All original files were parsed and merged into a single, harmonized CSV format with consistent variable naming, column structure, and metadata fields. To ensure compatibility with other global synthesis efforts such as GLODAPv2 (Lauvset et al., 2024) and CODAP-NA (Jiang et al., 2021), all chemical concentrations were standardized to SI units, primarily in moles per kilogram of seawater (e.g. µmolkg-1; see Table 2). Metadata fields, including station coordinates, timestamps, and parameter units were normalized across all entries.

Missing data were encoded using the conventional placeholder value of “-999” and assigned a QC flag value of “9”. All reported values were initially assigned a flag of “2” (good) prior to QC screening. The flagging system used is summarized in Table 4.

Given the geographic extent of the EGSL as well as the regionally specific hydrography and water column stratification in the various channels, regional identifiers were assigned to each profile to avoid the identification of false outliers. The regional identifiers (Region) were defined as follows: Gulf of St. Lawrence = 1, Lower St. Lawrence Estuary = 2, Upper St. Lawrence Estuary = 3, and Saguenay Fjord = 4. Furthermore, binary identifiers were assigned to indicate if a profile was located within the Laurentian Channel (LC), Anticosti Channel (AC), or Esquiman Channel (EC) (1 = present, 0 = absent). These indicators were assigned manually based on station coordinates and their placement on a map in relation to bathymetry. Stations outside these three main channels were coded as a 0 in each column, whereas some samples lie in multiple or all channels, such as just inside Cabot Strait, where the three channel branches form, were assigned a value of 1 in two or more columns. This structure facilitates regional analysis and provides the framework for the quality control procedures described below.

In the absence of deep-water observations (>1500 m), a prerequisite for secondary QC techniques such as crossover analysis, the present dataset solely underwent primary QC. This approach follows the standards established in other large-scale syntheses like CARINA (Key et al., 2010; Tanhua et al., 2010), GLODAPv2 (Lauvset et al., 2024), CASTS (Coyne et al., 2026) and, perhaps most relevant to this effort, CODAP-NA (Jiang et al., 2021), with adaptations suited to the stratified and regionally heterogenous nature of the EGSL system.

Primary QC involves identifying and flagging implausible or inconsistent values within individual profiles, among regional clusters, and across property-property relationships. It does not attempt to apply multi-cruise consistency adjustments (e.g. inter-calibrated offsets) that would be inappropriate in dynamic, shallow coastal systems. Instead, we focus on producing a transparent and regionally contextualized dataset for users to build upon.

Given the distinct physical and biogeochemical characteristics of the four major subregions: the GSL, LSLE, USLE, and SF, each profile was assessed in the context of its regional variability. This region-specific approach avoided false flagging of valid data that deviate from global norms but are consistent within the regional oceanography (e.g., freshwater lenses, hypoxic zones, high nutrient layers, or acidified bottom waters).

All profiles were visually examined with parameters plotted against pressure (analogous with depth in this case) while overlaying with regionally aggregated profiles derived from the full dataset. Particular attention was paid to shape irregularities (e.g., sawtooth shapes, abrupt trend reversals, etc.) that often reflect bottle misfires, sampling contamination, or sensor drift. Obvious single-point outliers were assigned a QC flag of “3” (questionable) and retained in the dataset with their original values for transparency. To complement these visual diagnostics, automated acceptance criteria were applied to selected property-property relationships within each region. Robust (Huber-weighted) linear fits were generated for key pairs (NO3--AOU, N2O-AOU, δ13C_DOC-DOC, etc., where AOU stands for Apparent Oxygen Utilization (=[DO]sat-[DO]i-s). Fits and ±3σ envelopes, are shown and data falling outside of these thresholds were flagged as questionable (flag =3). This 3σ threshold effectively captures extreme deviations while preserving the natural scatter associated with spatial and seasonal variability. Elemental ratios, such as N:P, were evaluated qualitatively but were not used to assign flags as natural deviations from the Redfield ratios (Redfield et al., 1963) occur frequently in estuarine and coastal waters (Paradis-Hautcoeur et al., 2023). Likewise, internal consistency checks for the carbonate-system were performed but not used to modify flags. Together, these procedures constitute a transparent, regionally-tuned QC framework. The resulting flags and residual diagnostics provide a foundation for secondary evaluations and cross-calibration efforts. The proportion of observations flagged as questionable (flag =3) was low across all parameters, typically below 5 % (see Table 5), with no systematic patterns identified across cruises, regions, or sampling periods.

