The WEGAS system is described in detail in . Briefly, seawater from just below the surface (at approximately 30 cm depth) was continuously passed through a water handling system consisting of a thermosalinograph (SBE45 MicroTSG, Seabird Scientific, US) measuring seawater temperature, salinity, and conductivity; a flowmeter maintaining stable flow at ∼3 L min⁻¹; and a showerhead equilibrator (RAD-AQUA, Durridge, US). After the seawater was equilibrated with a flow of ambient air, the air stream was passed through a custom-built cryocooler that cooled the gas to a dew point of 4 °C to reduce excess humidity before analysis by the CRDS. A gas handling system controlled airflow switching between ambient air measurements and equilibrator measurements. Each sampling cycle consisted of 5 min of ambient air followed by 40 min of equilibrator air, with cycles repeated until horizontal profiling of each bay was completed. Transition periods between ambient and equilibrator air were excluded from analysis. Sampling durations lasted between 60 and 90 min (typically ∼ 75 min). Measurements were conducted both inside and outside bay areas. To distinguish between “inner bay” and “outer bay” sampling points, we delineated the bay boundary at the narrowest part of the inlet connecting each bay to the open Baltic Sea. This location represents the transition in water exchange, residence time, and mixing characteristics. For cross-bay comparisons, concentrations were first averaged within each bay, and summary statistics (e.g., median) were then calculated across bays using one value per bay, treating each bay as an independent unit rather than applying area-weighted averaging.

Mole fractions (ppm) of CO₂ were converted to partial pressures (µatm) using the Seacarb (v.3.3) x2pCO2 function . Mole fractions (ppm) of CH₄ and N₂O were converted to molar concentrations using Henry's law (Eq. ), assuming full equilibration in the equilibrator at ambient pressure: 1C=p×KH where C is concentration (mol L⁻¹), p is the partial pressure (1 ppmv corresponds to 1 µatm at an ambient pressure of 1 atm), and KH is the temperature-corrected Henry's law constant: 2KH=KH*×exp-ΔsolHR×1TK-1298.15K. where KH* is the Henry's law constant at reference temperature (298.15 K), ΔsolH is the enthalpy of dissolution, R is the gas constant and TK is water temperature in Kelvin. Constants were obtained from .

Gas solubilities were calculated using the Bunsen solubility coefficient: 3β=expA1+A2100T+A3ln⁡T100+SB1+B2T100+B3T1002 where β is the dimensionless Bunsen coefficient, A1–A3 and B1–B3 are gas-specific constants from , T is temperature (K), and S is salinity (g kg⁻¹). For N₂O, the solubility constant is given by K0=β, whereas for CH₄ – assuming ideal gas behaviour – the solubility constant is calculated as K0=β (R×273.15 K).

Air-sea fluxes of GHGs were estimated using: 4F=k×K0×(pXseawater-pXair). where F is flux, k is gas transfer velocity (m s⁻¹), K0 is solubility, and pX represents partial pressures in seawater and air. The gas transfer velocity was calculated following which is representative for lake environments: 5k=(2.07+0.215×U101.7)×Sc600-0.5. where U10 is wind speed and Sc is the Schmidt number. Schmidt numbers for brackish Baltic Sea conditions were interpolated between freshwater and seawater values : 6Sc=(Scseawater-Scfreshwater)×S35+Scfreshwater. Wind speed at 10 m height was obtained from the ICON-EU numerical weather prediction model (Deutscher Wetterdienst, Germany). Model output at ∼ 7 km horizontal resolution was accessed through the Ventusky online visualization platform (https://www.ventusky.com, last access: 25 February 2026). We extracted 10 m wind values corresponding to the sampling dates and coordinates of each site. The derived wind speeds were 1.67 m s⁻¹ (Sandviken), 7.0 m s⁻¹ (Assöviken), 6.67 m s⁻¹ (Högklykeviken), and 4.4 m s⁻¹ (Bodviken) in April; 3.3 m s⁻¹ (Sandviken), 3.9 m s⁻¹ (Assöviken), 7.2 m s⁻¹ (Högklykeviken), and 3.6 m s⁻¹ (Bodviken) in September; and 2.5 m s⁻¹ in both Östra Lermaren and Östra Myttingeviken in October.

