Differential Mortality Risks Associated With PM2.5 Components: A Multi-Country, Multi-City Study.
Journal
Epidemiology (Cambridge, Mass.)
ISSN: 1531-5487
Titre abrégé: Epidemiology
Pays: United States
ID NLM: 9009644
Informations de publication
Date de publication:
01 03 2022
01 03 2022
Historique:
pubmed:
16
12
2021
medline:
19
3
2022
entrez:
15
12
2021
Statut:
ppublish
Résumé
The association between fine particulate matter (PM2.5) and mortality widely differs between as well as within countries. Differences in PM2.5 composition can play a role in modifying the effect estimates, but there is little evidence about which components have higher impacts on mortality. We applied a 2-stage analysis on data collected from 210 locations in 16 countries. In the first stage, we estimated location-specific relative risks (RR) for mortality associated with daily total PM2.5 through time series regression analysis. We then pooled these estimates in a meta-regression model that included city-specific logratio-transformed proportions of seven PM2.5 components as well as meta-predictors derived from city-specific socio-economic and environmental indicators. We found associations between RR and several PM2.5 components. Increasing the ammonium (NH4+) proportion from 1% to 22%, while keeping a relative average proportion of other components, increased the RR from 1.0063 (95% confidence interval [95% CI] = 1.0030, 1.0097) to 1.0102 (95% CI = 1.0070, 1.0135). Conversely, an increase in nitrate (NO3-) from 1% to 71% resulted in a reduced RR, from 1.0100 (95% CI = 1.0067, 1.0133) to 1.0037 (95% CI = 0.9998, 1.0077). Differences in composition explained a substantial part of the heterogeneity in PM2.5 risk. These findings contribute to the identification of more hazardous emission sources. Further work is needed to understand the health impacts of PM2.5 components and sources given the overlapping sources and correlations among many components.
Sections du résumé
BACKGROUND
The association between fine particulate matter (PM2.5) and mortality widely differs between as well as within countries. Differences in PM2.5 composition can play a role in modifying the effect estimates, but there is little evidence about which components have higher impacts on mortality.
METHODS
We applied a 2-stage analysis on data collected from 210 locations in 16 countries. In the first stage, we estimated location-specific relative risks (RR) for mortality associated with daily total PM2.5 through time series regression analysis. We then pooled these estimates in a meta-regression model that included city-specific logratio-transformed proportions of seven PM2.5 components as well as meta-predictors derived from city-specific socio-economic and environmental indicators.
RESULTS
We found associations between RR and several PM2.5 components. Increasing the ammonium (NH4+) proportion from 1% to 22%, while keeping a relative average proportion of other components, increased the RR from 1.0063 (95% confidence interval [95% CI] = 1.0030, 1.0097) to 1.0102 (95% CI = 1.0070, 1.0135). Conversely, an increase in nitrate (NO3-) from 1% to 71% resulted in a reduced RR, from 1.0100 (95% CI = 1.0067, 1.0133) to 1.0037 (95% CI = 0.9998, 1.0077). Differences in composition explained a substantial part of the heterogeneity in PM2.5 risk.
CONCLUSIONS
These findings contribute to the identification of more hazardous emission sources. Further work is needed to understand the health impacts of PM2.5 components and sources given the overlapping sources and correlations among many components.
Identifiants
pubmed: 34907973
doi: 10.1097/EDE.0000000000001455
pii: 00001648-202203000-00003
pmc: PMC7612311
mid: EMS140504
doi:
Substances chimiques
Air Pollutants
0
Nitrates
0
Particulate Matter
0
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
167-175Subventions
Organisme : Medical Research Council
ID : MR/M022625/1
Pays : United Kingdom
Organisme : Medical Research Council
ID : MR/R013349/1
Pays : United Kingdom
Organisme : NIEHS NIH HHS
ID : P30 ES019776
Pays : United States
Organisme : NCATS NIH HHS
ID : UL1 TR001863
Pays : United States
Informations de copyright
Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.
Déclaration de conflit d'intérêts
The authors report no conflicts of interest.
Références
Stanaway JD, Afshin A, Gakidou E, et al. Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. The Lancet. 2018;392:1923–1994.
Atkinson RW, Kang S, Anderson HR, Mills IC, Walton HA. Epidemiological time series studies of PM2.5 and daily mortality and hospital admissions: a systematic review and meta-analysis. Thorax. 2014;69:660–665.
Rückerl R, Schneider A, Breitner S, Cyrys J, Peters A. Health effects of particulate air pollution: a review of epidemiological evidence. Inhal Toxicol. 2011;23:555–592.
Liu C, Chen R, Sera F, et al. Ambient particulate air pollution and daily mortality in 652 Cities. N Engl J Med. 2019;381:705–715.
