Five-Year PM2.5 Trends in Lahore: A Monthly and Annual Overview (2019–2023)
Bibliographic record
Abstract
This study presents a comprehensive analysis of monthly and yearly average PM2.5 concentrations in Lahore, Pakistan, from 2019 to 2023, utilizing data sourced from IQAir. The research aims to understand the influence of meteorological parameters on air quality trends and to identify significant patterns and shifts in pollution levels over the five-year period. Results reveal a distinct seasonal cycle, with peak pollution occurring during the colder, stagnant winter months (October-February) characterized by lower temperatures, reduced wind speeds, and frequent temperature inversions. Conversely, the lowest PM2.5 concentrations are observed during the warmer, rainy summer months (May-August) due to enhanced atmospheric dispersion and wet deposition. Year-on-year comparisons indicate a persistent air quality challenge, with a concerning escalation in peak pollution levels observed in November 2023, recording the highest monthly average in the dataset. The analysis also highlights the temporary air quality improvements during the COVID-19-induced economic slowdown in 2020, followed by a rebound and increase in pollution levels as economic activity resumed. This study underscores the critical need for more aggressive and sustained emission control measures in Punjab to mitigate the adverse impacts of air pollution on public health and the environment.
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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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".