To derive CO₂-equivalent fluxes, calculated fluxes (µmol m⁻² d⁻¹) were converted to mass units (mg m⁻² d⁻¹) using respective molar masses, then multiplied by 100-year sustained global warming potentials of 45 for CH₄ and 270 for N₂O .

Surface water samples (0–1 m depth) were collected from the centre of each bay and kept cool until analysis at the certified Erken laboratory, Uppsala University (ISO/IEC 17025). Dissolved concentrations of nitrite-N and nitrate-N NO₂-N + NO₃-N,, ammonium-N NH₄-N, and phosphate-P PO₄-P, were determined colorimetrically using an AutoAnalyzer 3 (SEAL Analytical, US) or a U-2910 analyser (Hitachi, Japan). Total nitrogen TN, and phosphorus TP, concentrations were determined as NO₃-N and PO₄-P after persulfate digestion.

Chlorophyll a (Chl a) was determined spectrophotometrically after acetone extraction . Total organic carbon TOC, was analyzed using a 680 °C combustion catalytic oxidation method with a TOC-L analyser (Shimadzu, Japan). Organic content was estimated as loss on ignition (LOI) after combustion at 550 °C . Turbidity was measured as Formazin Nephelometric Units FNU, using a 2100 P ISO turbidity meter (Hach, CO, USA).

Temperature, salinity and dissolved oxygen were measured using a WTW Multi 3420 probe (Xylem, US), and pH was measured with a YSI Pro10 pH meter (Xylem, US). All measurements were taken in the centre of each bay, adjacent to the water sampling location.

Aquatic vegetation cover was recorded in all basins by a free-diver in August, a few weeks prior to the September GHG measurements. For two bays (Högklykeviken and Bodviken), vegetation cover was also estimated in May, a few weeks after the April GHG measurements. For the other two bays with GHG measurements in May (Assöviken and Sandviken), we retrieved May vegetation data from a previously conduced survey (in 2022). Survey sites consisting of 7–13 circular areas (5 m radius, ∼80 m²) were distributed evenly from the bay opening to the innermost areas. Survey sites were randomly allocated within subareas along a distance-from-opening gradient, excluding nearshore areas with <0.5 m water depth. Within each survey site, percentage cover of individual taxa and total cover of all macroscopic autotrophs (including filamentous algae and cyanobactera) was visually estimated. The vegetation assessment method follows a standardized national protocol that has been widely applied in this region e.g.,. For this study, we used two vegetation indicators: (1) total vegetation cover and (2) cumulative cover of all rooted vegetation (sum of all rooted taxa cover) because they capture distinct functional aspects of benthic vegetation that are relevant for greenhouse-gas dynamics. Total vegetation cover provides an integrated measure of overall primary producer abundance, which can influence water-column oxygen dynamics and carbon cycling through photosynthesis and respiration. Rooted vegetation cover specifically reflects the presence of macrophytes capable of affecting sediment–water exchange processes through below-ground gas transport in addition to photosynthesis and respiration. These indicators therefore represent the most ecologically meaningful metrics for assessing vegetation-related controls on GHG concentrations in these shallow bays.

Sediment cores were collected using a gravity corer (63 mm inner diameter) and sectioned on-site immediately upon return to land. For this study, we used only data from the uppermost sediment layer (0–1 cm), which represents the sediment-water interface where redox-sensitive processes and exchanges directly influence surface-water GHG concentrations. We note that deeper sediment layers may be important for methane production and ebullition dynamics, but were beyond the scope of the present study. Sediment samples were homogenized in sterile containers and transferred to pre-weighed polypropylene vials for analysis. Samples were freeze-dried and pulverized to fine powder. Porosity was calculated from weight loss after freeze-drying, assuming a sediment dry density of 2.65 g cm⁻³ . Sediment water content was determined after freeze-drying, and organic content was determined by loss on ignition (LOI) at 550 °C for 2 h . Organic carbon (Corg) and nitrogen (Norg) content were determined using an Elemental Combustion System (ECS 4010, Costech Analytical Technologies Inc, US).

