Editorial: Science to Foster the WHO Air Quality Guideline Values
Bibliographic record
Abstract
There is a large global body of evidence linking exposure to air pollution, especially fine particulate matter (PM2.5), with impacts on all major human organ systems. Furthermore, epidemiological studies have now documented health effects at levels below current national ambient air quality standards. The Health Effects Institute recently completed a comprehensive research initiative to investigate the health effects of long-term exposure to low levels of air pollution in Europe, Canada and the United States [3]. Particular strengths of the studies included the large populations (7-69 million people), state-of-the-art exposure assessment methods, and thorough statistical analyses that applied novel methods. All three studies documented positive associations between mortality and exposure to PM2.5 at levels as low as 4 μg/m 3 or even lower. Furthermore, the studies observed linear (United States), or supra-linear (Canada and Europe) exposure-response functions for PM2.5 and mortality, with no evidence for a threshold. This research initiative provided important new evidence of the adverse effects of long-term exposures to low levels of air pollution at and below current standards, suggesting that further reductions in air pollution could yield larger benefits than previously anticipated [3].Based on these and other studies, the World Health Organization (WHO) released new Air Quality Guidelines (AQG) in September 2021. They recommended that annual mean concentrations of PM2.5 should not exceed 5 μg/m 3 , finding that adverse health effects occur above this concentration [4]. They also recommended a set of interim targets, meant to provide a step wise pathway towards achievement of the AQG values set at 35, 25, 15, and 10 μg/m 3 . Governments in the United States and Europe have recently moved toward more stringent PM2.5 standards-9 and 10 µg/m 3 , respectively-to align more closely with the 2021 WHO AQG [5,6]. Meanwhile, the Federal Commission of Air Hygiene advised the Swiss Government to adopt the new WHO AQG values as the national standards [7]. Others such as Uganda have recently adopted National Air Quality Standards for the first time [8], and Brazil has adopted the National Air Quality Policy with progressive air quality targets consistent with the 2021 WHO AQG [9]. This Special Issue, entitled "Science to Foster the WHO Air Quality Guideline Values", presents recent science that underpins the WHO AQG and offers insights into pathways for action. One issue is abundantly clear -the disease burden from air pollution is not borne equally across the world, with countries in Asia, Africa, and the Middle East experiencing the highest levels of ambient PM2.5 and associated health impacts (Figure 1). Hence, there is a particular need to improve air quality in those regions [e.g., 10,11].Much of what is currently known about the adverse effects of ambient air pollution and its solutions come from studies conducted in high-income regions, especially North America and Western Europe, with relatively low air pollution levels, and in more recent years, from studies in China where air pollution levels are relatively high [12,13]. As governments around the world act to improve air quality, there is a continuing need for research to strengthen the local evidence base on disease risk at relatively high levels of air pollution, identify the air pollution sources most responsible for disease burden and assess the public health effectiveness of actions taken to improve air quality. Such studies are also invaluable for strengthening local scientific and infrastructure capacities, raising awareness of local communities, and supporting evidence-based decision making. To strengthen awareness, there is also a need to update Air Quality Index toolsused by many authorities to communicate the state of air quality on a daily basis -with the 2021 WHO AQG [14]. More research is also needed to capture the direct and indirect health effects of climate change more fully, including the interactions with air pollution.Overall, bold air quality and climate actions are needed at all levels-international, national, localand across all sectors such as transport, energy, industry, agriculture, and residential. There is cause for optimism: there are various examples from locations across the globe that show that if action is taken to improve air quality, so does population health [15]. Scientific data and evidence such as that presented in the articles in the Special Issue, will continue to play a fundamental role in fostering evidence-based air quality and climate actions, to reduce the inequity in air quality both within and across countries, and to close the gap between national air quality standards and the 2021 WHO AQG.
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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 | 0.008 | 0.047 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.026 | 0.030 |
| Insufficient payload (model declined to judge) | 0.027 | 0.026 |
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".