Chen R, Yin P, Meng X, et al. Fine particulate air pollution and daily mortality. A Nationwide Analysis in 272 Chinese Cities. Am J Respir Crit Care Med. 2017;196:73–81.
Franklin M, Zeka A, Schwartz J. Association between PM2.5 and all-cause and specific-cause mortality in 27 US communities. J Expo Sci Environ Epidemiol. 2007;17:279–287.
Adams K, Greenbaum DS, Shaikh R, van Erp AM, Russell AG. Particulate matter components, sources, and health: systematic approaches to testing effects. J Air Waste Manag Assoc. 2015;65:544–558.
Kelly FJ, Fussell JC. Size, source and chemical composition as determinants of toxicity attributable to ambient particulate matter. Atmos Environ. 2012;60:504–526.
McDuffie EE, Smith SJ, O’Rourke P, et al. A global anthropogenic emission inventory of atmospheric pollutants from sector- and fuel-specific sources (1970–2017): an application of the Community Emissions Data System (CEDS). Earth Syst Sci Data Discuss. 2020;12:3413–3442.
van Donkelaar A, Martin RV, Li C, Burnett RT. Regional estimates of chemical composition of fine particulate matter using a combined geoscience-statistical method with information from satellites, models, and monitors. Environ Sci Technol. 2019;53:2595–2611.
Park RJ, Jacob DJ, Field BD, Yantosca RM, Chin M. Natural and transboundary pollution influences on sulfate-nitrate-ammonium aerosols in the United States: Implications for policy. J Geophys Res Atmospheres. 2004;109(D15):D15204.
Meng J, Martin RV, Li C, et al. Source contributions to ambient fine particulate matter for Canada. Environ Sci Technol. 2019;53:10269–10278.
Bond TC, Streets DG, Yarber KF, Nelson SM, Woo JH, Klimont Z. A technology-based global inventory of black and organic carbon emissions from combustion. J Geophys Res-Atmospheres. 2004;109(D14):D14203.
Hashizume M, Kim Y, Ng CFS, et al. Health effects of Asian Dust: a systematic review and meta-analysis. Environ Health Perspect. 2020;128:66001.
Stafoggia M, Zauli-Sajani S, Pey J, et al.; MED-PARTICLES Study Group. Desert dust outbreaks in Southern Europe: contribution to daily PM 10 concentrations and short-term associations with mortality and hospital admissions. Environ Health Perspect. 2016;124:413–419.
Chen S, Zhang X, Lin J, et al. Fugitive road dust PM2.5 emissions and their potential health impacts. Environ Sci Technol. 2019;53:8455–8465.
Philip S, Martin RV, Snider G, et al. Anthropogenic fugitive, combustion and industrial dust is a significant, underrepresented fine particulate matter source in global atmospheric models. Environ Res Lett. 2017;12:044018.
Janssen NA, Hoek G, Simic-Lawson M, et al. Black carbon as an additional indicator of the adverse health effects of airborne particles compared with PM10 and PM2.5. Environ Health Perspect. 2011;119:1691–1699.
Luben TJ, Nichols JL, Dutton SJ, et al. A systematic review of cardiovascular emergency department visits, hospital admissions and mortality associated with ambient black carbon. Environ Int. 2017;107:154–162.
Bell ML, Ebisu K, Peng RD, Samet JM, Dominici F. Hospital admissions and chemical composition of fine particle air pollution. Am J Respir Crit Care Med. 2009;179:1115–1120.
Franklin M, Koutrakis P, Schwartz P. The role of particle composition on the association between PM2.5 and mortality. Epidemiology. 2008;19:680–689.
Yang Y, Ruan Z, Wang X, et al. Short-term and long-term exposures to fine particulate matter constituents and health: a systematic review and meta-analysis. Environ Pollut. 2019;247:874–882.
R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing; 2020. Available at: http://www.R-project.org/
Gasparrini A. Distributed lag linear and non-linear models in R: the package dlnm. J Stat Softw. 2011;43:1–20.
Sera F, Armstrong B, Blangiardo M, Gasparrini A. An extended mixed-effects framework for meta-analysis. Stat Med. 2019;38:5429–5444.
van den Boogaart KG, Tolosana-Delgado R. “compositions”: a unified R package to analyze compositional data. Comput Geosci. 2008;34:320–338.
Palarea-Albaladejo J, Martín-Fernández JA. zCompositions — R package for multivariate imputation of left-censored data under a compositional approach. Chemom Intell Lab Syst. 2015;143:85–96.
Aitchison J. A New approach to null correlations of proportions. Math Geol. 1981;13:175–189.
Aitchison J. The statistical analysis of compositional data. J R Stat Soc Ser B Methodol. 1982;44:139–160.
Aitchison J. Principal component analysis of compositional data. Biometrika. 1983;70):57–65.
Aitchison J, Bacon-Shone J. Log contrast models for experiments with mixtures. Biometrika. 1984;71:323–330.