CO₂ was generally close to saturation in surface waters (see Table 2), with concentrations differing significantly between bays (see Fig. 2 and Tables  and A1). The highest concentrations were observed in Bodviken in April (mean 1022 ± 121.6 ppm) and in Östra Lermaren and Östra Myttingeviken in October (mean 1108 ± 117.7 ppm and 1033 ± 83.1 ppm, respectively). These bays showed significantly higher CO₂ concentrations inside compared to outside areas (see Table A2). In contrast, bays where CO₂ was near saturation showed no significant inside–outside differences. Overall, no consistent patterns emerged across the bay openness gradient or between inside versus outside areas across all bays and seasons.

Previous studies from the Tvärminne archipelago in southwestern Finland reported values that were of a similar magnitude or exceeded the concentrations measured in our study: 750 ppm , 4.5–13,100 ppm , and 160–2521 ppm . Long-term measurements across the open Baltic Sea, that were conducted on the Finnmaid ferry between Travemünde and Helsinki , reported values ranging between 18–1238 µatm (mean 293 ± 60 µatm) in April and 14–1198 µatm (mean 375 ± 50 µatm) in September. Similar measurements by yielded values of <200 µatm in summer and ∼ 400 µatm in September. Notably, maximum values in our study compared well with the maximum values reported in as well as the mean concentrations of 1288 ppm reported from Swedish lakes . The sheltered nature of the bays may resemble lake-like conditions with respect to air–water CO₂ exchange, but not necessarily other gases.

These findings suggest that although shallow bays often accumulate organic matter and are significant reservoirs of carbon and nutrients accumulated from surrounding areas , their role in atmospheric CO₂ exchange is not uniform. Instead, they may function either as CO₂ sources or sinks depending on seasonal conditions and bay-specific properties such as openness, vegetation cover, and eutrophication status. However, our measurements represent only snapshots from two seasons and capture transitional states rather than peak or minimum seasonal conditions. In temperate coastal environments, growth of phytoplankton and algae in spring reduces pCO₂ in the water column, while biomass decay in autumn results in elevated pCO₂. Recent studies by and reported diurnal variability in surface-water pCO₂ and CH₄ in the Baltic Sea that could be linked to biological and physical drivers such as solar radiation, temperature or biological activity. We acknowledge that our measurements, which were always conducted around noon, do not capture these diurnal fluctuations and thus likely introduce a small but systematic bias relative to true daily mean conditions. While such measurements remain valuable, more extensive, long-term monitoring is required to identify the environmental parameters that drive these systems to function as CO₂ sources or sinks across different temporal scales.

CH₄ was strongly supersaturated in all study bays (see Table 2) and significantly higher inside bays compared to open water (see Fig. 2 and shown using a Wilcoxon rank-sum test, see Table A2 in the Appendix). Concentrations were generally higher in autumn compared to spring (see Fig. c, d and Table ), likely reflecting enhanced organic matter degradation and increased activity of methanogenic archaea in anoxic sediments . The highest concentrations were recorded in Högklykeviken, reaching 181 nmol L⁻¹ in April and 580 nmol L⁻¹ in September. Östra Myttingeviken also showed elevated levels up to 494 nmol L⁻¹. Both are enclosed bays, with Högklykeviken representing a more disturbed system that has shifted from benthic vegetation dominance to plankton dominance.

CH₄ production occurs primarily through methanogenic archaea in oxygen-depleted sediments . In enclosed bays with narrow openings, limited water exchange minimizes sediment disturbance by waves and currents, allowing organic matter to accumulate and creating conditions conducive to elevated CH₄ production . Recent studies have shown that such shallow, sheltered bays are significant organic carbon reservoirs, with higher accumulation correlated with vegetation cover, coastal morphology, and landscape characteristics .

