Reversal of trends in global fine particulate matter air pollution.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
02 09 2023
02 09 2023
Historique:
received:
26
06
2023
accepted:
21
08
2023
medline:
4
9
2023
pubmed:
3
9
2023
entrez:
2
9
2023
Statut:
epublish
Résumé
Ambient fine particulate matter (PM
Identifiants
pubmed: 37660164
doi: 10.1038/s41467-023-41086-z
pii: 10.1038/s41467-023-41086-z
pmc: PMC10475088
doi:
Substances chimiques
Particulate Matter
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
5349Subventions
Organisme : World Health Organization
ID : 001
Pays : International
Informations de copyright
© 2023. Springer Nature Limited.
Références
Murray, C. J. L. et al. Global burden of 87 risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet 396, 1223–1249 (2020).
Burnett, R. et al. Global estimates of mortality associated with long-term exposure to outdoor fine particulate matter. Proc. Natl Acad. Sci. USA 115, 9592–9597 (2018).
pubmed: 30181279
pmcid: 6156628
Cohen, A. J. et al. Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the Global Burden of Diseases Study 2015. Lancet 389, 1907–1918 (2017).
pubmed: 28408086
pmcid: 5439030
Burnett, R. T. et al. An integrated risk function for estimating the global burden of disease attributable to ambient fine particulate matter exposure. Environ. Health Perspect. 122, 397–403 (2014).
pubmed: 24518036
pmcid: 3984213
Burnett, R. T., Spadaro, J. V., Garcia, G. R. & Pope, C. A. Designing health impact functions to assess marginal changes in outdoor fine particulate matter. Environ. Res. 204, 112245 (2022).
pubmed: 34687750
Apte, J. S., Brauer, M., Cohen, A. J., Ezzati, M. & Pope, C. A. I. Ambient PM2.5 reduces global and regional life expectancy. Environ. Sci. Technol. Lett. 5, 546–551 (2018).
Yin, H. et al. Population ageing and deaths attributable to ambient PM2·5 pollution: a global analysis of economic cost. Lancet Planet. Health 5, e356–e367 (2021).
pubmed: 34119010
Muller, N. Z. Boosting GDP growth by accounting for the environment. Science 345, 873–874 (2014).
pubmed: 25146270
Maji, K. J., Ye, W.-F., Arora, M. & Nagendra, S. M. S. PM2.5-related health and economic loss assessment for 338 Chinese cities. Environ. Int. 121, 392–403 (2018).
pubmed: 30245362
Apte, J. S., Marshall, J. D., Cohen, A. J. & Brauer, M. Addressing global mortality from ambient PM2.5. Environ. Sci. Technol. 49, 8057–8066 (2015).
pubmed: 26077815
Wang, S., Zhou, C., Wang, Z., Feng, K. & Hubacek, K. The characteristics and drivers of fine particulate matter (PM2.5) distribution in China. J. Clean. Prod. 142, 1800–1809 (2017).
Lim, C.-H., Ryu, J., Choi, Y., Jeon, S. W. & Lee, W.-K. Understanding global PM2.5 concentrations and their drivers in recent decades (1998–2016). Environ. Int. 144, 106011 (2020).
pubmed: 32795749
McDuffie, E. E. et al. Source sector and fuel contributions to ambient PM2.5 and attributable mortality across multiple spatial scales. Nat. Commun. 12, 3594 (2021).
pubmed: 34127654
pmcid: 8203641
Crippa, M. et al. Forty years of improvements in European air quality: regional policy-industry interactions with global impacts. Atmos. Chem. Phys. 16, 3825–3841 (2016).
Xing, J. et al. Historical gaseous and primary aerosol emissions in the United States from 1990 to 2010. Atmos. Chem. Phys. 13, 7531–7549 (2013).
