Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Air pollution exposure disparities across US population and income groups

Abstract

Air pollution contributes to the global burden of disease, with ambient exposure to fine particulate matter of diameters smaller than 2.5 μm (PM2.5) being identified as the fifth-ranking risk factor for mortality globally1. Racial/ethnic minorities and lower-income groups in the USA are at a higher risk of death from exposure to PM2.5 than are other population/income groups2,3,4,5. Moreover, disparities in exposure to air pollution among population and income groups are known to exist6,7,8,9,10,11,12,13,14,15,16,17. Here we develop a data platform that links demographic data (from the US Census Bureau and American Community Survey) and PM2.5 data18 across the USA. We analyse the data at the tabulation area level of US zip codes (N is approximately 32,000) between 2000 and 2016. We show that areas with higher-than-average white and Native American populations have been consistently exposed to average PM2.5 levels that are lower than areas with higher-than-average Black, Asian and Hispanic or Latino populations. Moreover, areas with low-income populations have been consistently exposed to higher average PM2.5 levels than areas with high-income groups for the years 2004–2016. Furthermore, disparities in exposure relative to safety standards set by the US Environmental Protection Agency19 and the World Health Organization20 have been increasing over time. Our findings suggest that more-targeted PM2.5 reductions are necessary to provide all people with a similar degree of protection from environmental hazards. Our study is observational and cannot provide insight into the drivers of the identified disparities.

This is a preview of subscription content

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Fig. 1: Average PM2.5 concentration in 2000 and 2016 across ZCTAs in which Black or white populations are overrepresented.
Fig. 2: Average PM2.5 concentration in 2000 and 2016 across low- and high-income ZCTAs.
Fig. 3: US ZCTAs with average PM2.5 concentrations of more than 8 μg m−3 for Black and white populations in 2000 and 2016.
Fig. 4: Relative disparities in exposure to PM2.5 among racial/ethnic groups for 2000–2016.

Data availability

Data are available in the following GitHub repositories: https://github.com/NSAPH/National-Causal-Analysis/tree/master/Confounders/census and https://github.com/xiaodan-zhou/pm25_and_disparity.

Code availability

Code is available in the following GitHub repository: https://github.com/xiaodan-zhou/pm25_and_disparity.

References

  1. 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).

    Article  Google Scholar 

  2. Di, Q. et al. Air pollution and mortality in the Medicare population. N. Engl. J. Med. 376, 2513–2522 (2017).

    CAS  Article  Google Scholar 

  3. Bell, M. L., Zanobetti, A. & Dominici, F. Evidence on vulnerability and susceptibility to health risks associated with short-term exposure to particulate matter: a systematic review and meta-analysis. Am. J. Epidemiol. 178, 865–876 (2013).

    Article  Google Scholar 

  4. Wang, Y. et al. Long-term exposure to PM2. 5 and mortality among older adults in the southeastern US. Epidemiology 28, 207 (2017).

    Article  Google Scholar 

  5. Kioumourtzoglou, M.-A., Schwartz, J., James, P., Dominici, F. & Zanobetti, A. PM2. 5 and mortality in 207 US cities: modification by temperature and city characteristics. Epidemiology 27, 221 (2016).

    PubMed  PubMed Central  Google Scholar 

  6. Bell, M. L. & Ebisu, K. Environmental inequality in exposures to airborne particulate matter components in the United States. Environ. Health Perspect. 120, 1699–1704 (2012).

    CAS  Article  Google Scholar 

  7. Rosofsky, A., Levy, J. I., Zanobetti, A., Janulewicz, P. & Fabian, M. P. Temporal trends in air pollution exposure inequality in Massachusetts. Environ. Res. 161, 76–86 (2018).

    CAS  Article  Google Scholar 

  8. Mikati, I., Benson, A. F., Luben, T. J., Sacks, J. D. & Richmond-Bryant, J. Disparities in distribution of particulate matter emission sources by race and poverty status. Am. J. Public Health 108, 480–485 (2018).

    Article  Google Scholar 

  9. Miranda, M. L., Edwards, S. E., Keating, M. H. & Paul, C. J. Making the environmental justice grade: the relative burden of air pollution exposure in the United States. Int. J. Environ. Res. Public Health 8, 1755–1771 (2011).

    Article  Google Scholar 

  10. Mohai, P., Pellow, D. & Roberts, J. T. Environmental justice. Annu. Rev. Environ. Resour. 34, 405–430 (2009).

    Article  Google Scholar 

  11. Agyeman, J., Schlosberg, D., Craven, L. & Matthews, C. Trends and directions in environmental justice: from inequity to everyday life, community, and just sustainabilities. Annu. Rev. Environ. Resour. 41, 321–340 (2016).

    Article  Google Scholar 

  12. Banzhaf, S., Ma, L. & Timmins, C. Environmental justice: the economics of race, place, and pollution. J. Econ. Perspect. 33, 185–208 (2019).

    Article  Google Scholar 

  13. Kelly, J. T. et al. Examining PM2.5 concentrations and exposure using multiple models. Environ. Res. 196, 110432 (2020).