Another factor that can contribute substantially to CH₄ emissions in shallow, organic rich sediments is ebullition . Recently, showed that ebullition from sandy sediments can be substantial. The WEGAS system measures CH₄ from both benthic diffusion and bubble dissolution. Consequently, the observed CH₄ concentrations represent the combined effect of these pathways, and without isotopic information we cannot distinguish between diffusive transport and ebullition. Although no visible bubbling was observed during sampling, we cannot exclude the possibility that episodic ebullition events might have impacted our measurements. This measurement limitation should be considered when interpreting the relationships between CH₄ and the environmental parameters described in Sect. 3.1.4.

Our measured concentrations are comparable to previous studies in the Baltic Sea region. Studies in the southern Stockholm Archipelago around Askö reported 6–460 nmol L⁻¹ and 26–6596 nmol L⁻¹ , while studies from the southwestern coast of Finland reported ranges of 19–469 nmol L⁻¹ , 44 nmol L⁻¹ , 130–665 nmol L⁻¹ , and 0–6767 nmol L⁻¹ . The CH₄ concentrations measured in our study are significantly higher than values reported from long-term measurements in the open Baltic Sea, ranging between 3.5–6 nmol L⁻¹ , 2.8–18.6 nmol L⁻¹ and 3.2–22 nmol L⁻¹ . The consistent observation of high spatial variability and local CH₄ hotspots across these studies underscores the need for high-resolution sampling to accurately characterize GHG dynamics in shallow coastal ecosystems.

N₂O concentrations showed pronounced seasonal variation, with higher values in spring (13.7–20.8 nmol L⁻¹) than in autumn (8–11.75 nmol L⁻¹). Most bays were slightly subsaturated or close to saturation (see Table 2). In April, N₂O concentrations were higher in open bays compared to more enclosed bays (see Fig. e), while no such trend was apparent in the September data. In addition, N₂O concentrations were generally higher outside bays than inside, contrasting with the patterns observed for CH₄.

The consistently higher N₂O concentrations outside the bays may be explained by hydrodynamic and sedimentological conditions that favour coupled nitrification-denitrification . Higher water currents enhance oxygen penetration into coarser sediments (sand, gravel, stones) which promotes nitrification in the oxic surface layer and denitrification in underlying anoxic microzones . Lower concentrations inside bays are likely the result of reduced water currents and the accumulation of fine organic matter. These conditions promote weaker ventilation, stronger sediment–water coupling, and lower oxygen availability, which tend to suppress nitrification and favour complete denitrification to N₂ rather than N₂O, ultimately reducing dissolved N₂O concentrations. Additionally, higher N₂O concentrations outside the bays may partly reflect wind-induced mixing in the more exposed areas, where longer fetch and higher wind speeds enhance vertical exchange and stimulate nitrification–denitrification dynamics. In contrast, the sheltered bay interiors experience reduced wind forcing, limiting mixing and potentially suppressing N₂O production and release.

Few studies have simultaneously measured CO₂, CH₄ and N₂O in shallow Baltic Sea bays. Our results are similar to those of , who reported concentrations of 9–25 nmol L⁻¹ in August/September 2023. Seasonal patterns in dissolved N₂O observed in our shallow Baltic Sea bays, with relatively high concentrations in spring (April) and lower concentrations in autumn (September/October), are consistent with patterns reported from other Baltic coastal settings. Long term observations in the Kiel Bay (Boknis Eck time series station in Eckernförde Bay) likewise show elevated N₂O in winter and early spring followed by reduced concentrations in autumn, particularly under hypoxic or anoxic conditions . At that site, seasonal declines in dissolved oxygen and nutrient dynamics were closely coupled to N₂O variability, with lower autumn N₂O attributed to increased denitrification to N₂ under suboxic conditions that consume N₂O . Likewise, identified pronounced seasonal N₂O variation in coastal Baltic waters and linked it to shifts in redox conditions and stratification that modulate microbial nitrification and denitrification pathways–processes that are both oxygen sensitive and seasonally dynamic. In shallow bays, spring mixing and higher oxygen availability may enhance nitrification and partial denitrification, leading to relatively elevated N₂O, whereas prolonged summer stratification and oxygen depletion in late summer and early autumn favour complete denitrification and N₂O consumption, resulting in lower observed N₂O concentrations. These seasonally varying oxygen and nitrogen transformation dynamics offer a plausible mechanistic framework for the spring–autumn N₂O trend observed in our study.

