Adjustment of statistical distributions to O3 and NO2 concentrations in the pre-COVID-19 periods and during the pandemic in Campo Grande, MS, Brazil
Bibliographic record
Abstract
COVID-19 has triggered a series of studies based on environmental factors that may influence its dissemination. Among them, the role of air pollution and climate variables in the dissemination of SARS-CoV-2 stands out. Campo Grande (MS), located in the Central-West region of Brazil, offers a scenario to investigate such relationships. This study evaluated the relationship between ozone (O3) and nitrogen dioxide (NO2) concentrations, meteorological variables, and confirmed cases of COVID-19, focusing on the statistical modeling of the distributions of these pollutants in the periods before and after COVID-19. Daily time series of O3, NO2, temperature, relative humidity, precipitation, and COVID-19 data were analyzed, referring to the period from January to March 2020. Probabilistic models (log-normal, gamma, Weibull, Gumbel, and log-logistic) were applied and evaluated based on the Akaike and Bayesian information criteria to identify the best-fit statistical distribution for the pollutants. The results showed a negative correlation of COVID-19 with humidity and precipitation and a positive correlation with temperature, O3 and NO2. An increase in the mean concentrations of O3 (+2.7 DU) and NO2 (+3.731015 molecules/cm2) was observed during COVID-19. The log-normal distribution was best suited to O3, while the Gumbel distribution performed best for NO2.
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 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.000 | 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".