| 25 Nov 2025
CO2 emissions from dredged sediment as a function of moisture, temperature, and oxygen
Abstract. Estuaries represent a crucial compartment in the global carbon cycle, with high rates of organic matter formation, burial and degradation. Sedimentary processes control the balance between long-term burial of carbon and CO2 and CH4 emissions upon OM degradation, for which estuaries are a global hotspot. The profound and globally intensifying perturbation of estuarine sediment by anthropogenic activities such as harbor dredging has a far-reaching but poorly understood impact on sedimentary carbon cycling processes in estuaries and by extension potentially on global carbon budgets. Hence, understanding carbon emissions from dredged sediments under varying environmental conditions is critical for assessing their environmental impact and informing large-scale sediment reuse strategies. This study investigates the effects of moisture, temperature, and oxygen availability on CO2 emission rates from dredged sediments collected from the Port of Rotterdam, the largest port in Europe. Results are compared with soil CO2 emissions from a global database of nearly 400 laboratory incubations under different conditions. Our sediment incubation showed that CO₂ emissions increased 1.5–8.1 times with higher moisture levels, 3.8–6.0 times with elevated temperatures, and 4.5–6.4 times with oxygen exposure. Applying machine-learning tools (XGBoost) to a global database of soils and sediment incubations suggested that moisture and temperature responses observed in our experiment are widespread in both soils and sediments. However, functions that described these responses differed significantly from those used in global biogeochemical carbon-cycle models, indicating a need to revisit these functions. Oxygen displayed a relatively stronger effect in sediments, likely due to better preservation of labile organic matter (OM) in anoxic conditions and its rapid oxidation upon re-exposure to oxygen. A model incorporating organic matter with different degradation rates showed that while labile OM fueled high initial rates of CO2 emission, more recalcitrant OM was a much more abundant OM pool (> 80 %) that dominated cumulative CO2 emissions on longer timescales. Overall, our experiment and meta-analysis on a global soil dataset suggest the importance of environmental controls on carbon emissions and that dredged sediments are an organic-rich, potent source of CO2 upon oxidation after dredging, which should be considered in sediment management and reuse practices.
This manuscript explores factors that affect the carbon dioxide emissions of dredge spoils. There are compelling reasons, including the sheer volume of dredge spoils excavated globally, to pursue this question in terms of global carbon cycling and management strategies. The authors performed an incubation of sediment collected from dredge spoils at the Port of Rotterdam, a location where dredging is a frequent undertaking. They proceeded to compare their results with a meta-analysis of almost 400 incubations, which were also used for machine learning to determine the importance of and interaction between oxygen, moisture, and temperature. Briefly, the authors report that temperature does have an important role, but may taper out between 20 and 35 degrees C where an enzymatic optimum is reached. Oxygen played a significant role in increasing carbon emissions. Moisture was also positively correlated with decomposition where wetter sediment yielded higher respiration rates. Together, this information represents a novel perspective in considering dredge spoils as a source of carbon dioxide to the atmosphere. Moreover, their approach provides important factors for other researchers to consider when preparing similar incubations.
Overall, this manuscript is written clearly. The methods are valid and reproducible. The results are not over-interpreted yet demonstrate their importance. The figures are sufficient, but I recommend including additional information in the Supplemental to list the studies used for meta-analysis.
My most substantial comment is to elaborate on the two-pool modeling approach: it is unclear how the proportion of the slow pool and fast pool were attributed. As discussed later in the manuscript, dredge spoils oftentimes consist of older, refractory material and contain only slow-pool OM. How is the portion of each pool determined? If dredge spoils usually constitute the slow-pool, is there any downside to continue using the two-pool model?
L58: The sentence beginning “This important…” is missing a word/verb. Perhaps “This is important globally because perturbations of…”
L93: “refrigerator” instead of “fridge”.
L116: I encourage the authors to tabulate the sources of data for meta-analysis. Fig S2 is helpful, but it would be helpful to see all the publications used.
L154: bring Sierra et al. Outside of parentheses and only cite the year: Following Sierra et al. (2017), the effects…
L160: I agree with the two-pool modeling approach. Could you elaborate more about the initial fractions of the fast pool and slow pool. What if dredge spoils are old sediments and contain mostly the slow pool? This is discussed further on L380-381.
L273: meta-analysis instead of meta-study?
L284: can you elaborate more on the mechanism driving this? Is it that microbes are not adapted to temperatures higher than this since they rarely experience it? Do enzymes stop working at these temperatures? It is surprising since I have read of (and conducted myself) experiments that use 20 & 30 or 15 & 25 degrees C for incubation temperatures and measure Q10 of ~2.
L309-317: Interesting about sand content. Could the machine learning be sensitive to this since higher sand content might possess lower carbon content which could possibly be more refractory organic matter, even when standardized to C? In other words, higher sand is just a proxy for low concentrations of refractory OM that yield low decomposition rates?
L438-439: Wait, I thought anaerobic conditions *decreased* GHG emissions?