Assessing the biodegradability of dissolved organic carbon in freshwater systems
Bibliographic record
Abstract
Dissolved organic carbon (DOC) is an important contributor to both carbon (C) cycling and other biogeochemical processes in aquatic ecosystems as it is the most mobile fraction of organic matter. The biodegradable fraction of DOC can be microbially degraded over time, producing carbon dioxide (CO2), a greenhouse gas (GHG) that is subsequently released to the atmosphere. In addition, microbial degradation-resistant DOC can accumulate in water bodies, causing chemical and physical changes to aquatic systems, resulting in decreased primary productivity, formation of anoxic zones, and negative implications on the aquatic food cycle. Although biodegradable DOC (BDOC) is widely studied, there is no agreed-upon standard method for assessing DOC biodegradability. Given its important control on CO2 production and natural functioning of aquatic ecosystems, it is essential to develop an accurate and reproducible method for quantifying BDOC in aqueous samples. \n \nIn Chapter 2, I developed and evaluated a new method for determining BDOC in freshwater samples. The method includes filtering water samples to below 0.22 µm, to remove existing microbial cells, prior to inoculating the samples with a concentrated microbial inoculum produced by stepwise isolation of microbial cells from a peat sample. Additionally, I added solutions containing nitrogen (N) and phosphorus (P) (in the forms of ammonium nitrate (NH4NO3) and potassium phosphate (K2HPO4), respectively) to ensure that the microbes were not nutrient-limited. The samples were then capped with foam stoppers and incubated in the dark at 25⁰C on a shaker for 28 days to allow constant aeration during BDOC degradation. When applied to five freshwater samples collected from rivers, stormwater ponds, and a lake, and a glucose control, I observed that the amount of BDOC in the natural samples ranged from 15% to 53% and was 90% in the glucose control. Rates of BDOC degradation were calculated from DOC measurements at six sampling time points between days 0 and 28. I found that the DOC trends with time were best explained by two successive phases for BDOC degradation in all of the samples: an initial, fast, phase of BDOC degradation followed by a second, slower, phase of BDOC degradation where the rate constant for the second phase was between 5.57 and 565 times slower than for the initial phase. Changes in chemical characteristics of DOC measured using absorbance and fluorescence parameters including specific ultraviolet absorbance at 254 nm (SUVA254), humification index (HIX), and parallel factor analysis (PARAFAC) at each sampling time revealed that the initial, fast, phase of BDOC degradation often represents the utilization of small, non-aromatic compounds while the later, slower, phase of BDOC degradation often represents the utilization of more complex, aromatic compounds. The developed method provides a new approach to measure and characterize BDOC degradability and degradation kinetics that can be applied to future studies on biogeochemical processes in aquatic ecosystems. \n \nIn Chapter 3, I examined the potential for CO2, a greenhouse gas, to be produced from two stormwater ponds (SWPs) in the City of Kitchener, Ontario, Canada by quantifying the biodegradability of DOC entering the ponds through the inlet sewers during rain events. Further, BDOC, the fraction of DOC that can be mineralized by microbes during respiration to produce CO2, was related to the optical properties of water entering each of the SWPs to determine if any statistically significant relationships exist between BDOC and the optical properties of water. In the two studied SWPs, one with industrial land use and one with residential land use in the catchment area, we found significant negative linear correlations between BDOC and SUVA254, HIX, biologic index (BIX), and humic-like and tryptophan-like PARAFAC components. Additionally, there were significant positive linear correlations between BDOC and DOC concentration, benzoic acid, and tyrosine-like PARAFAC components. These optical properties are influenced by characteristics of the SWP catchment areas including imperviousness and land use. Overall, these findings indicate that increased urbanization results in changes in optical properties of DOC entering SWPs, increasing the amount of BDOC and, in turn, the potential for increased CO2 emissions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".