Health Burden of Ambient Air Pollution on Respiratory Diseases in the Delhi–National Capital Region (2010–2025): A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Introduction: Ambient air pollution is a major contributor to respiratory morbidity and mortality globally, with disproportionate impacts in low- and middle-income countries. The Delhi–National Capital Region (NCR) experiences persistently high levels of air pollution, yet region-specific quantitative syntheses of respiratory health impacts remain limited. This study aimed to systematically review and meta-analyse epidemiological evidence on the association between ambient air pollution and respiratory outcomes in Delhi–NCR from 2010 to 2025. Methods: A systematic review and meta-analysis were conducted in accordance with PRISMA 2020 guidelines. PubMed/MEDLINE, Scopus, Web of Science, Embase, and additional sources were searched for eligible studies examining associations between ambient air pollutants (PM₂.₅, PM₁₀, NO₂, SO₂, O₃, CO) and respiratory morbidity or mortality in Delhi–NCR. Time-series, case-crossover, cohort, and observational studies reporting quantitative effect estimates were included. Random-effects meta-analyses were performed to derive pooled relative risks per 10 µg/m³ increase in pollutant concentration. Risk of bias was assessed using adapted ROBINS-E and Newcastle–Ottawa tools. Results: Ten studies met inclusion criteria for qualitative synthesis, and seven were included in meta-analysis. Short-term increases in PM₂.₅ were associated with higher respiratory emergency visits (RR 1.038; 95% CI: 1.021–1.056) and respiratory mortality (RR 1.019; 95% CI: 1.006–1.033). PM₁₀ exposure was linked to increased respiratory admissions (RR 1.026; 95% CI: 1.012–1.041), while NO₂ showed a strong association with acute respiratory visits (RR 1.041; 95% CI: 1.018–1.064). Heterogeneity ranged from low to moderate. Conclusion: Ambient air pollution is consistently associated with increased respiratory morbidity and mortality in Delhi–NCR. These findings underscore the urgent need for strengthened air quality interventions and targeted public health strategies in highly polluted urban settings.
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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 | 0.020 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.051 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".