This dataset contains discrete carbonate data (TA, DIC, pH, Temperature, Salinity), collected as part of the study described below. See the "Related Publications" section for autonomously collected pH data from this study. Study description: The increase in atmospheric carbon dioxide (CO2) over the last 200 years has largely been mitigated by the ocean’s function as a carbon sink. However, this continuous absorption of CO2 by seawater triggers ocean acidification (OA), a process in which water becomes more acidic and more depleted in carbonate ions that are essential for calcifiers. OA is well-studied in open ocean environments; however, understanding the unique manifestation of OA in coastal ecosystems presents myriad challenges due to considerable natural variability resulting from concurrent and sometimes opposing coastal processes--e.g. eutrophication, changing hydrological conditions, heterogeneous biological activity, and complex water mass mixing. This study analyzed high temporal resolution pH data collected during 2022 and 2023 from Narragansett Bay, RI--a mid-sized, urban estuary that since 2005 has undergone a 50% reduction in nitrogen loading with weekly, discrete bottle samples to verify sensor data. We used autonomous data for pH, temperature, salinity, and dissolved oxygen from 4 sensors in Narragansett Bay. The autonomous data spanned over a year from 2022 to mid-2023 and had temporal resolutions between 10 and 15 minutes. The data have been subjected to QA/QC protocols, such that all pH measurements are final and quality-controlled. As well, pH values normalized to 15°C (using PyCO2SYS) are included. All pH values are in total scale. Discrete samples were taken weekly at the Narragansett Bay Long Term Phytoplankton Time Series site and monthly from Greenwich Bay, collocated with 2 of the sensors. Discrete data were analyzed in lab for dissolved inorganic carbon and total alkalinity, and include in situ temperature and salinity.

Table of Contents

• Coverage
• Dataset Description
  • Methods & Sampling
  • Data Processing Description
  • BCO-DMO Processing Description
• Data Files
• Related Publications
• Related Datasets
• Parameters
• Instruments
• Project Information
• Funding

This dataset contains discrete carbonate data (TA, DIC, pH, Temperature, Salinity). See the "Related Publications" section for autonomously collected pH data also described in this methodology.

The Mount View (MV) and Quonset Point (QP) sensors are maintained by the Narragansett Bay Fixed Station Monitoring Network (NBFSMN). Each sensor measures pH, along with temperature, salinity, and DO in the surface, every 15 and were deployed only from spring to fall.  Complete documentation provided by the Rhode Island Department of Environmental Management (RIDEM, 2020; https://dem.ri.gov/sites/g/files/xkgbur861/files/programs/benviron/water/quality/surfwq/pdfs/nbfsmn.pdf).

The Conanicut Hydrocat 620 and Potowomut Hydrocat 720 sensors are maintained by the Rhode Island Consortium for Coastal Ecology Assessment, Innovation, and Modeling (RI C-AIM; https://data-explorer.riddc.brown.edu/dataset/buoy-telemetry/summary) and Rhode Island Sea Grant. Each sensor measures pH, along with temperature, salinity, and DO, every 15 minutes approximately 1 meter below the surface (RI C-AIM, 2024). The hydrocat sensors were deployed year-round and were recovered every 2 to 4 weeks, weather permitting, for maintenance at which point the flow path and the conductivity cell are flushed with 1% Triton detergent followed by vigorous flushing with DI water.

Discrete bottle samples were collected to verify the sensor data. Samples for the southern region of the bay (i.e. near QP and Conanicut buoy) were collected weekly as part of the Narragansett Bay Long Term Phytoplankton Time Series (PLT), which is located just off the Conanicut buoy. The time series typically collects samples on Monday mornings at approximately 7:30am, barring weeks when inclement weather conditions delayed or canceled sampling. Samples for the northern region of the bay (i.e. Greenwich Bay near MV and Potowomut buoy) were collected approximately once a month. After collection, samples were poisoned with 100 uM  of saturated mercuric chloride solution and stored in the refrigerator until analysis for DIC and TA. DIC and TA were measured according to Dickson et al. (2007) using the Apollo SciTech Model AS-C6L Dissolved Inorganic Carbon Analyzer and the Apollo SciTech Model AS-ALK3 Total Alkalinity Titrator. Instruments were calibrated to Certified Reference Material (CRM) from Scripps Institute of Oceanography at room temperature (21 - 22° C). For DIC analysis, a sample of CRM was run prior to and at the end of sample analysis for quality control. TA analysis was calibrated to CRM, and either a sample of CRM or aged open ocean water was run at beginning of daily analysis and end of daily analysis for quality control. Lab-based measurements carry a ±0.2% uncertainty for alkalinity and ±0.1% uncertainty for DIC.