Dissolved oxygen data in this product originate from two sources: a Sea-Bird Scientific SBE43 CTD-mounted oxygen probe and discrete Winkler oxygen titrations. For all cruises, CTD dissolved oxygen data had undergone post-cruise calibration by the Principal Investigators (PIs) or scientific data managers through regression of matched sensor-Winkler pairs. The resulting cruise-specific fits were then applied to the unmatched CTD oxygen values to correct them to an inferred Winkler-equivalent concentration. These adjusted values were adopted in the present dataset. Subsequent tests confirmed close agreement between sensor and discrete values across the dataset, indicating that this calibration step was consistently implemented. The resulting adjusted CTD oxygen values are the ones reported in the final product.

Following Grégoire et al. (2021), we adopted a threshold of ±10 µmolkg-1 as the acceptable deviation between each sample that have both CTDOXY and paired Winkler measurements (see Fig. 3). If |CTD oxygen-Winkler|>10 µmolkg-1, the CTD oxygen value was noted as questionable (flag =3). To provide a single, consistent oxygen field for analyses, a composite variable “best_Oxygen” was generated using the following hierarchy: Winkler(flag=2)>CTD oxygen(flag=2)>Winkler(flag=3)>CTD oxygen(flag=3). The first available value in this sequence was adopted, and its original flag was propagated to the composite column. This ensures that the highest-quality measurement (usually Winkler titration) is retained where available while maintaining transparency for users.

The QC'd oxygen field (best_Oxygen in the data product; see Table 2) was subsequently used to calculate the AOU, as it serves as a diagnostic tracer to evaluate the internal consistency of other redox-sensitive parameters. AOU provides a quantitative measure of remineralization and is thus compared with other variables (i.e., nutrients, nitrous oxide etc.) in later QC steps and is included in the final data product.

First, TA was plotted against the practical salinity and the DSi concentrations for each region to identify obvious analytical or sampling outliers. Clear outliers were assigned flag =3 (questionable). Evaluations presented in this section are intended to illustrate data behaviour and patterns within the dataset and provide completeness of the description of the data. Comparisons between measured and calculated carbonate-system parameters are therefore used to highlight systematic offsets and visualize variability across regions and are not used as a basis for flag assignment. Carbonate-system calculations were performed in MATLAB version R2024a with the CO2SYSv3 package (Lewis and Wallace, 1998; Sharp et al., 2020). As prescribed by Dickson et al. (2007), the following constants were selected for these calculations: carbonic acid dissociation constants of Lueker et al. (2000), bisulfate dissociation constant of Dickson (1990), hydrofluoric acid dissociation constant of Perez and Fraga (1987), and total borate concentration of Lee et al. (2010).

It is important to note that the choice of equilibrium constants can significantly influence calculated carbonate-system parameters under low-salinity (<20) or cold (<8 °C) conditions. Several studies have shown that the constants of Cai and Wang (1998) return improved agreement of fCO2 and pH_TS values in low-salinity and estuarine waters (Delaigue et al., 2020; Dinauer and Mucci, 2017; Minor and Brinkley, 2022). Likewise, the formulations of Waters et al. (2014) have also been recommended for brackish waters. In colder coastal regions, the Lueker et al. (2000) constants may underestimate fCO2 and overestimate pH_TS in surface waters (Sulpis et al., 2020). Despite these considerations, the Lueker et al. (2000) constants were retained to maintain consistency with published best practices (Dickson et al., 2007) as well as GLODAP and CODAP-NA. The objective of this manuscript is to provide a consistent, quality-controlled dataset. Hence, a systematic assessment of constant selection and its impact on carbonate system calculations, while worthwhile, is beyond the manuscript's scope. A full propagation of uncertainty would require resolving multiple coupled equilibria involving numerous acid-base systems and equilibrium constants (Carter et al., 2024). This has been partially addressed in previous publications in this region (Delaigue et al., 2020; Dinauer and Mucci, 2017, 2018; Fradette, 2025; Mucci et al., 2011; Mucci and Jutras, 2026) and should be further considered in future use of this dataset.