    Article  Google Scholar 

  14. Fann, N., Coffman, E., Timin, B. & Kelly, J. T. The estimated change in the level and distribution of PM2.5-attributable health impacts in the United States: 2005–2014. Environ. Res. 167, 506–514 (2018).

    CAS  Article  Google Scholar 

  15. Tessum, C. W. et al. PM2.5 polluters disproportionately and systemically affect people of color in the United States. Sci. Adv. 7, (2021).

  16. Harper, S. et al. Using inequality measures to incorporate environmental justice into regulatory analyses. Int. J. Environ. Res. Public Health 10, 4039–4059 (2013).

    Article  Google Scholar 

  17. Colmer, J., Hardman, I., Shimshack, J. & Voorheis, J. Disparities in PM2.5 air pollution in the United States. Science 369, 575–578 (2020).

    ADS  CAS  Article  Google Scholar 

  18. 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).

    ADS  CAS  Article  Google Scholar 

  19. US Environmental Protection Agency. Process of reviewing the National Ambient Air Quality Standards. https://www.epa.gov/criteria-air-pollutants/process-reviewing-national-ambient-air-quality-standards

  20. World Health Organization. Air Quality Guidelines. Global Update 2005. Particulate Matter, Ozone, Nitrogen Dioxide, and Sulfur Dioxide (World Health Organization, 2005).

  21. Dominici, F. et al. Fine particulate air pollution and hospital admission for cardiovascular and respiratory diseases. J. Am. Med. Assoc. 295, 1127–1134 (2006).

    CAS  Article  Google Scholar 

  22. Bell, M. L. et al. Seasonal and regional short-term effects of fine particles on hospital admissions in 202 US counties, 1999–2005. Am. J. Epidemiol. 168, 1301–1310 (2008).

    Article  Google Scholar 

  23. Kloog, I. et al. Short term effects of particle exposure on hospital admissions in the mid-atlantic states: a population estimate. PLoS One 9, (2014).

  24. Bravo, M. A. et al. Airborne fine particles and risk of hospital admissions for understudied populations: effects by urbanicity and short-term cumulative exposures in 708 US counties. Environ. Health Perspect. 125, 594–601 (2017).

    CAS  Article  Google Scholar 

  25. Dominici, F., McDermott, A., Zeger, S. L. & Samet, J. M. National maps of the effects of particulate matter on mortality: exploring geographical variation. Environ. Health Perspect. 111, 39–44 (2003).

    Article  Google Scholar 

  26. Beelen, R. et al. Effects of long-term exposure to air pollution on natural-cause mortality: an analysis of 22 European cohorts within the multicentre escape project. Lancet 383, 785–795 (2014).

    CAS  Article  Google Scholar 

  27. Crouse, D. L. et al. Ambient PM2.5, O3, and NO2 exposures and associations with mortality over 16 years of follow-up in the Canadian census health and environment cohort (CanCHEC). Environ. Health Perspect. 123, 1180–1186 (2015).

    CAS  Article  Google Scholar 

  28. Makar, M. et al. Estimating the causal effect of fine particulate matter levels on death and hospitalization: are levels below the safety standards harmful? Epidemiology 28, 627 (2017).

    Article  Google Scholar 

  29. Di, Q. et al. Association of short-term exposure to air pollution with mortality in older adults. J. Am. Med. Assoc. 318, 2446–2456 (2017).

    CAS  Article  Google Scholar 

  30. Wu, X., Braun, D., Schwartz, J., Kioumourtzoglou, M. & Dominici, F. Evaluating the impact of long-term exposure to fine particulate matter on mortality among the elderly. Sci. Adv. 6, eaba5692 (2020).

    ADS  CAS  Article  Google Scholar 

  31. Liu, C. et al. Ambient particulate air pollution and daily mortality in 652 cities. N. Engl. J. Med. 381, 705–715 (2019).

    CAS  Article  Google Scholar 

  32. Samoli, E. et al. Estimating the exposure–response relationships between particulate matter and mortality within the aphea multicity project. Environ. Health Perspect. 113, 88–95 (2005).

    CAS  Article  Google Scholar 

  33. Samet, J. M., Dominici, F., Curriero, F. C., Coursac, I. & Zeger, S. L. Fine particulate air pollution and mortality in 20 us cities, 1987–1994. N. Engl. J. Med. 343, 1742–1749 (2000).

    CAS  Article  Google Scholar 

  34. Shah, A. S. et al. Global association of air pollution and heart failure: a systematic review and meta-analysis. Lancet 382, 1039–1048 (2013).

    CAS  Article  Google Scholar 

  35. American Psychological Association. Racial and ethnic identity. https://apastyle.apa.org/style-grammar-guidelines/bias-free-language/racial-ethnic-minorities

  36. EPA proposes to retain NAAQS for particulate matter. https://www.epa.gov/newsreleases/epa-proposes-retain-naaqs-particulate-matter

  37. Clark, L. P., Millet, D. B. & Marshall, J. D. National patterns in environmental injustice and inequality: outdoor NO2 air pollution in the United States. PLoS One 9, e94431 (2014).