Wang, H., Zhang, L., Yao, X., Cheng, I. & Dabek-Zlotorzynska, E. Identification of decadal trends and associated causes for organic and elemental carbon in PM2.5 at Canadian urban sites. Environ. Int. 159, 107031 (2022).
pubmed: 34890898
Meng, J. et al. Estimated long-term (1981–2016) concentrations of ambient fine particulate matter across north america from chemical transport modeling, satellite remote sensing, and ground-based measurements. Environ. Sci. Technol. 53, 5071–5079 (2019).
pubmed: 30995030
Hsu, N. C. et al. Global and regional trends of aerosol optical depth over land and ocean using SeaWiFS measurements from 1997 to 2010. Atmos. Chem. Phys. 12, 8037–8053 (2012).
van Donkelaar, A., Martin, R. V., Brauer, M. & Boys, B. L. Use of satellite observations for long-term exposure assessment of global concentrations of fine particulate matter. Environ. Health Perspect. 123, 135–143 (2015).
pubmed: 25343779
Southerland, V. A. et al. Global urban temporal trends in fine particulate matter (PM2·5) and attributable health burdens: estimates from global datasets. Lancet Planet. Health 6, e139–e146 (2022).
pubmed: 34998505
pmcid: 8828497
Butt, E. W. et al. Global and regional trends in particulate air pollution and attributable health burden over the past 50 years. Environ. Res. Lett. 12, 104017 (2017).
Shaddick, G., Thomas, M. L., Mudu, P., Ruggeri, G. & Gumy, S. Half the world’s population are exposed to increasing air pollution. npj Clim. Atmos. Sci. 3, 23 (2020).
Yang, X. et al. Health risk and disease burden attributable to long-term global fine-mode particles. Chemosphere 287, 132435 (2022).
pubmed: 34606897
Pandey, A. et al. Health and economic impact of air pollution in the states of India: the Global Burden of Disease Study 2019. Lancet Planet. Health 5, e25–e38 (2021).
Zhang, Q. et al. Drivers of improved PM2.5 air quality in China from 2013 to 2017. Proc. Natl Acad. Sci. USA 116, 24463–24469 (2019).
pubmed: 31740599
pmcid: 6900509
Geng, G. et al. Drivers of PM2.5 air pollution deaths in China 2002–2017. Nat. Geosci. 14, 645–650 (2021).
Li, C., Hammer, M. S., Zheng, B. & Cohen, R. C. Accelerated reduction of air pollutants in China, 2017-2020. Sci. Total Environ. 803, 150011 (2022).
pubmed: 34525772
van Donkelaar, A. et al. Monthly global estimates of fine particulate matter and their uncertainty. Environ. Sci. Technol. 55, 15287–15300 (2021).
pubmed: 34724610
Zhai, S. et al. Fine particulate matter (PM
Singh, V., Singh, S. & Biswal, A. Exceedances and trends of particulate matter (PM(2.5)) in five Indian megacities. Sci. Total Environ. 750, 141461 (2021).
pubmed: 32882489
Gui, K. et al. Seasonal variability and trends in global type-segregated aerosol optical depth as revealed by MISR satellite observations. Sci. Total Environ. 787, 147543 (2021).
pubmed: 34000526
Logothetis, S.-A. et al. 15-year variability of desert dust optical depth on global and regional scales. Atmos. Chem. Phys. 21, 16499–16529 (2021).
Yousefi, R., Wang, F., Ge, Q. & Shaheen, A. Long-term aerosol optical depth trend over Iran and identification of dominant aerosol types. Sci. Total Environ. 722, 137906 (2020).
pubmed: 32192970
Klingmüller, K., Pozzer, A., Metzger, S., Stenchikov, G. L. & Lelieveld, J. Aerosol optical depth trend over the Middle East. Atmos. Chem. Phys. 16, 5063–5073 (2016).
Reddington, C. L. et al. Air quality and human health improvements from reductions in deforestation-related fire in Brazil. Nat. Geosci. 8, 768–771 (2015).
