Temporal trends in ambient fine particulate matter in Grenada between 2009 and 2022
Bibliographic record
Abstract
Despite its association with significant morbidity and mortality, ambient fine particulate matter (PM 2.5 ) is not robustly monitored in the Caribbean region. To estimate historical PM 2.5 concentration trends in Grenada, an island often impacted by Saharan dust storms, a daily time series analysis was conducted using PM 2.5 reanalysis data from the Modern-Era Retrospective Analysis for Research and Applications (MERRA-2) as a proxy, due to the absence of reference ground monitoring stations. A multivariate linear regression model was used to evaluate the interactions between MERRA-2 PM 2.5 concentrations and meteorological correlates. Finally, MERRA-2 PM 2.5 concentrations were compared to low-cost ground monitoring data obtained from PurpleAir laser particle counters. Although mean PM 2.5 concentrations remained relatively stable over the study period (14.50 μg m −3 ), significant variability was noted with a standard deviation of 8.07 μg m −3 . PM 2.5 concentrations were consistently and significantly higher during the months typically associated with Saharan dust events, with pronounced peaks linked to recorded dust storms and other natural phenomena. Given that PM 2.5 exposure is linked to various adverse health effects, the findings from this study can serve as a basis for establishing more comprehensive monitoring networks. Additionally, these findings can help inform air quality management decisions that are better tailored to the region. Finally, the lessons learned from this evaluation of temporal trends in PM 2.5 can serve to enhance research capabilities and support the implementation of air quality management initiatives in Grenada and other small island developing states in the region.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".