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Record W7162085624 · doi:10.82308/35483

Pesticides in the urban environment: Targeted and non-targeted screening of pesticide profiles in urban honey from Montreal by LC-QTOF-MS.

2023· dissertation· en· W7162085624 on OpenAlexaboutno aff
Caren Akiki

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldAgricultural and Biological Sciences
TopicBee Products Chemical Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsPesticidePesticide residuePopulationUrban areaPesticide applicationPollutant

Abstract

fetched live from OpenAlex

The safety of urban honey is a growing concern due to pesticide contamination, necessitating effective monitoring methods. Small-scale urban beekeeping has emerged as a promising solution to counter the honeybee population decline and high honey demand. However, urban pesticides pose risks as bees unintentionally collect them while foraging, leading to pesticide residue accumulation in honey. This issue has been substantiated by various reports confirming the occurrence of pesticides in urban settings and their detection in honey, underscoring the importance of addressing this concern for both honey producers and consumers. Recognized as a promising approach for pesticide residue monitoring in food samples, non-targeted analysis (NTA) has gained significant attention. While non-targeted studies have increased, honey-focused research is limited, and as far as we know, no studies have been conducted on urban honey to better understand human exposure to pollutants in such honey. To address these concerns, in Chapter 3, a direct injection technique coupled with high-performance liquid chromatography and quadrupole time-of-flight mass spectrometry was employed as an NTA to detect pesticides in urban honey. The technique was validated according to the SANCO guideline recommendations for 21 key pesticides and was assessed to be robust and sensitive for this application. It was able to detect pesticide residues 2 to 1000 times below Canada's 0.1 ppm limit and yielded comparable results to methods involving prior sample preparation. The instrument linearity, repeatability, and recoveries met satisfactory criteria, ensuring the method's reliability. Subsequently, the method was applied for targeted (79 pesticides) and non-targeted screenings of 118 urban honey samples collected from Montreal, Canada in 2021. None of the 79 pesticide residues were present at levels above the Limit of Detection (LOD) suggesting that targeted pesticides are of no concern. However, employing an NTA revealed the presence of 111 compounds tentatively identified with scores above 80%, highlighting the need for additional study. The physicochemical parameters of urban honey and rural Quebec honey were assessed and showed to be similar, except for urban honey's slightly higher electrical conductivity. This shows that physicochemical features alone cannot distinguish urban from rural honey. The developed method effectively detected pesticides in urban honey, while the NTA identified unknown compounds warranting further investigation. Additionally, the comprehensive analysis of urban honey samples, including moisture content, pH, and electrical conductivity, provided valuable insights into the quality of honey collected from Montreal in 2021. In Chapter 4, the investigation aimed to evaluate the potential of pesticide measurements in urban honey and air as mutually beneficial and supplementary approaches by comparing the results with those obtained from an artificial XAD-resin-based passive air sampler (PAS). 118 urban honey samples were collected from Montreal, while 40 sites across Montreal were selected for the deployment of the XAD-PAS over three months during the summer of 2021. The screening technique combining an NTA and HPLC-QTOF-MS was used to identify pesticides in both matrices obtained from urban areas. The method was effective in identifying targeted compounds of interest. The insect-repellent DEET was found in 28 of the 40 passive air samplers analyzed. While DEET, a known environmental contaminant, was found in PASs, its presence could not be unequivocally identified in the urban honey samples. None of other target pesticides were detectable in the PAS. The findings from this study offer proof that measuring pesticides in both urban honey and air can effectively function as complementary and advantageous methods. In summary, this study showcased the effectiveness of NTA in enhancing our understanding of the presence of contaminants in urban honey

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.729
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.197
Teacher spread0.188 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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