Instruments:
(additional context to accompany the list in the "Instruments" section)

The Mount View (MV; EXO V2) and Quonset Point (QP; 6600EDS) sensors are YSI brand multi-parameter sondes maintained by the Narragansett Bay Fixed Station Monitoring Network (NBFSMN). Both sondes were equipped to measure temperature, salinity, dissolved oxygen, pH [NBS], depth, and chlorophyll. The surface equipment was housed in a tube on a monitoring buoy station with the instrument depth about 1 meter from surface. All NBFSMN data were subject to quality assurance measures including verification of calibrations and consistency among multiple instruments, corrections for sensor drift and biases due to biofouling, removal of outliers, and interpolation across selected intervals of missing data, in accordance with the NBFSMN’s EPA approved Quality Assurance Project Plan (RIDEM, 2020). To keep consistency among instruments, servicing protocols consist of swapping the deployed instruments with newly calibrated instruments on a 2-week interval to minimize biofouling. All data are verified through a three-point comparison: data from the retrieved sonde are compared to the newly calibrated sonde, as well as an independent profiling sonde, all at the deployment depth. Outliers and data errors are removed based on criteria set in the NBFSMN’s Quality Assurance Project Plan. Data correction and gaps in coverage on average affect up to 6% of the record at an individual station for an annual assessment. Corrections were filled using linear interpolation and/or sensor offsets following Quality Assurance and Quality Control (QA/QC) protocols detailed in the NBFSMN’s Quality Assurance Project Plan.

The Conanicut Hydrocat 620 and Potowomut Hydrocat 720 sensors were equipped with Seabird Hydrocat-EP CTD sensors (model number# HC-EP.1011S), equipped with a Seabird Hydrocat-EP pH Module (model#802532). Hydrocat-EPs collected multiple parameters including temperature, salinity, pH, dissolved oxygen. The hydrocat sensors were deployed year-round and were recovered every 2 to 4 weeks, weather permitting, for maintenance at which point the flow path and the conductivity cell are flushed with 1% Triton detergent followed by vigorous flushing with DI water. Following the cleaning, conductivity checks with conductivity standards (1413 μS,10,000 μS) ensures proper function, as well as a three point pH calibration of the pH Module (pH 4,7,10). pH calibrations were performed in Seabird's UCI software using their pH calibration wizard. Sensors were sent back to Seabird Scientific for yearly manufacturer calibrations. QA/QC was conducted in R using the R package OCE (Kelley & Richards, 2024), following QA/QC tests established by OOIO (NSF Ocean Observatories Initiative, 2012) including, a stuck value test, despiking, and a range test against global values for measured parameters provided by OOIO and local values in Narragansett Bay provided by the Narragansett Bay Long-Term Plankton Time Series (https://web.uri.edu/gso/research/plankton/).

DIC and TA in discrete samples were measured according to Dickson et al. (2007) using the Apollo SciTech Model AS-C6L Dissolved Inorganic Carbon Analyzer and the Apollo SciTech Model AS-ALK3 Total Alkalinity Titrator. Instruments were calibrated to Certified Reference Material (CRM) from Scripps Institute of Oceanography at room temperature (21 - 22° C). For DIC analysis, a sample of CRM was run prior to and at the end of sample analysis for quality control. TA analysis was calibrated to CRM, and either a sample of CRM or aged open ocean water was run at beginning of daily analysis and end of daily analysis for quality control. Lab-based measurements carry a ±0.2% uncertainty for alkalinity and ±0.1% uncertainty for DIC.

Temperature and salinity in the discrete carbonate data were measured with the Seabird instruments (analogous sites in the autonomous dataset).  Seabird Hydrocat-EP CTD sensors (model number# HC-EP.1011S), equipped with a Seabird Hydrocat-EP pH Module (model#802532). Hydrocat-EPs collected multiple parameters, including temperature, salinity, pH, and dissolved oxygen. 

This dataset contains discrete carbonate data (TA, DIC, pH, Temperature, Salinity). See the "Related Publications" section for autonomously collected pH data also described in this methodology.

