Table of Contents

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

Continuous water-quality monitoring was conducted in five salt marsh ponds (mean water depths <1m) located within SMIIL from April 2023–June 2024. Water quality sensors were mounted approximately 15 cm above the bottom of the pond to a fixed pole. Each pond was equipped with internally logging HOBO sensors (Onset Computer Corp.) that measured pressure, conductivity/salinity, dissolved oxygen (DO), and pH every 10 minutes. Temperature was also recorded by each of the sensors, and temperature data provided here reflects the median of the four sensors in each pond. An additional HOBO pressure sensor, deployed at a central location within SMIIL, recorded air temperature and barometric pressure. During maintenance trips every 4-16 weeks, sensors were retrieved, cleaned, recalibrated (DO and pH only) and redeployed in each pond for a total of five deployments. Data files from each sensor were downloaded during each maintenance visit. Discrete surface-water samples for DO, dissolved inorganic carbon (DIC), and total alkalinity (TA) measurements were also collected during maintenance visits and used to validate DO and pH time series data (see discrete sample dataset, also in this project).
 
HOBO DO loggers were calibrated every service trip before deployment using two points: a 100% saturation open air point and a 0% point using sodium sulfite dissolved in deionized water. HOBO pH loggers were calibrated every service trip before deployment using three-point calibration standards: pH 4.0, pH 7.0, and pH 10.0.

All data were processed through a combination of manual and automated quality checks. Quality control procedures were based on protocols adapted from the U.S. Integrated Ocean Observing System (IOOS) Quality Assurance/Quality Control of Real-Time Oceanographic Data (QARTOD) recommendations (IOOS, 2018; Palevsky et al., 2024; Chua et al. (2025) related dataset in this project).

First, all deployments were merged into a continuous time series and retimed to 10-minute intervals. Depth was calculated using pressure measurements in the ponds and barometric pressure from a central location at SMIIL using the Gibbs Seawater Toolbox (GSW, McDougall and Barker 2011). Time series from each pond were manually inspected with “human-in-the-loop” (HITL) annotations to identify and document suspect depth data. Points flagged for review were compared to service trip logs in order to diagnose the reason for flagged data (e.g., periods where sensors were not fully submerged in seawater). The time periods for flagged depth data that were associated with service trips were then removed for all parameters.
 
In each pond, each individual HOBO sensor measured temperature in addition to pressure, conductivity, pH, or oxygen. The temperature time series presented for each pond is the median temperature collected by the four sensors in each pond. We conducted gross range tests on the median temperature, calculated salinity, pH, and oxygen to ensure data fell within reasonable ranges. Ranges for each parameter are kept consistent across all ponds. For the time points flagged by temperature or salinity gross range tests in each pond, the corresponding oxygen and pH values were flagged and removed from the pond’s dataset.
 
Salinity (unitless) was calculated from conductivity and temperature using the Gibbs Seawater Toolbox. Salinity data was observed to be “spikier” than other parameters with acute changes in salinity occurring without the expected corresponding changes in temperature. As such we identified these spikes using the “isoutlier” function in MATLAB that determines outliers as a point that is over three standard deviations from the moving mean calculated using a three hour window. If the identified “spikes” resulted from points greater than 0.05 salinity units from the moving mean then the point was removed. Time points flagged as salinity “spikes” were removed from the corresponding oxygen and pH time series.
 
Onset HOBO Conductivity data loggers are not capable of internal calibration, so data were compared with nearby channel station salinity, water level, and temperature data (Gull Platform, ~1 km away, see Chua et al. related dataset) for salinity calibration and validation. During certain high tides, water from the channel rises above the marsh platform and enters the ponds. During these high tides, pond depths were observed to increase and decrease tidally, and pond temperatures were similar to that in the channel, consistent with pond flushing. However, the preliminary salinity data from the HOBO loggers in the ponds were offset from the channel salinity data during this period, indicating offsets in the HOBO Conductivity measurements. In order to correct for these calibration offsets, we identified high tide periods when channel water entered the ponds based on water depth and temperature data and determined calibration offsets for the pond salinity measurements by correcting the pond salinity to match that of the channel during these high tide periods. Salinity data reported here for the Ring, Shark, and Long Reach ponds were corrected using this approach. For the final two ponds (White and Drum), the HOBO Conductivity sensors deployed at these sites malfunctioned and did not report any usable data. Salinity data reported for White and Drum ponds are the median calibrated salinity values from the three ponds that did not experience sensor malfunctions.
 
