Estimating greenhouse gas emissions from land-applied biosolids in Canada: A mathematical modelling approach
Bibliographic record
Abstract
Municipal wastewater biosolids are increasingly used to fertilize crops on Canadian farmland with the attendant effect of greenhouse gas (GHG) emissions. As part of their national inventories to the United Nations Framework Convention on Climate Change, countries are required to estimate GHG emissions from the land application of biosolids and report them using Intergovernmental Panel on Climate Change (IPCC) protocols. However, Canada currently does not have N2O emission factors to accurately model emissions from biosolids because of scarce empirical data. This study therefore measured emissions, generated emissions factors, and refined models of biosolids-induced GHG emissions to improve Canada’s national GHG inventory. To do this, laboratory and field experiments were used to assess three types of biosolids (i.e., composted, mesophilic anaerobically digested called “digested”, and alkaline-stabilized). In an incubation experiment, soil samples were amended with the three biosolids to assess the rate of C and N mineralization under 29% and 49% water-filled pore space (WFPS) soil moisture conditions. Under 49% WFPS, 79%, 52%, and 8% of C was mineralized in the digested, alkaline-stabilized, and composted biosolids, respectively. A first-order mathematical equation was fitted to the cumulative CO2-C and N2O-N emissions data, with R2 > 0.98 and p < 0.05. This study also highlighted the potential of composted biosolids to sequester carbon in soil and mitigate soil N2O emissions. The results of this experiment helped to calibrate the DeNitrification and DeComposition (DNDC) model to simulate C and N dynamics in a biosolids-fertilized corn (Zea mays L.) field in Quebec from 2017 to 2019. Pearson’s correlation coefficients between measured and simulated data ranged between 0.3 and 0.8 for crop yield, daily and cumulative CO2 and N2O emissions, and soil organic carbon, while being 0.1 for total soil N. In addition to the Quebec (mixed wood plains) site, DNDC was then used to simulate N2O emissions from two other sites in Nova Scotia (Atlantic maritime) and Alberta (prairie). Overall, N2O emissions were highest for digested biosolids and overall emissions were influenced by site-specific factors, with emissions magnitudes following the order: Quebec > Nova Scotia > Alberta. The DNDC simulations were contrasted with IPCC Tier 1 and Tier 2 methods, and root mean-square error and coefficient of determination values between measured and simulated values showed that the DNDC (Tier 3) approach was more accurate than the Tier 1 and 2 methods. Empirically derived correction factors for each of the biosolids were proposed to improve the accuracy of Tier 2 method, which fits the proposed update to the Canadian GHG inventory methodology.This study resulted in improved estimates of biosolids-induced N2O emissions from Canadian farmlands, with the option to use an improved Tier 2 method to report such emissions in the national GHG inventory until the Tier 3 method is implemented
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".