Pierre Masselot1, Francesco Sera1,2, Rochelle Schneider1,3,4, Haidong Kan5, Éric Lavigne6,7, Massimo Stafoggia8, Aurelio Tobias9,10, Hong Chen11, Richard T Burnett11, Joel Schwartz12, Antonella Zanobetti12, Michelle L Bell13, Bing-Yu Chen14, Yue-Liang Leon Guo14, Martina S Ragettli15, Ana Maria Vicedo-Cabrera16,17, Christofer Åström18, Bertil Forsberg18, Carmen Íñiguez19,20, Rebecca M Garland21,22,23, Noah Scovronick24, Joana Madureira25,26, Baltazar Nunes27,28, César De la Cruz Valencia29, Magali Hurtado Diaz29, Yasushi Honda30,31, Masahiro Hashizume32, Chris Fook Cheng Ng10, Evangelia Samoli33, Klea Katsouyanni33,34, Alexandra Schneider35, Susanne Breitner35,36, Niilo R I Ryti37,38, Jouni J K Jaakkola37,38,39, Marek Maasikmets40, Hans Orru41, Yuming Guo42, Nicolás Valdés Ortega43, Patricia Matus Correa44, Shilu Tong44,45,46,47, Antonio Gasparrini1,3,48. 1. From the Department of Public Health, Environments and Society, London School of Hygiene and Tropical Medicine (LSHTM), 15-17 Tavistock Place, London, WC1H 9SH, United Kingdom. 2. Department of Statistics, Computer Science and Applications "G. Parenti", University of Florence, Florence, Italy. 3. Centre on Climate Change and Planetary Health, London School of Hygiene & Tropical Medicine (LSHTM), Keppel Street, London, WC1E 7HT, United Kingdom. 4. European Centre for Medium-Range Weather Forecast, Reading, United Kingdom. 5. Department of Environmental Health, School of Public Health, Fudan University, Shanghai, China. 6. School of Epidemiology and Public Health, Faculty of Medicine, University of Ottawa, Ottawa, Canada. 7. Air Health Science Division, Health Canada, Ottawa, Canada. 8. Department of Epidemiology, Lazio Regional Health Service/ASL Roma 1, Rome, Italy. 9. Institute of Environmental Assessment and Water Research, Spanish Council for Scientific Research, Barcelona, Spain. 10. School of Tropical Medicine and Global Health, Nagasaki University, Nagasaki, Japan. 11. Health Canada, Ottawa, Canada. 12. Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA. 13. School of the Environment, Yale University, New Haven CT. 14. National Institute of Environmental Health Science, National Health Research Institutes, Zhunan, Taiwan. 15. Swiss Tropical and Public Health Institute, Basel, Switzerland. 16. Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland. 17. Oeschger Center for Climate Change Research, University of Bern, Bern, Switzerland. 18. Department of Public Health and Clinical Medicine, Umeå University, Sweden. 19. Department of Statistics and Computational Research. Universitat de València, València, Spain. 20. Ciberesp, Madrid. Spain. 21. Natural Resources and the Environment Unit, Council for Scientific and Industrial Research, Pretoria 0001, South Africa. 22. Unit for Environmental Sciences and Management, North-West University, Potchefstroom 2520, South Africa. 23. Department of Geography, Geo-informatics and Meteorology, University of Pretoria, Pretoria 0001, South Africa. 24. Gangarosa Department of Environmental Health. Rollins School of Public Health, Emory University, Atlanta. 25. Department of Environmental Health, Instituto Nacional de Saúde Dr Ricardo Jorge, Porto, Portugal. 26. EPIUnit - Instituto de Saúde Pública, Universidade do Porto, Porto, Portugal. 27. Department of Epidemiology, Instituto Nacional de Saúde Dr Ricardo Jorge, Porto, Portugal. 28. Centro de Investigação em Saúde Pública, Escola Nacional de Saúde Pública, Universidade NOVA de Lisboa, Lisboa, Portugal. 29. Department of Environmental Health, National Institute of Public Health, Cuernavaca, Morelos, Mexico. 30. Center for Climate Change Adaptation, National Institute for Environmental Studies, Tsukuba, Japan. 31. Faculty of Health and Sport Sciences, University of Tsukuba, Tsukuba, Japan. 32. Department of Global Health Policy, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan. 33. Department of Hygiene, Epidemiology and Medical Statistics, National and Kapodistrian University of Athens, Greece. 34. School of Population Health and Environmental Sciences, King's College, London, United Kingdom. 35. Institute of Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health (GmbH), Neuherberg, Germany. 36. IBE-Chair of Epidemiology, LMU Munich, Munich, Germany. 37. Center for Environmental and Respiratory Health Research (CERH), University of Oulu, Oulu, Finland. 38. Medical Research Center Oulu (MRC Oulu), Oulu University Hospital and University of Oulu, Oulu, Finland. 39. Finnish Meteorological Institute, Helsinki, Finland. 40. Estonian Environmental Research Centre, Tallinn, Estonia. 41. Department of Family Medicine and Public Health, University of Tartu, Tartu, Estonia. 42. Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia. 43. Department of Public Health, Universidad de los Andes, Santiago, Chile. 44. Shanghai Children's Medical Centre, Shanghai Jiao-Tong University, Shanghai, China. 45. School of Public Health and Institute of Environment and Human Health, Anhui Medical University, Hefei, China. 46. Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China. 47. School of Public Health and Social Work, Queensland University of Technology, Brisbane, Australia. 48. Centre for Statistical Methodology, London School of Hygiene & Tropical Medicine (LSHTM), Keppel Street, London, WC1E 7HT, United Kingdom.
Abstract
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.
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.
Authors: Jun Meng; Randall V Martin; Chi Li; Aaron van Donkelaar; Zitely A Tzompa-Sosa; Xu Yue; Jun-Wei Xu; Crystal L Weagle; Richard T Burnett Journal: Environ Sci Technol Date: 2019-08-21 Impact factor: 9.028
Authors: Roger D Peng; Michelle L Bell; Alison S Geyh; Aidan McDermott; Scott L Zeger; Jonathan M Samet; Francesca Dominici Journal: Environ Health Perspect Date: 2009-02-11 Impact factor: 9.031