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Premature mortality related to United States cross-state air pollution

Nature volume 578, pages 261–265 (2020)Cite this article

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Abstract

Outdoor air pollution adversely affects human health and is estimated to be responsible for five to ten per cent of the total annual premature mortality in the contiguous United States1,2,3. Combustion emissions from a variety of sources, such as power generation or road traffic, make a large contribution to harmful air pollutants such as ozone and fine particulate matter (PM2.5)4. Efforts to mitigate air pollution have focused mainly on the relationship between local emission sources and local air quality2. Air quality can also be affected by distant emission sources, however, including emissions from neighbouring federal states5,6. This cross-state exchange of pollution poses additional regulatory challenges. Here we quantify the exchange of air pollution among the contiguous United States, and assess its impact on premature mortality that is linked to increased human exposure to PM2.5 and ozone from seven emission sectors for 2005 to 2018. On average, we find that 41 to 53 per cent of air-quality-related premature mortality resulting from a state’s emissions occurs outside that state. We also find variations in the cross-state contributions of different emission sectors and chemical species to premature mortality, and changes in these variations over time. Emissions from electric power generation have the greatest cross-state impacts as a fraction of their total impacts, whereas commercial/residential emissions have the smallest. However, reductions in emissions from electric power generation since 2005 have meant that, by 2018, cross-state premature mortality associated with the commercial/residential sector was twice that associated with power generation. In terms of the chemical species emitted, nitrogen oxides and sulfur dioxide emissions caused the most cross-state premature deaths in 2005, but by 2018 primary PM2.5 emissions led to cross-state premature deaths equal to three times those associated with sulfur dioxide emissions. These reported shifts in emission sectors and emission species that contribute to premature mortality may help to guide improvements to air quality in the contiguous United States.

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Fig. 1: Early-death source–receptor matrices for 2011.
Fig. 2: Total annual early deaths caused per 10,000 people for 2005, 2011 and 2018.
Fig. 3: Total annual early deaths attributable to emission sector, emission species and in total.

Data availability

The cross-state source–receptor matrices generated and analysed here, together with sector definitions, are available in the 4TU.ResearchData repository at https://doi.org/10.4121/uuid:edfc5304-39ed-4556-a95a-f8b3313f7cfc.

Code availability

The atmospheric modelling code used is publicly available; instructions for download are given at http://wiki.seas.harvard.edu/geos-chem/index.php/GEOS-Chem_Adjoint.

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Acknowledgements

We thank the EPA and K. Travis (Harvard) for providing assistance with the NEI datasets. This publication was made possible by US EPA grant RD-835872-01. Its contents are solely the responsibility of the grantee and do not necessarily represent the official views of the USEPA. Further, USEPA does not endorse the purchase of any commercial products or services mentioned in the publication. I.C.D. was additionally funded through the Massachusetts Institute of Technology (MIT) Martin Family Fellowship for Sustainability and the MIT George and Marie Vergottis Fellowship. We also acknowledge support by the VoLo Foundation.

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Authors and Affiliations

  1. Laboratory for Aviation and the Environment, Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA, USA

    Irene C. Dedoussi, Sebastian D. Eastham & Steven R. H. Barrett

  2. Section Aircraft Noise and Climate Effects, Faculty of Aerospace Engineering, Delft University of Technology, Delft, The Netherlands

    Irene C. Dedoussi

  3. Joint Program on the Science and Policy of Global Change, Massachusetts Institute of Technology, Cambridge, MA, USA

    Sebastian D. Eastham, Erwan Monier & Steven R. H. Barrett

  4. Department of Land, Air and Water Resources, University of California, Davis, Davis, CA, USA

    Erwan Monier

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Contributions

I.C.D. and S.R.H.B. planned the research. I.C.D. performed the emissions modelling and the air quality modelling PM2.5 simulations. S.D.E. and E.M. performed the air quality modelling ozone simulations. I.C.D. and S.D.E. performed results analysis. I.C.D. drafted the manuscript with the help of S.D.E. and S.R.H.B. All authors provided feedback on the manuscript.

