These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


Pubmed for Handhelds

PUBMED FOR HANDHELDS

Search MEDLINE/PubMed

  • Title: Global estimates of daily ambient fine particulate matter concentrations and unequal spatiotemporal distribution of population exposure: a machine learning modelling study.
    Author: Yu W, Ye T, Zhang Y, Xu R, Lei Y, Chen Z, Yang Z, Zhang Y, Song J, Yue X, Li S, Guo Y.
    Journal: Lancet Planet Health; 2023 Mar; 7(3):e209-e218. PubMed ID: 36889862.
    Abstract:
    BACKGROUND: Short-term exposure to ambient PM2·5 is a leading contributor to the global burden of diseases and mortality. However, few studies have provided the global spatiotemporal variations of daily PM2·5 concentrations over recent decades. METHODS: In this modelling study, we implemented deep ensemble machine learning (DEML) to estimate global daily ambient PM2·5 concentrations at 0·1° × 0·1° spatial resolution between Jan 1, 2000, and Dec 31, 2019. In the DEML framework, ground-based PM2·5 measurements from 5446 monitoring stations in 65 countries worldwide were combined with GEOS-Chem chemical transport model simulations of PM2·5 concentration, meteorological data, and geographical features. At the global and regional levels, we investigated annual population-weighted PM2·5 concentrations and annual population-weighted exposed days to PM2·5 concentrations higher than 15 μg/m3 (2021 WHO daily limit) to assess spatiotemporal exposure in 2000, 2010, and 2019. Land area and population exposures to PM2·5 above 5 μg/m3 (2021 WHO annual limit) were also assessed for the year 2019. PM2·5 concentrations for each calendar month were averaged across the 20-year period to investigate global seasonal patterns. FINDINGS: Our DEML model showed good performance in capturing the global variability in ground-measured daily PM2·5, with a cross-validation R2 of 0·91 and root mean square error of 7·86 μg/m3. Globally, across 175 countries, the mean annual population-weighted PM2·5 concentration for the period 2000-19 was estimated at 32·8 μg/m3 (SD 0·6). During the two decades, population-weighted PM2·5 concentration and annual population-weighted exposed days (PM2·5 >15 μg/m3) decreased in Europe and northern America, whereas exposures increased in southern Asia, Australia and New Zealand, and Latin America and the Caribbean. In 2019, only 0·18% of the global land area and 0·001% of the global population had an annual exposure to PM2·5 at concentrations lower than 5 μg/m3, with more than 70% of days having daily PM2·5 concentrations higher than 15 μg/m3. Distinct seasonal patterns were indicated in many regions of the world. INTERPRETATION: The high-resolution estimates of daily PM2·5 provide the first global view of the unequal spatiotemporal distribution of PM2·5 exposure for a recent 20-year period, which is of value for assessing short-term and long-term health effects of PM2·5, especially for areas where monitoring station data are not available. FUNDING: Australian Research Council, Australian Medical Research Future Fund, and the Australian National Health and Medical Research Council.
    [Abstract] [Full Text] [Related] [New Search]