding and support of offshore wind energy sitings off North and South Carolina.

Seafloor Substrate Mapping and Model Validation of Offshore Wind Sites

CSS supported developing a comprehensive seafloor characterization in support of a $3 billion offshore wind energy project in the coastal waters of New York.

Predictive Modeling/Spatial Planning, NY Bight

CSS designed and implemented this integrated data acquisition, data management, and statistical predictive modeling to select optimal wind farm locations while minimally impacting seabirds.

Ecologically Sound Offshore Wind Development

CSS designed and implemented this integrated data acquisition, data management, and statistical predictive modeling to select optimal wind farm locations while minimally impacting seabirds.