Aragão, L. E. O. C. et al. 21st Century drought-related fires counteract the decline of Amazon deforestation carbon emissions. Nat. Commun. 9, 536 (2018).
pubmed: 29440640
pmcid: 5811599
Pope, C. A. III, Cropper, M., Coggins, J. & Cohen, A. Health benefits of air pollution abatement policy: Role of the shape of the concentration–response function. J. Air Waste Manag. Assoc. 65, 516–522 (2015).
pubmed: 25947311
Weichenthal, S. et al. How low can you go? Air pollution affects mortality at very low levels. Sci. Adv. 8, eabo3381 (2022).
pubmed: 36170354
pmcid: 9519036
Marshall, J. D., Apte, J. S., Coggins, J. S. & Goodkind, A. L. Blue skies bluer? Environ. Sci. Technol. 49, 13929–13936 (2015).
pubmed: 26535809
van Donkelaar, A., Martin, R. V. & Park, R. J. Estimating ground-level PM2.5 using aerosol optical depth determined from satellite remote sensing. J. Geophys. Res. Atmos. 111, https://doi.org/10.1029/2005JD006996 (2006).
van Donkelaar, A. et al. Global estimates of ambient fine particulate matter concentrations from satellite-based aerosol optical depth: development and application. Environ. Health Perspect. 118, 847–855 (2010).
pubmed: 20519161
pmcid: 2898863
van Donkelaar, A. et al. Global estimates of fine particulate matter using a combined geophysical-statistical method with information from satellites, models, and monitors. Environ. Sci. Technol. 50, 3762–3772 (2016).
pubmed: 26953851
Martin, R. V. et al. No one knows which city has the highest concentration of fine particulate matter. Atmos. Environ. X 3, 100040 (2019).
Marais, E. A. et al. Air quality and health impact of future fossil fuel use for electricity generation and transport in Africa. Environ. Sci. Technol. 53, 13524–13534 (2019).
pubmed: 31647871
World Health Organization. WHO Global Air Quality Guidelines: Particulate Matter (PM2.5 and PM10), Ozone, Nitrogen Dioxide, Sulfur Dioxide And Carbon Monoxide (World Health Organization, 2021).
Brauer, M. et al. Mortality-air pollution associations in low exposure environments (MAPLE): phase 2. Res. Rep. (Health Eff. Inst.) 2022, 1–91 (2022).
pubmed: 36224709
Brunekreef, B. et al. Mortality and morbidity effects of long-term exposure to low-level PM2. 5, bc, NO2, and O3: an analysis of European cohorts in the ELAPSE project. Res. Rep.: Health Eff. Inst. 2021, 1–127 (2021).
pubmed: 36106702
Wang, Y., Wang, J., Wang, Y. & Li, W. Drought impacts on PM2.5 composition and amount over the US during 1988–2018. J. Geophys. Res. Atmos. 127, e2022JD037677 (2022).
Pai, S. J., Carter, T. S., Heald, C. L. & Kroll, J. H. Updated World Health Organization air quality guidelines highlight the importance of non-anthropogenic PM2.5. Environ. Sci. Technol. Lett. 9, 501–506 (2022).
pubmed: 35719860
pmcid: 9202349
Jiang, Y. et al. Extreme emission reduction requirements for china to achieve world health organization global air quality guidelines. Environ. Sci. Technol. 57, 4424–4433 (2023).
pubmed: 36898019
Hammer, M. S. et al. Global estimates and long-term trends of fine particulate matter concentrations (1998–2018). Environ. Sci. Technol. 54, 7879–7890 (2020).
pubmed: 32491847
Hussain, M. & Mahmud, I. pyMannKendall: a python package for non parametric Mann Kendall family of trend tests. J. Open Source Softw. 4, 1556 (2019).
Sen, P. K. Estimates of the regression coefficient based on Kendall’s tau. J. Am. Stat. Assoc. 63, 1379–1389 (1968).
McDuffie, E. et al. GBD-MAPS-Global: Analysis Input Dataset. https://doi.org/10.5281/zenodo.4642700 (2021).
Zheng, P., Barber, R., Sorensen, R. J. D., Murray, C. J. L. & Aravkin, A. Y. Trimmed constrained mixed effects models: formulations and algorithms. J. Comput. Graph. Stat. 30, 544–556 (2021).
Met Office. Cartopy: A Cartographic Python Library With A Matplotlib Interface. (2010).
Hunter, J. D. Matplotlib: a 2D graphics environment. Comput. Sci. Eng. 9, 90–95 (2007).