Assessing the Emerging Environmental Concerns from Bio-originated Organic Pollutants in Cropping Systems
Bibliographic record
Abstract
Agriculture plays a significant role in achieving the goal of carbon neutrality and emission reduction through practices such as crop residue management. Crop residues can be utilized to produce biodegradable mulches (BMs), which can increase crop production and carbon sequestration potential. However, agricultural health and safety are facing new challenges, particularly concerning bio-originated organic pollutants in cropping systems, including biogenic volatile organic compounds (BVOCs) and biodegradable microplastics (BMPs). The main purpose of the research presented in this dissertation is to assess these emerging environmental concerns, including the appropriate assessment of BVOC emissions and the degradation and fragmentation of BMs. \n \nBVOC emissions were generally influenced by various factors, including temperature, drought, solar radiation, humidity, nutrient availability, carbon dioxide (CO2), ozone (O3), etc. Among these factors, growth length, air temperature, solar radiation, and leafage were found to be the most important variables affecting the spatial-temporal variations of methanol (MeOH) emissions from spring wheat during the growing period in a Canadian province. The seasonality of MeOH emissions was positively correlated with concentrations of carbon monoxide (CO), filterable particulate matter (FPM), and coarse particulate matter (PM10), but negatively related to nitrogen dioxide (NO2) and O3. Compared with paper mulch, bioplastic mulch contributed a higher amount of aromatic structure-containing chemicals and carboxylic acids, to the water environment, but released fewer and smaller plastic particles. After entering the soil-water environment, the rough microstructure and oxygenated functional groups on BMP surfaces played a crucial role in the adsorption of aromatic compounds and heavy metals from soils. \n \nScientometric analysis can provide researchers with an in-depth understanding of BVOC emission mechanisms, while also offering decision-makers insights into emission mitigation and environmental management. The newly developed BVOC assessment approach, designed to evaluate the biogenic MeOH emitted from crops during growing seasons, can help uncover the relationships between BVOC emissions and key influencing factors. The characterization and quantification of BMPs in cropping systems focused on examining the fragmentation and degradation of BMPs under UV irradiation using visual inspection and quantitative analysis. This dissertation offers scientific support for researching and further developing the impact of BVOC emissions and BMP generation on environmental management.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".