Residuals between measured and calculated parameters were evaluated against arbitrary envelopes (±25 µmolkg-1 for TA and DIC, ±0.15 for pH_TS, and ±60 µatm for fCO2). Residuals exceeding these limits were qualitatively investigated but not used to assign flags, given the difficulty of attributing them unambiguously to a single parameter in coastal and estuarine waters. This approach allows visualization of how carbonate-system variables behave across regions while avoiding over-interpretation of residual structure in a setting where natural variability and methodological diversity are prevalent. Each parameter evaluation is described in the subsections below.

Nutrient data were evaluated through individual profile inspection and regional property-property analyses following the primary-QC framework described above. Each dissolved nutrient (nitrate, nitrite, ammonium, soluble reactive phosphate (SRP), and silicate (DSi)) was assessed in the context of its vertical distribution, regional norms, and expected stoichiometric relationships. Clear analytical outliers were assigned a flag =3 (questionable) and retained for transparency.

For nitrate, concentration profiles displayed strong and coherent vertical structure across most regions, generally increasing with depth in stratified water columns (see Fig. 14 in Sect. 5). Property–property plots with SRP revealed Redfield-like slopes in the GSL and LSLE (Fig. 8), whereas samples from the USLE displayed local departures with a weak or nonlinear relationship at low SRP concentrations (<1 µmolkg-1). Deep-water nitrate plateaus were observed around 25 µmolkg-1 in the LSLE, and near 10 µmolkg-1 in the SF. A consistent positive relationship between nitrate and AOU was evident across most subregions (apart from the USLE; Fig. 8), confirming coupling between remineralization and oxygen consumption.

Automated regional regressions were applied for nitrate against SRP, DSi, and AOU, using ±3σ acceptance envelopes (see Sect. 4.2). Points outside these ranges were visually reviewed, and only clear analytical outliers were flagged. Deviations from the classical Redfield ratio (Redfield et al., 1963; N:P ≈16:1) were not used as a flagging criterion, as such variability is expected in estuarine and shelf systems influenced by differential regeneration, denitrification, and sedimentary fluxes.

Nitrite concentrations were generally low, with distinct subsurface maxima occurring at AOU values of ∼50 and 200 µmolkg-1 (Fig. 9). These features were most pronounced in the stratified GSL and LSLE, consistent with active nitrification or partial denitrification zones (Crowe et al., 2012). Depth profiles show narrow peaks (see Fig. 14 in Sect. 5), consistent with localized redox transition zones (Wang et al., 2003).

Ammonium profiles exhibited subsurface peaks that co-occurred with those of nitrite but with greater amplitude (Fig. 9). Concentrations were elevated below the photic zone, near the sediment–water interface, and in high-AOU (low-oxygen) waters, reflecting remineralization/degradation of organic matter under oxygen-limited conditions. These maxima were particularly pronounced in the GSL.

SRP concentrations displayed a strong positive correlation with AOU across all regions, increasing with depth as remineralization progressed. This trend was robust throughout the GSL, SLE, and SF, though slightly weaker in the USLE and SF. The observed profiles reflect the combined effects of remineralization and benthic release of SRP from sediments (Pascal et al., 2025), processes that can occur well before fully reducing conditions are reached (Katsev et al., 2007; Lefort, 2012; Sundby et al., 1986, 1992).

DSi showed a similar relationship, increasing with AOU and depth. Concentrations were highest in the LSLE and GSL deep layers (up to 60–70 µmolkg1), and remained elevated near the seafloor in the SF, consistent with benthic regeneration in stratified, organic-rich environments.

Profiles of N2O, DOC and TN were examined for outliers and regionally-consistent trends following the primary-QC framework. Outlier detection combined visual inspection of vertical profiles (see Figs. 14 and 15 in Sect. 5) for all three parameters with additional property–property comparisons against AOU for N2O. Implausible values were assigned flag =3 (questionable) and retained for transparency.

