Air pollution and pregnancy: A long history of rising exposure
Bibliographic record
Abstract
Humans have been exposed to air pollution for millennia, from both natural (volcanoes and wildfires) and man-made (heating, lighting, cooking and manufacturing) sources. By the Middle Ages air pollution was already recognised as a health problem in England. Burning coal rather than wood was prohibited in London in 1273, having been declared ‘prejudicial to health’, and King Edward I of England supported this ban with a royal proclamation in 1306 (www.air-quality.org.uk). However, this measure was difficult to implement. Severe pollution episodes or ‘smogs’ (resulting from the combination of smoke, fog and sulphur dioxide (SO2) emissions) were recorded from as early as the 17th century. The energy to power the industrial revolution required a vast consumption of fossil fuel. The resulting smoke from factories and railways rapidly became the dominant source of air pollution in the towns of the UK. Smogs or ‘pea-soupers’ were a regular feature of life in Victorian London, and recurred throughout the 20th century. From the late 19th century, spreading industrialisation has accelerated the emission of air pollutants worldwide. An additional related risk, first recognised by Canadian physicist Gilbert Plass in the1950s, and now well established, was the link between the increased use of fossil fuels and the rising levels of carbon dioxide (CO2) in the atmosphere, leading to ‘global warming’ (Plass, Scientific American 1959;201:41–7). Much of the epidemiological, clinical and basic science research into the effects of air pollution on human health has focused on the development of respiratory diseases, particularly asthma (Bharadwaj et al., Am J Respir Crit Care Med 2016;194:1475–82). However, in recent decades, there has been a growing awareness of the association between maternal exposure to air pollution during pregnancy and the risks of preterm birth, placental abruption and stillbirth. In a small study comparing carbon monoxide (CO) levels in maternal and fetal blood between groups of smokers and non-smokers, Young and Pugh found no significant difference. They concluded that maternal exposure to CO during pregnancy is not the main factor in the association between smoking and low birthweight. They also found that CO levels were higher in both groups than in non-smoking male laboratory workers, indicating potential environmental exposure to CO from sources other than smoking (Young & Pugh, J Obstet Gynaecol Br Commonw 1963;70:681–4) (Figure 1). The challenge of establishing the individual contribution of exposure to a particular environmental factor is illustrated in an article by Baird (Br J Obstet Gynaecol 1980;87:1057–84). He describes the many confounding socio-economic factors that have specific effects on pregnancy outcome, such as unemployment, poverty and health during childhood, and that are impossible to separate. Following implementation of version 2 of the Saving Babies Lives Care Bundle (SBLCBv2) and to comply with the recommendations of the Maternity Incentive Scheme, all UK National Health Service (NHS) trusts are now required to assess maternal CO levels throughout pregnancy. The identification of high CO levels may perhaps motivate pregnant women to quit smoking, service their gas appliances and instal CO alarms at home, but tackling the wider causes of air pollution will require more concerted action from all of us and from our governments. The authors declare that they have no conflicts of interest.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".