To identify environmental factors associated with variability in surface-water GHG concentrations, we conducted a Spearman’s rank correlation analysis using bay-averaged GHG concentrations and environmental parameters measured in the center of each bay (see Fig.  as well as Figs. A7 and A8 in the Appendix). To increase statistical power and assess general trends, data from April and September/October were pooled.

CO₂ concentrations were positively correlated with LOI (r=0.67, p=0.04) and showed negative correlation trends with chlorophyll-a (r=-0.64, p=0.05) and pH (r=-0.59, p=0.07), as well as a positive correlation trend with rooted vegetation cover (r=0.60, p=0.06). The negative relationship with chlorophyll-a and pH suggests that periods or locations of enhanced primary production are associated with CO₂ drawdown and elevated pH, whereas positive correlations with LOI and vegetation indicate that respiration and mineralization of organic matter – particularly from macrophyte-derived inputs – can offset photosynthetic uptake and elevate CO₂ concentrations in surface waters. This interpretation is supported by the observation that the bays with the highest CO₂ concentrations (Östra Lermaren, Östra Myttingeviken, and Bodviken) shared extensive rooted vegetation cover and elevated sediment organic carbon content. In Östra Lermaren and Östra Myttingeviken, which also exhibited the lowest eutrophication status as measured by TP and chlorophyll-a concentrations, high CO₂ concentrations may appear counter-intuitive but are likely driven by substantial autochthonous organic matter inputs from decaying vegetation, consistent with coastal studies documenting seasonal CO₂ hotspots linked to remineralization of organic-rich material . In contrast, Bodviken combined high CO₂ concentrations with comparatively higher eutrophication, suggesting that enhanced internal mineralization under nutrient-rich conditions may dominate CO₂ production in this system. Although the correlations with pH and rooted vegetation were slightly above the conventional 5 % significance threshold, they are consistent with the expected coupling between primary production, organic matter mineralization, and CO₂ dynamics in shallow coastal systems. Given the limited number of bays, these trends should be interpreted as exploratory and warrant confirmation through studies with higher spatial and temporal resolution.

CH₄ concentrations showed a significant negative correlation with dissolved oxygen (r=-0.75, p=0.03) and a positive correlation with LOI (r=0.67, p=0.04). These relationships are consistent with enhanced methanogenesis under low-oxygen conditions and increased availability of degradable organic substrates in the water column, which together promote CH₄ production and accumulation.

In contrast, N₂O concentrations exhibited significant negative correlations with temperature (r=-0.82, p=0.01), TN (r=-0.72, p=0.04), total vegetation cover (r=-0.71, p=0.04), and rooted vegetation (r=-0.78, p=0.02). The negative relationship of N₂O and temperature is likely driven by two key factors: (1) increased N₂O solubility at lower temperatures, and (2) the temperature sensitivity of denitrification enzymes. Under low-temperature conditions, enzymatic activity of N₂O reductase may be reduced, potentially slowing conversion of N₂O to N₂ and thereby increasing net N₂O emissions . In addition, vegetation-driven oxygenation of surface sediments can both increase and decrease N₂O production by shifting the balance between nitrification and denitrification. While oxygenation can stimulate nitrification near roots and dentrification in adjacent anoxic zones e.g., sustained and strong oxygenation can surpress denitrification and lead to more complete reduction to N₂ thereby lowering N₂O fluxes . Contrary to findings reported by , we could not observe a correlation between the concentrations of NO₂ + NO₃ and N₂O across the bays. This decoupling likely reflects the dominance of local-scale processes characteristic of shallow, sheltered bay environments. In particular, N₂O production may be spatially decoupled from water-column NO_x concentrations if it occurs primarily in sediments, where nitrate availability, redox gradients, and organic matter supply differ substantially from overlying waters. In organic-rich bay sediments, denitrification may proceed efficiently to N₂, thereby limiting N₂O accumulation despite elevated NO_x in the water column. In addition, rapid biological uptake of inorganic nitrogen by phytoplankton and benthic vegetation can reduce ambient NO2- + NO3- concentrations without proportionally affecting N₂O production. Physical processes such as advection, sediment–water exchange, and episodic ebullition may further bypass water-column NO_x controls on dissolved N₂O. Finally, differences in spatial scale, environmental setting, and sampling strategy between this study and the global synthesis of likely contribute to the contrasting relationships observed.