DO concentrations (μmol/L) that passed quality control procedures were adjusted for temperature, salinity (calibrated as above) and pressure using the Aanderaa Data Instruments (2017) compensation equations to provide temperature, salinity and pressure compensated oxygen concentrations (μmol/L). We calculated the density using the GSW Toolbox with the calibrated salinity, temperature and depth variables to provide DO concentrations in μmol/kg. DO equilibrium saturation (DOeqsat) was calculated using the GSW Toolbox according to Garcia & Gordon (1992). DO percent saturation (DOsat) was then calculated as the ratio of the observed DO concentration (DOconc) to the equilibrium saturation concentration (DOeqsat), and multiplied by 100.
 
Calibration accuracy for both DO and pH was validated based on discrete samples collected at the beginning and end of each deployment (see related dataset). For comparison, pH, DIC, and TA data were converted to hydrogen ion space ([H+]) using the CO2SYSv3 MATLAB package (Lewis & Wallace, 1998; Sharp, 2023; Van Heuven et al., 2011). 

- Loaded data from "all_data.csv" in CSV format, using row 1 as headers, treating empty strings, "nd", and "NaN" as missing values
- Converted field "datetime_utc" from format "%d-%b-%Y %H:%M:%S" (UTC) to ISO 8601 format "%Y-%m-%dT%H:%M:%SZ" (UTC), output as datetime type
- Output final dataset to "988873_v1_salt_pond_monitoring.csv"

Salt marshes are highly productive, dynamic coastal environments that experience large fluctuations in biogeochemical parameters such as dissolved oxygen and pH in response to both natural forcings and anthropogenic impacts. At present, we have a limited understanding of the magnitude of biogeochemical variability in coastal habitats, hindering our ability to predict how they will respond in the future to episodic events and long-term change. This incomplete picture owes to a lack of sustained water quality measurements in coastal and estuarine systems worldwide.

This project investigates biogeochemical cycling in the Seven Mile Island Innovation Laboratory (SMIIL), a network of tidal marshes and channels in coastal New Jersey that is a site of historic and contemporary dredging and a testbed for marsh restoration techniques such as beneficial use of dredged sediment. It leverages multiple data sets of continuous, high-frequency (10-minute) measurements of physical and biogeochemical parameters, including water depth, temperature, salinity, dissolved oxygen, pH, chlorophyll a, and turbidity, as well as precipitation, wind speed, wind direction, temperature, relative humidity, and atmospheric pressure data. Two long-term biogeochemical sensor data sets, comprising a three-year time series at three sites in the main marsh channel (from June 2021–June 2024) and 1-2 year time series at six distinct salt ponds (August 2022–June 2024), were collected. Additionally, short-term (~months long) dredging and sediment placement monitoring data were collected at five different locations in 2022 and 2023. Data collection was designed to capture a variety of natural (diel, tidal, seasonal, storm-related) and human-created (sediment dredging and placement) conditions. Factory-calibrated biogeochemical sensors were field calibrated and maintained every 4 to 12 weeks. Discrete samples for dissolved oxygen, total alkalinity, and dissolved inorganic carbon were collected at the start and end of each sensor deployment for additional sensor calibration and validation. Altogether, our high-quality, multiyear dataset provides critical insights into the inherent variability of biogeochemical conditions in temperate salt marshes on diel and seasonal timescales, as well as how they may respond to transient events (e.g., storms, dredging activities) and over the longer term.