Temporal trends in ambient fine particulate matter and the impacts of COVID-19 on this pollutant in Grenada, West Indies
Bibliographic record
Abstract
Most Caribbean islands do not have air pollution surveillance programs. Those who live in these countries are exposed to ambient air pollution from a variety of sources including motor vehicles, ocean-going vessels, and Saharan dust. We conducted an air sampling exposure study in Grenada to describe daily changes in fine particulate matter (PM2.5) pollution, and during Saharan dust episodes. Further, we assessed the impacts of COVID-19 public health interventions on PM2.5 concentrations in 2020. Four fixed-site PurpleAir monitors were installed throughout Grenada, and one on the neighboring island of Carriacou. PM2.5 was measured between January 6 and December 31, 2020. We classified each of these days based on whether COVID-19 public health mitigation measures were in place or not. Descriptive analyses were performed to characterize fluctuations in PM2.5, and we assessed the impacts of public health restrictions on PM2.5 using multivariate regression. The mean daily PM2.5 concentration in 2020 was 4.4 μg/m3. During the study period, the minimum daily PM2.5 concentration was 0.7 μg/m3, and the maximum was 20.4 μg/m3. Daily mean PM2.5 concentrations more than doubled on Saharan dust days (8.5 vs 3.6 μg/m3; p < 0.05). The daily mean PM2.5 concentrations were estimated to be 1.2 μg/m3 lower when COVID-19 restrictions were in effect. Ambient PM2.5 concentrations in Grenada are relatively low compared to other countries; however, Saharan dust episodes represent an important source of exposure. Low-cost sensors provide an opportunity to increase surveillance of air pollution in the Caribbean, however their value could be enhanced with the development of correction algorithms that more closely approximate values from reference-grade monitors.Implications: This study describes daily fluctuations in ambient PM2.5 concentrations in Grenada in 2020. Overall, concentrations of PM2.5 were low; however, we found that Saharan dust events cause daily exceedances in PM2.5 above the current 24-hr limits of the World Health Organization. Moreover, the constructed models suggest that public health interventions to reduce the spread of COVID-19 reduced PM2.5 concentrations by 27%.
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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 | 0.001 |
| 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 | 0.001 | 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".