Aitchison J. The Statistical Analysis of Compositional Data. Chapman & Hall, Ltd.; 1986.
Gasparrini A, Armstrong B, Kenward MG. Multivariate meta-analysis for non-linear and other multi-parameter associations. Stat Med. 2012;31:3821–3839.
Pope CA, Coleman N, Pond ZA, Burnett RT. Fine particulate air pollution and human mortality: 25+years of cohort studies. Environ Res. 2020;183:108924.
Suissa S. Relative excess risk: an alternative measure of comparative risk. Am J Epidemiol. 1999;150:279–282.
Higgins JP, Thompson SG. Quantifying heterogeneity in a meta-analysis. Stat Med. 2002;21:1539–1558.
Harrell F. Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis. 2nd ed. Springer-Verlag; 2015.
Chen H, Zhang Z, van Donkelaar A, et al. Understanding the joint impacts of fine particulate matter concentration and composition on the incidence and mortality of cardiovascular disease: a component-adjusted approach. Environ Sci Technol. 2020;54:4388–4399.
Kioumourtzoglou MA, Austin E, Koutrakis P, Dominici F, Schwartz J, Zanobetti A. PM2.5 and survival among older adults: effect modification by particulate composition. Epidemiology. 2015;26:321–327.
Peng RD, Bell ML, Geyh AS, et al. Emergency admissions for cardiovascular and respiratory diseases and the chemical composition of fine particle air pollution. Environ Health Perspect. 2009;117:957–963.
Wang M, Beelen R, Stafoggia M, et al. Long-term exposure to elemental constituents of particulate matter and cardiovascular mortality in 19 European cohorts: results from the ESCAPE and TRANSPHORM projects. Environ Int. 2014;66:97–106.
Crouse DL, Philip S, van Donkelaar A, et al. A New Method to Jointly Estimate the Mortality Risk of Long-Term Exposure to Fine Particulate Matter and its Components. Sci Rep. 2016;6:18916.
Huang W, Cao J, Tao Y, et al. Seasonal variation of chemical species associated with short-term mortality effects of PM(2.5) in Xi’an, a Central City in China. Am J Epidemiol. 2012;175:556–566.
Lin H, Tao J, Du Y, et al. Particle size and chemical constituents of ambient particulate pollution associated with cardiovascular mortality in Guangzhou, China. Environ Pollut. 2016;208(Pt B):758–766.
Liu S, Zhang K. Fine particulate matter components and mortality in Greater Houston: did the risk reduce from 2000 to 2011? Sci Total Environ. 2015;538:162–168.
Son JY, Lee JT, Kim KH, Jung K, Bell ML. Characterization of fine particulate matter and associations between particulate chemical constituents and mortality in Seoul, Korea. Environ Health Perspect. 2012;120:872–878.
Lelieveld J, Evans JS, Fnais M, Giannadaki D, Pozzer A. The contribution of outdoor air pollution sources to premature mortality on a global scale. Nature. 2015;525:367–371.
Xu J, Hu W, Liang D, Gao P. Photochemical impacts on the toxicity of PM2.5. Crit Rev Environ Sci Technol. 2020;0:1–27.
Niu Y, Chen R, Xia Y, et al. Fine particulate matter constituents and stress hormones in the hypothalamus-pituitary-adrenal axis. Environ Int. 2018;119:186–192.
Cassee FR, Héroux ME, Gerlofs-Nijland ME, Kelly FJ. Particulate matter beyond mass: recent health evidence on the role of fractions, chemical constituents and sources of emission. Inhal Toxicol. 2013;25:802–812.
Air Quality Expert Group. Mitigation of United Kingdom PM2.5 Concentrations. Department for Environment, Food and Rural Affairs; 2013:49. Available at: https://uk-air.defra.gov.uk/assets/documents/reports/cat11/1508060903_DEF-PB14161_Mitigation_of_UK_PM25.pdf . Accessed 29 October 2020.
Pinder RW, Adams PJ, Pandis SN. Ammonia emission controls as a cost-effective strategy for reducing atmospheric particulate matter in the Eastern United States. Environ Sci Technol. 2007;41:380–386.
Wu Y, Gu B, Erisman JW, et al. PM2.5 pollution is substantially affected by ammonia emissions in China. Environ Pollut. 2016;218:86–94.
Hime NJ, Marks GB, Cowie CT. A comparison of the health effects of ambient particulate matter air pollution from five emission sources. Int J Environ Res Public Health. 2018;15:E1206.
Park M, Joo HS, Lee K, et al. Differential toxicities of fine particulate matters from various sources. Sci Rep. 2018;8:17007.
Pearson K. Mathematical contributions to the theory of evolution.—On a form of spurious correlation which may arise when indices are used in the measurement of organs. Proc R Soc Lond. 1897;60:489–498.