N2O measurements were available only in the GSL and LSLE (Fig. 10), as no samples were collected in the USLE and SF. Across the investigated regions, N2O concentrations increased systematically with AOU, peaking just below the surface mixed layer in waters with AOU >100 µmolkg1. Regional regressions of N2O versus AOU showed strong positive slopes within ±3σ envelopes, consistent with nitrification–denitrification coupling under suboxic conditions (Pascal et al., 2025). The relationship was most pronounced in the LSLE, where clusters of elevated-AOU and N2O concentrations corresponded to oxygen-deficient waters of the LC.

DOC displayed a classical profile structure, with elevated surface concentrations that decayed exponentially with depth. DOC typically stabilized near 50 µmolkg-1 below ∼150 m, consistent with the transition from reactive to refractory carbon pools (LaBrie et al., 2020). Surface values were slightly higher in the USLE and SF, reflecting greater relative terrestrial and freshwater inputs and/or its release from suboxic sediments (Hélie and Hillaire-Marcel, 2006; Lévesque et al., 2023; Qudsi et al., 2024). No systematic inter-regional offsets were identified.

TN profiles exhibited an inverse pattern to DOC, with concentrations lowest at the surface and increasing with depth to a stable mean of ∼25 µmolkg-1 below ∼150 m. The deep-water enrichment of TN reflects the accumulation of inorganic nitrogen species regenerated during organic matter remineralization. When considered jointly, the DOC and TN distributions highlight the vertical decoupling of organic and inorganic nitrogen pools within the St. Lawrence system.

All stable isotope profiles were visually screened for outliers and evaluated for internal consistency using regional regressions and expected isotopic–property relationships (δ13C_DIC-AOU, δ13C_DOC-DOC, and δ18OH2O–δDH2O; see Fig. 11). Linear fits and ±3σ envelopes were used to identify implausible values, following the general QC framework described in Sect. 4.2.

δ13C_DIC values in the GSL and LSLE exhibited vertically coherent profiles consistent with marine carbonate-system dynamics. Surface waters showed typical marine signatures of +1 ‰ to +2 ‰ (relative to Vienna Pee Dee Belemnite (VPDB)), decreasing systematically with increasing AOU and depth to approximately -1 ‰ at 250–300 m (see Fig. 16 in Sect. 5). These gradients reflect progressive release of isotopically light inorganic carbon during organic matter remineralization and mixing with Atlantic-derived DIC at depth. The δ13C_DIC–AOU slopes agree with model-derived remineralization rates for the LC reported by Nesbitt et al. (2025).

In the USLE, δ13C_DIC values were more variable and generally depleted (-4‰ to 0‰; see Fig. 16 in Sect. 5), consistent with freshwater inputs and in-situ respiration of organic carbon. The SF data, although limited, showed similarly low and weakly stratified values (-2 ‰ to -1 ‰), reflecting a restricted deep-water renewal and a stronger terrigenous influence (Qudsi et al., 2024).

δ13C_DOC values (-22 ‰ to -27 ‰) were depleted relative to those of DIC, consistent with organic matter of mixed terrestrial and planktonic origin. The strongest depletions occurred in the USLE and SF, where terrestrial organic carbon inputs dominate (Barber et al., 2017; Tremblay and Gagné, 2009)

The water isotopes, δ18OH2O and δDH2O, displayed linear covariance (slope =7.23, R2=0.998 in the GSL; slope =7.23 and R2=0.999 in the LSLE), consistent with the global meteoric water line (Craig, 1961). Values increased with depth and salinity, reflecting conservative mixing between isotopically light riverine water (δ18O ≈-15 ‰ to -20 ‰ VSMOW; δD ≈-100 ‰ to -140 ‰) and heavier Atlantic inflows (δ18O ≈0 ‰; δD ≈0 ‰). Profiles in the GSL and LSLE followed this two-endmember structure closely, confirming internal consistency between isotope and hydrographic data (Dinauer and Mucci, 2018).