Negative correlations between N₂O and CH₄ were observed across different bays and seasons (see Fig. ). A similar negative correlation was reported by .

This negative correlation can be likely explained by the spatial distribution of the gases. N₂O concentrations were generally highest outside the bays and in the channels that connect to the open sea, where the water current velocities are higher and coarser substrates (sand, gravel, stones) dominate. This pattern is consistent with previous observations that N₂O hotspots often occur in hydrodynamically energetic settings, where strong currents, turbulent mixing, and coarse substrates (sand and gravel) enhance oxygen penetration into sediments and stimulate nitrification . Such conditions also promote rapid porewater–water column exchange, facilitating the release of N₂O produced during coupled nitrification–denitrification. Our elevated N₂O concentrations in channels and outer-bay areas therefore align well with the mechanistic understanding established by earlier studies. In contrast, CH₄ was highest inside the bays, where sedimentary organic matter accumulates in fine muddy sediments.

However, in Högklykeviken and Östra Myttingeviken, the bays with the highest autumn CH₄ concentrations, we observed an interesting shift from negative correlations at CH₄ concentrations <250 nmol L⁻¹ to positive correlations at CH₄ concentrations >250 nmol L⁻¹. This threshold-like behaviour suggests that different biogeochemical processes dominate at high versus low CH₄ concentrations, which is indicative of the complex carbon-nitrogen cycling dynamics of these systems.

The different spatial distributions of CH₄ and N₂O may partly reflect their different optimal oxygen conditions: CH₄ production occurs mainly in anoxic regions, while N₂O production is maximal at suboxic levels where denitrification dominates . Although our dissolved oxygen measurements in the central bay locations indicate generally oxic conditions in both Högklykeviken (O2,dissolved=8.3 mg L⁻¹ ≈ 91 % saturation) and Östra Myttingeviken (O2,dissolved=5.6 mg L⁻¹ ≈ 59 % saturation), we cannot resolve small-scale oxygen heterogeneity and therefore can only speculate that oxygen-reduced microenvironments may existed in areas of high CH₄ concentrations . Beyond oxygen availability, several additional mechanisms could explain the shift from a negative to a positive CH₄–N₂O correlation. As mentioned earlier, increased inputs of labile organic matter can stimulate methanogenesis further inside the bays, while changes in the availability of alternative electron acceptors (e.g., nitrate, sulfate, iron) alter competition among metabolic pathways, which can suppress or enhance methanogenesis and modulate N₂O production or consumption. Coupled processes such as nitrate-dependent anaerobic methane oxidation can also link CH₄ and N cycling in non-linear ways . Ebullition would provide a pathway for CH₄ accumulation by bypassing water-column oxidation and decoupling CH₄ from dissolved N₂O dynamics. However, as mentioned previously, our measurement set-up does not allow us to discern between bubble-mediated and diffusive CH₄. Changes in rooted vegetation and bioturbation may further modify sediment oxygen penetration and bubble release, influencing the relative dominance of CH₄ and N₂O-producing pathways. Finally, sediment disturbance from the research vessel in very shallow areas could explain these anomalous patterns . In order to resolve which of these factors operates in our bays would require targeted process data, limiting our discussion to speculations.

In Högklykeviken, an additional factor may have influenced these relationships. As part of a coastal restoration project, an aluminum-based geoengineering treatment was conducted on 13 May 2024 in the area where both CH₄ and N₂O exhibited high concentrations and positive correlations. This treatment involved injecting an aluminum solution into the sediment to increase the phosphorus retention and reduce eutrophication. Previous research has suggested that aluminium can decrease organic matter remineralization, possibly slowing CH₄ production . Whether this sediment disturbance altered microbial communities and affected GHG emissions requires further investigation that is beyond the scope of this study.