IMPACT OF ENVIRONMENTAL POLLUTANTS ON PUBLIC HEALTH OUTCOMES: A SYSTEMATIC REVIEW OF GLOBAL EVIDENCE
Bibliographic record
Abstract
Background: Environmental pollution poses a major global public health challenge, contributing to a wide range of chronic diseases and premature mortality. While numerous individual studies have explored associations between pollutants and specific health outcomes, findings are often fragmented and context-specific, limiting broader application. A comprehensive synthesis of current global evidence is necessary to inform policy and guide public health interventions. Objective: This systematic review aims to evaluate and synthesize global evidence on the impact of environmental pollutants—including air, water, and soil contaminants—on population-level health outcomes such as respiratory illness, cancer, neurodevelopmental disorders, and all-cause mortality. Methods: A systematic review was conducted following PRISMA guidelines. Searches were performed across PubMed, Scopus, Web of Science, and the Cochrane Library for studies published between 2010 and 2024. Inclusion criteria encompassed observational studies, cohort studies, randomized controlled trials, and systematic reviews involving human populations exposed to environmental pollutants with reported health outcomes. Studies were screened by two independent reviewers, and data extraction was performed using a standardized form. Risk of bias was assessed using the Newcastle-Ottawa Scale and the Cochrane Risk of Bias Tool. A narrative synthesis was performed due to heterogeneity in study designs and outcomes. Results: Eight high-quality studies were included, encompassing diverse global populations and pollutant exposures. Key findings indicated significant associations between PM2.5 and respiratory and cardiovascular mortality (p < 0.01), arsenic and nitrate in drinking water with increased cancer risk (HR > 2.0), and lead exposure with neurodevelopmental delays in children. All-cause mortality was consistently elevated in populations exposed to ambient air pollution across multiple regions. Conclusion: Environmental pollutants are strongly associated with a range of adverse public health outcomes, underscoring the need for enhanced regulatory policies and integrated clinical awareness of environmental risks. Although the findings are supported by robust evidence, further standardized, longitudinal research is warranted to deepen understanding and guide targeted interventions.
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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 | 0.026 | 0.103 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.012 |
| Bibliometrics | 0.022 | 0.021 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".