Consideration of local geographical variations in PM2·5 concentrations in China
Bibliographic record
Abstract
Global efforts have been made to reduce air pollution. The Lancet Commission on pollution and health estimates that in 2015, air pollution was responsible for 9 million premature deaths and 268 million disability-adjusted life-years, with the most severe effects on individuals residing in low-income and middle-income countries.1Hu H Landrigan PJ Fuller R Lim SS Murray CJL New Initiative aims at expanding Global Burden of Disease estimates for pollution and climate.Lancet Planet Health. 2018; 2: e415-e416Summary Full Text Full Text PDF PubMed Scopus (7) Google Scholar, 2Landrigan PJ Fuller R Acosta NJ et al.The Lancet Commission on pollution and health.Lancet. 2018; 391: 462-512Summary Full Text Full Text PDF PubMed Scopus (1963) Google Scholar Although various factors have been used to understand air pollution-related mortalities in low-income and middle-income countries,2Landrigan PJ Fuller R Acosta NJ et al.The Lancet Commission on pollution and health.Lancet. 2018; 391: 462-512Summary Full Text Full Text PDF PubMed Scopus (1963) Google Scholar geographical variations of air pollution concentrations in low-population dense and rural regions are often overlooked. Environmental programmes monitoring air pollution, including fine particulate matter (PM2·5), have widely been accredited to understanding their effect on people in urban areas, often ignoring people in low-population dense and rural regions.3Yan S Wu G Network analysis of fine particulate matter (PM2·5) emissions in China.Sci Rep. 2016; 6: 33227Crossref PubMed Scopus (15) Google Scholar, 4Chen D Liu X Lang J et al.Estimating the contribution of regional transport to PM2·5 air pollution in a rural area on the North China Plain.Sci Total Environ. 2017; 583: 280-291Crossref PubMed Scopus (112) Google Scholar Most areas of China (including rural regions) have recently increased industrialisation, which has generated PM2·5 concentrations that not only exceed the WHO air quality annual mean threshold,5Ji JS Air pollution and China's ageing society.Lancet Public Health. 2018; 3: e457-e458Summary Full Text Full Text PDF PubMed Scopus (7) Google Scholar but also are three times higher than the national guideline.4Chen D Liu X Lang J et al.Estimating the contribution of regional transport to PM2·5 air pollution in a rural area on the North China Plain.Sci Total Environ. 2017; 583: 280-291Crossref PubMed Scopus (112) Google Scholar High PM2·5 concentrations in low-population dense and rural regions increase the chances of air pollution-related mortality.6Brender JD Maantay JA Chakraborty J Residential proximity to environmental hazards and adverse health outcomes.Am J Public Health. 2011; 101: S37-S52Crossref PubMed Scopus (119) Google Scholar Thus, neglecting local variations in PM2·5 concentrations can potentially undervalue air pollution's full effects on human health.1Hu H Landrigan PJ Fuller R Lim SS Murray CJL New Initiative aims at expanding Global Burden of Disease estimates for pollution and climate.Lancet Planet Health. 2018; 2: e415-e416Summary Full Text Full Text PDF PubMed Scopus (7) Google Scholar In The Lancet Public Health (October, 2018), Tiantian Li and colleagues 7Li T Zhang Y Wang J et al.All-cause mortality risk associated with long-term exposure to ambient PM2·5 in China: a cohort study.Lancet Public Health. 2018; 3: e470-e547Summary Full Text Full Text PDF PubMed Scopus (150) Google Scholar studied all-cause mortality from long-term exposure to PM2·5 in both Chinese men and women aged 65 years and older from 2008 to 2014. Specifically, the authors note a higher hazard ratio in rural than in urban regions of southern versus northern China, in regard to exposure of lower versus higher PM2·5 concentrations.7Li T Zhang Y Wang J et al.All-cause mortality risk associated with long-term exposure to ambient PM2·5 in China: a cohort study.Lancet Public Health. 2018; 3: e470-e547Summary Full Text Full Text PDF PubMed Scopus (150) Google Scholar However, they omitted nine Chinese provinces from their analysis because of low-population densities in 2008. By excluding individuals in these provinces from their analysis, the authors could be underestimating or overestimating the effect that PM2·5 concentrations can have on individuals residing in rural or urban regions of China. With regard to PM2·5 exposure associated with all-cause mortality, the authors note that their results differ from the Global Burden of Disease Study (GBD) estimates in 2010.7Li T Zhang Y Wang J et al.All-cause mortality risk associated with long-term exposure to ambient PM2·5 in China: a cohort study.Lancet Public Health. 