We performed a gross range test on sensor data to eliminate unrealistic data (i.e. pH measurements less than 5 or greater than 10, or DO less than 1 mg L^-1) that indicate biofouling or other sensor malfunction. We then removed obvious outliers--any data points that were more than 3 standard deviations greater or less than the sensor's annual mean or any data points that were more than 1.5 standard deviations greater than or less than the sensor's 24-hour moving mean. 

Autonomous observations were compared to discrete samples. Occasionally, periods of sensor data diverged from bottle sample data with consistent, identifiable bias--for instance, a consistent underestimation or overestimation of pH by a value less than 0.1 pH unit or a dynamic bias less than 0.1 pH unit that changes with temperature or time. In the case of QP, we corrected this by adjusting all the sensor data points by the mean difference between sensor pH and bottle pH; specifically, we lowered QP pH by 0.084, the mean amount by which QP overestimated pH. Conanicut Hydrocat 620 overestimated pH in January-March 2023 when temperatures are lowest. We applied a more dynamic correction as a third order polynomial function of temperature, such that sensor pH was adjusted to be lower at colder temperatures.  Conanicut Hydrocat 620 and QP sensors were compared to weekly bottle samples collected as part of the Narragansett Bay Long Term Phytoplankton Time Series, whereas Potowomut Hydrocat 720 and MV sensors were compared to bottle samples collected approximately monthly at the same location at the Potowomut sensor.

pH measurements from all sensors were originally on NBS scale but converted to total scale using the Python PyCO2SYS package (Humphreys et al., 2022).

* The data table within the submitted file "DiscreteData_Baskind2024.csv" was imported into the BCO-DMO data system for this dataset.  Table will appear as Data File: 961940_v1_discrete_carbonate-narr-bay.csv (along with other download format options).

* Column names adjusted to conform to BCO-DMO naming conventions designed to support broad re-use by a variety of research tools and scripting languages. [Only numbers, letters, and underscores.  Can not start with a number]

* BCO-DMO requires all columns have names. After a conversation with data submitter, the un-named first column with a row id was dropped from the table.

* Additional site_lat, site_lon columns were added to the data table from site information included in provided metadata for the locations.

Coverage: Narragansett Bay, Rhode Island

NSF Award Abstract:
About one-third of human-produced carbon dioxide (CO2) has dissolved in the ocean, significantly slowing down climate change and global warming. However, as more CO2 dissolves in the sea, the ocean water becomes acidified because CO2 is a weak acid. We call this change ocean acidification (OA). OA has a significant and lasting impact on marine life, as acidified water causes shell dissolution for numerous marine species. US society is already grappling with the adverse effects of OA. OA is predicted to lead to economic losses of $400 million annually by 2100 for the US shellfish industry. Therefore, it is urgent to understand how OA is changing and the potential approaches to managing it in coastal waters. Presently, we do not have a good understanding of OA change at either the large or the small scale because so many competing processes impact OA.

One of the most dramatic processes that affect ocean acidification (OA) is the extra nutrient loading from rivers due to human inputs. While moderate nutrient levels are necessary to support marine life, nutrient levels that are too high can cause oceanic plankton blooms. As plankton die, they sink to the seafloor, where they are decomposed by bacteria and speed up OA where shellfish live. In recent decades, management agencies have been working on improving water quality by reducing the nutrient input to coastal waters, which has the potential to reduce the OA even though OA is not the initial motivation. To our knowledge, there has yet to be targeted research to understand how OA responds to aggressive nutrient reduction. The average nutrient input to Narragansett Bay? one of the most rapidly warming estuaries in the US?has been reduced over one-third in recent decades because of intentional nutrient reduction. Therefore, Narragansett Bay can be considered a natural laboratory that can help in better understanding the OA change that other coastal areas may soon experience resulting from environmental management actions and climate change. This project will leverage the existing water quality monitoring network, collect new data, and utilize a coastal biogeochemical model to quantify OA change and the mechanisms driving its change in Narragansett Bay, RI. Through engagement with the public and local shellfish growers, we will increase their awareness of OA and enhance their ability to carry out ongoing or new aquaculture operations in the near and long term. As more US states reduce nutrient input to improve coastal water quality, this project will allow management agencies nationwide to make next-generation refinements to best limit OA for sustainable fisheries and wildlife in the face of climate change.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria