Ocean Acidification in Massachusetts and Cape Cod Bay

· 02/2024 - 01/2026

Strategic Focus Area: Healthy Coastal Ecosystems

Lead PI: Ryan Woosley, Massachusetts Institute of Technology


  1. Build on established water quality monitoring stations to add OA monitoring to Cape Cod Bay.
  2. Determine magnitude of temporal and spatial distribution of OA in the bay.
  3. Determine the specific requirements for an OA monitoring network for Cape Cod Bay.
  4. Use traditional regression based and machine learning models to develop an OA model for Cape Cod Bay.
  5. Apply above models to historical data to determine past OA conditions in the Bay, and use emissions scenarios to predict possible future conditions.
  6. Partner with local environmental and community organizations to collect samples and disseminate results to the broader public.

The total alkalinity (TA), dissolved inorganic carbon (DIC), and pH will be analyzed using standard operating procedures at 14 stations around Cape Cod Bay. The samples will be collected by local community water quality monitoring groups and analyzed in the Woosley lab at MIT. The carbon data will be combined with other water quality monitoring parameters (temperature, salinity, nutrients, oxygen, etc.) already determined by partner groups to develop a traditional and machine learning models of ocean acidification in the Bays. The models will be used to determine distribution of sampling sites and timing required to monitor ocean acidification in the region.

Ocean acidification occurs globally, but due to the highly dynamic nature of coastal regions it is poorly understood on the local scale. Ocean acidification is known to have a myriad of (mostly negative) impacts on organisms and ecosystems, particularly economically important organisms (e.g. shellfish). Despite the known impacts on natural and economic resources Massachusetts does not have an ocean acidification monitoring network. Although a two year project cannot provide a sustained monitoring network, it can provide baseline measurements and create models to estimate past and future acidification. It can also inform the requirements (spatial and temporal) needed to develop one.