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Correspondence to Steven R. H. Barrett.

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Peer review information Nature thanks Marianthi-Anna Kioumourtzoglou, Enrico Pisoni, Andrea Pozzer and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Fig. 1 Source–receptor matrix showing total impacts in 2011 for the contiguous US.

‘By each state’ indicates sources; ‘in each state’ indicates receptors. The matrix is annotated with state abbreviations and their regional grouping.

Extended Data Fig. 2 Annual early-death source–receptor matrices for 2005, 2011 and 2018 for the contiguous US.

Each matrix comprises 48 × 48 states. a (i), The total source–receptor matrix for 2011. a (ii), Its breakdown to PM2.5-attributable and ozone-attributable impacts for all three years. b, Source–receptor early-death attribution to emission sectors (i) and emission species that lead to the formation of PM2.5 and/or ozone (ii). States are grouped in regions defined by the Bureau of Economic Analysis20 (labelled in a) and ordered from west (left) to east (right). The ordering of individual states is presented in Extended Data Fig. 1. Boxed percentages represent the fraction of impacts that occur out of the state that caused the corresponding emissions. We note that to obtain these summarized source–receptor matrices using conventional modelling approaches (‘forward difference simulations’) would have required around 1,300 simulations.

Extended Data Fig. 3 Origins of New York annual early deaths, for 2005, 2011 and 2018, for five sectors and in total.

Each state is coloured according to the annual early deaths that emissions from that state cause in the state of New York, for each sector–year combination. The total early deaths occurring in New York (that is, the sum of all states’ values) for each sector–year combination is displayed at the bottom left of each panel.

Extended Data Fig. 4 Origins of North Carolina annual early deaths, for 2005, 2011 and 2018, for five sectors and in total.

Each state is coloured according to the annual early deaths that emissions from that state cause in the state of North Carolina, for each sector–year combination. The total early deaths occurring in North Carolina (the sum of all states’ values) for each sector–year combination is displayed at the bottom left of each panel.

Extended Data Fig. 5 Receptors of annual early deaths due to emissions in Indiana for 2005, 2011 and 2018, for five sectors and in total.

Each state is coloured according to the annual early deaths that occur in that state because of emissions in Indiana, for each sector–year combination. The total early deaths caused by Indiana emissions (that is, the sum of all states’ values) for each sector–year combination is displayed at the bottom left of each panel.

Extended Data Fig. 6 Changes in the response of surface-population-weighted PM2.5 and ozone concentrations to US emissions.

Data points show the results of a series of forward simulations, in which the input conditions of the simulation (the total US anthropogenic emissions of all species) are reduced, joined by a cubic spline fit. The ‘average sensitivity’ lines indicate the gradient implied when impacts due to all sectors combined are calculated—that is, when the effects of atmospheric nonlinearity are taken into account—and thus the total results are scaled to match this. The ‘marginal sensitivity’ lines indicate the gradient of the response obtained by our GEOS-Chem adjoint simulation, and are used for calculations of individual sector and species impacts (where individual perturbations are of smaller size). The difference between the zero intercept of the two lines constitutes the ‘interaction’ effect. All values are population-weighted means for 2011.

Extended Data Table 1 Primary PM2.5, NOx and SOx emissions totals for 2005, 2011 and 2018
Full size table
Extended Data Table 2 Five states with the greatest reduction in annual early deaths between 2005 and 2018
Full size table
Extended Data Table 3 Early deaths attributable to each sector and species (that lead to PM2.5 and/or ozone formation) for 2005, 2011 and 2018
Full size table
Extended Data Table 4 Alternative CRF application to 2011 early deaths for all sectors, for PM2.5 and ozone
Full size table

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Dedoussi, I.C., Eastham, S.D., Monier, E. et al. Premature mortality related to United States cross-state air pollution. Nature 578, 261–265 (2020). https://doi.org/10.1038/s41586-020-1983-8

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