Overall, isotopic parameters exhibited high internal consistency and physically coherent mixing relationships. Variations among regions primarily reflect salinity-driven endmember mixing and redox-linked fractionation during organic matter remineralization, rather than analytical offsets. Future QC efforts should continue to test isotopic stability in estuarine gradients, where freshwater–marine transitions and the complexity of the organic carbon cycle (multiple sources, photosynthesis and respiration) may challenge internal consistency approaches.

Measurements of three gaseous tracers (CFC-12, SF₆, and CF₃SF₅) were included in the later years of the dataset, particularly during the 2022–2023 Gulf of St. Lawrence Tracer Release Experiment (TReX). Of these, CF₃SF₅ was a deliberate tracer, released and tracked to directly investigate the dispersion and ventilation pathways of the deep inflow in the LC (Stevens et al., 2024). The other two compounds, CFC-12 and SF₆, are well-established transient tracers that provide context for water mass age and transit times, and are used for cross-validation of ventilation estimates based on the dispersion of the deliberate tracer (Gerke et al., 2026; Stevens et al., 2024). All three tracers were measured simultaneously.

QC focused on ensuring internal consistency between cruises. Profile distributions were visually inspected to confirm agreement with neighbouring casts. Data from the two 2022 cruises (TReX2 (Cruise #19) and DFO AZMP Fall 2022 (Cruise #20)) were compared in T-S space and against expected atmospheric concentrations, displaying elevated variability in the Cruise #20 data. To correct for this systematic offset, measurements from Cruise #20 were uniformly scaled by -20 % for CFC-12, -14 % for SF₆, and -17 % for CF₃SF₅, bringing them into agreement with Cruise #19 observations and with conceivable atmospheric equilibrium saturation in surface waters. Thus, these parameters were rigorously quality-controlled at the analytical stage by the laboratory personnel performing the gas extractions and analyses. Tracer profiles were visually inspected during compilation to confirm consistency with adjacent depth structure and to ensure that regional patterns matched known distributions. No additional profile-level outliers were flagged.

To evaluate the accuracy of oxygen measurements, CTD-probe derived dissolved oxygen data (CTDOXY) were compared with results of discrete Winkler titrations (Oxygen) from matched bottle samples. Cruise-specific linear regressions were used to correct CTD oxygen to the Winkler calibration, and residuals were examined for departures beyond ±10 µmolkg-1 (the threshold adopted by Grégoire et al. (2021) and consistent with typical SBE43 sensor uncertainty). Values exceeding this difference were flagged as questionable. The composite parameter best_Oxygen, that merges corrected CTD oxygen and Winkler data by flag priority, provides the most reliable oxygen estimate for subsequent calculations such as the apparent oxygen utilization (AOU). The strong CTD oxygen – Winkler correlation and narrow residual spread (Fig. 3) confirm the validity of this ±10 µmolkg-1 criterion.

The behaviour of carbonate-system variables, as assessed in Sect. 4.4, reflects the combined influence of measurement uncertainty, equilibrium constant selection, and natural variability associated with low-salinity coastal environments. Comparisons among measured and calculated parameters reveal systematic patterns and offsets that vary regionally, rather than a single, consistent level of agreement across the dataset. Across all regions, offsets between measured and calculated parameters were large relative to open-ocean standards.

The most consistent dataset was found in the GSL and LSLE, where residuals were smaller but still exhibited systematic offsets. In the USLE and SF, discrepancies were substantially larger and often exceeded these limits (Figs. 4–7), reflecting the combined influence of low salinity, weak buffering, and the likely presence of unaccounted organic alkalinity. Measured TA commonly exceeded calculated values, consistent with contributions from weak organic bases, whereas occasional negative residuals, typically found in the Saguenay Fjord (Delaigue et al., 2020), imply the influence of organic acids (Qudsi et al., 2024).

These findings confirm that internal consistency deteriorates below salinities of ∼20 due to both uncertainties in the thermodynamic constants and variable contributions from organic alkalinity (Patsavas et al., 2015). Regardless, quantitative interpretations remain possible when appropriate parameter pairs are selected and when constants more suited for low-salinity waters (e.g., Cai and Wang, 1998) are used (Delaigue et al., 2020; Dinauer and Mucci, 2017, 2018; Mucci et al., 2011; Mucci and Jutras, 2026). Therefore, carbonate-system parameters at low salinity should be interpreted critically and with caution, with preference given to over-determined pairs where available.