2018; 3: e470-e547Summary Full Text Full Text PDF PubMed Scopus (150) Google Scholar The neglected local geographical variations in PM2·5 concentrations among low-population dense and rural regions might promote a smaller or larger difference of results in comparison with GBD estimates. Furthermore, individuals residing in low-population dense and rural regions have lifestyles that might contribute to relatively higher PM2·5 concentrations.3Yan S Wu G Network analysis of fine particulate matter (PM2·5) emissions in China.Sci Rep. 2016; 6: 33227Crossref PubMed Scopus (15) Google Scholar These individuals are often limited by their resources and rely on burning biomass for basic daily necessities, such as cooking or farming purposes, leading to adverse health effects over time and increased possibility of mortality due to air pollution.3Yan S Wu G Network analysis of fine particulate matter (PM2·5) emissions in China.Sci Rep. 2016; 6: 33227Crossref PubMed Scopus (15) Google Scholar Moreover, to fully understand the long-term relationship between mortality risk and PM2·5 concentrations in individuals aged 65 years and older, not only for China, but overall in low-income and middle-income countries, local geographical variations in PM2·5 concentrations, particularly a comparison of the effects of air pollution on low-population or high-population dense areas should be incorporated in future analyses. Understanding local geographical variations in PM2·5 concentrations can also be included in the new GBD-Pollution and Health Initiative targeted to reduce air pollution-related mortalities along with avoiding environmental tragedies that have plagued past development.1Hu H Landrigan PJ Fuller R Lim SS Murray CJL New Initiative aims at expanding Global Burden of Disease estimates for pollution and climate.Lancet Planet Health. 2018; 2: e415-e416Summary Full Text Full Text PDF PubMed Scopus (7) Google Scholar I declare no competing interests. All-cause mortality risk associated with long-term exposure to ambient PM2·5 in China: a cohort studyLong-term exposure to PM2·5 is associated with an increased risk of all-cause mortality among adults aged 65 years and older in China, but the magnitude of the risk declines as the concentration of PM2·5 increases. Full-Text PDF Open AccessNew Initiative aims at expanding Global Burden of Disease estimates for pollution and climateThe Institute for Health Metrics and Evaluation (IHME) held a workshop in Seattle (March 1 and 2, 2018) to plan a new Initiative—the Global Burden of Disease (GBD)-Pollution and Health Initiative—to increase knowledge on the contributions of pollution and climate to the GBD study. The workshop attracted 60 attendees from around the world, including experts in environmental health research, economics, and policy; senior scientists based at IHME; and representatives from the US National Institute for Environmental Health Sciences, environmental non-governmental organisations, the UN Environment Programme, and major foundations. Full-Text PDF Open AccessThe Lancet Commission on pollution and healthPollution is the largest environmental cause of disease and premature death in the world today. Diseases caused by pollution were responsible for an estimated 9 million premature deaths in 2015—16% of all deaths worldwide—three times more deaths than from AIDS, tuberculosis, and malaria combined and 15 times more than from all wars and other forms of violence. In the most severely affected countries, pollution-related disease is responsible for more than one death in four. Full-Text PDF Consideration of local geographical variations in PM2·5 concentrations in China – Authors' replyHaris Majeed commented on our study,1 pointing out that geographical variations in air pollution concentrations, even in low-population densities and rural regions, are important to understand the full effect of air pollution, and suggesting that not including nine provinces could affect our analysis. These are valid concerns, especially when a nationally representative cohort is used. Full-Text PDF Open Access
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".