Dissolved nutrient profiles (nitrate, nitrite, ammonium, SRP and DSi) were inspected against AOU and stoichiometric relationships to verify internal coherence (Figs. 8 and 9). Most regions followed Redfield-like nitrate–phosphate scaling, with minor deviations in the USLE where low-phosphate samples (<1 µmolkg-1) produced nonlinear slopes. Subsurface nitrite and ammonium maxima coincided with mid and high-AOU layers, confirming realistic redox-transition features.

Transient (CFC-12, SF₆) and the deliberate (CF₃SF₅) tracer measurements were included for 2021–2023 cruises during the TReX campaign. These data were quality-controlled analytically at the laboratory stage; visual inspection confirmed smooth depth structure and realistic gradients consistent with known ventilation pathways (Gerke et al., 2026; Stevens et al., 2024). No additional flags were required.

Several factors constrain the completeness and interpretability of this dataset. Seasonal coverage is uneven, with minimal winter sampling due to ice conditions and logistical constraints; consequently, late-winter processes such as cold-intermediate-layer formation and deep-water renewal remain under-resolved. Spatially, sampling is densest along the Laurentian Channel and Lower Estuary, whereas the Anticosti and Esquiman Channels, Upper Estuary, and Saguenay Fjord are less represented in certain years. Parameter coverage also varies among cruises: some tracers (e.g., N2O, fCO2, stable isotopes) were collected only during targeted field efforts.

In addition, this dataset integrates measurements from multiple laboratories, instruments, and analytical procedures. As such, measurement uncertainties reflect those reported by the original data providers and are inherently heterogeneous across the entirety of the dataset. No additional harmonization or uncertainty propagation was applied beyond the primary QC performed by the source research groups or during the synthesis of this product.

Due to the lack of deep-water reference layers (>1500 m), a pre-requisite for secondary crossover analysis, no inter-cruise adjustments could be applied. For carbonate-system parameters, uncertainties increase substantially at low salinity due to weak buffering, uncharacterized organic alkalinity, and the limited applicability of seawater equilibrium constants in estuaries. These factors can produce large residuals for DIC, TA, pH_TS, fCO2 in the USLE and SF, and users should interpret derived carbonate variables in these regions with caution.

Despite these limitations, GOSLED constitutes one of the most spatially and temporally comprehensive academic archives of hydrographic and biogeochemical profiles for the St. Lawrence system to date. The dataset provides a robust foundation for regional carbon–oxygen budget assessments, model validation, and future synthesis within coastal and estuarine carbon-cycle research. Users are therefore encouraged to consider these sources of variability when performing quantitative analyses, particularly in regions of strong freshwater influence or limited sampling coverage.

The variable best_Oxygen represents the most reliable estimate of dissolved oxygen per sample. The dataset was constructed using a decision hierarchy that prioritizes Winkler titrations (when available), followed by sensor-corrected probe values. Users are strongly encouraged to use best_Oxygen as the default for biogeochemical and stoichiometric calculations, rather than relying solely on CTDOXY or Oxygen columns.

When direct measurements are missing, this dataset includes several calculated variables that can be used as substitutes or for internal consistency checks:

AOU (Apparent Oxygen Utilization): calculated from best_Oxygen and oxygen solubility at in-situ temperature and salinity using standard equations (Garcia and Gordon, 1992). AOU is useful for estimating remineralization or respiration in subsurface layers.

Use measured carbonate-system parameter values (e.g., DIC, TALK, pH_TS_measured, fCO2_insitu_measured) when validating instruments, comparing with field campaigns, or for direct observational analysis.

This data product does not include secondary inter-cruise adjustments and therefore should be used with caution when assessing small-scale temporal trends across years. Nevertheless, it is well-suited for regional comparisons, ecosystem model validation, tracer budget studies, and investigations into physical-biogeochemical coupling in the estuarine and coastal domain.