    ADS  Article  Google Scholar 

  38. Levy, J. I., Chemerynski, S. M. & Tuchmann, J. L. Incorporating concepts of inequality and inequity into health benefits analysis. Int. J. Equity Health 5, 2 (2006).

    Article  Google Scholar 

  39. Lambert, P. J., Millimet, D. L. & Slottje, D. Inequality aversion and the natural rate of subjective inequality. J. Public Econ. 87, 1061–1090 (2003).

    Article  Google Scholar 

  40. Currie, J., Voorheis, J. & Walker, R. What Caused Racial Disparities in Particulate Exposure to Fall? New Evidence From the Clean Air Act and Satellite-Based Measures of Air Quality. Technical report (National Bureau of Economic Research, 2020).

  41. Auffhammer, M., Bento, A. M. & Lowe, S. E. Measuring the effects of the clean air act amendments on ambient PM10 concentrations: the critical importance of a spatially disaggregated analysis. J. Environ. Econ. Manage. 58, 15–26 (2009).

    Article  Google Scholar 

  42. Grainger, C. A. The distributional effects of pollution regulations: do renters fully pay for cleaner air? J. Public Econ. 96, 840–852 (2012).

    Article  Google Scholar 

  43. Di, Q. et al. Assessing PM2.5 exposures with high spatiotemporal resolution across the continental United States. Environ. Sci. Technol. 50, 4712–4721 (2016).

    ADS  CAS  Article  Google Scholar 

  44. Di, Q. et al. An ensemble-based model of PM2.5 concentration across the contiguous United States with high spatiotemporal resolution. Environ. Int. 130, 104909 (2019).

    CAS  Article  Google Scholar 

Download references

Acknowledgements

We thank R. Martin and J. D. Schwartz for providing the air-pollution data; B. Sabbath for cleaning and preparing the data sets; and L. Bennett for comments and discussions. We also thank J. Kodros for his comments on an earlier draft. This work was supported financially by grants from the Health Effects Institute (4953- RFA14-3/16-4), the National Institutes of Health (DP2MD012722, P50MD010428), the National Institutes of Health and Yale University (R01MD012769), the National Institutes of Health and National Institute of Environmental Health Sciences (R01 ES028033, R01ES026217, R01AG066793-01, R01ES029950, R01ES028033-S1), the National Institutes of Health and Columbia University (1R01ES030616), the Environmental Protection Agency (83587201-0), The Climate Change Solutions Fund, and a Harvard Star Friedman Award.

Author information

Authors and Affiliations

Authors

Contributions

A.J., S.V. and F.D. contributed to the study design. A.J. led the research, with support from X.Z. and supervision from F.D. Maps and videos were prepared by X.Z., J.L. and T.-H.L. A.J. drafted the manuscript, with support from L.K., S.V. and F.D. All authors read and approved the final manuscript for submission.

Corresponding authors

Correspondence to Abdulrahman Jbaily or Francesca Dominici.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review information

Nature thanks Corbett Grainger, Jonathan Levy, Arden Pope III and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Fig. 1 Summary PM2.5 metrics across racial/ethnic and income groups.

a, The population-weighted average of PM2.5 decreased by 40.4% from the year 2000 to 2016. b, Population-weighted average PM2.5 concentration across the different racial/ethnic communities for 2000 to 2016, showing that Black and Native American populations live in the most- and least-polluted areas, respectively. c, Population-weighted average PM2.5 concentration across racial/ethnic communities as a function of ZCTA racial/ethnic population (%) for 2016. For example, when the racial/ethnic population percentage is equal to 0.2, the red curve includes every ZCTA where the Black population is 20% or more, and the blue curve includes every ZCTA where the white population is 20% or more. As a ZCTA’s Black and Hispanic or Latino populations increase, the PM2.5 concentration levels increase. The opposite effect is seen for the white and Native American communities. d, The population-weighted average PM2.5 concentration across the income groups reveals that the low-income group has been exposed to only slightly higher PM2.5 levels than the high-income groups since 2004. e, Population-weighted average PM2.5 concentrations across the different racial/ethnic communities that are in the low-income group, for 2000–2016. f, Population-weighted average PM2.5 concentrations across the different racial/ethnic communities that are in the high-income group, for 2000–2016. Panels e, f show similar differences in average PM2.5 concentrations across the racial/ethnic groups as seen in b.

Extended Data Fig. 2 Average PM2.5 concentrations across the US.

a, Distribution of PM2.5 in 2000. b, Distribution of PM2.5 in 2016. Supplementary Video 1 shows the change in the distribution of PM2.5 concentration levels in the US from 2000 to 2016. Note that Hawaii and Alaska are not shown. Imagery provided courtesy of Esri, HERE, Garmin, FAO, NOAA, USGS, ©OpenStreetMap contributors, and the GIS User Community.

Extended Data Fig. 3 Average PM2.5 concentrations across ZCTAs in which different racial/ethnic groups are overrepresented.

a, Distribution of PM2.5 across five different maps for 2000, each showing the ZCTAs in which one race/ethnicity group is overrepresented. b, Distribution of PM2.5 across five different maps for 2016, each showing the ZCTAs in which one race/ethnicity group is overrepresented. Supplementary Videos 2, 3 show the change in the distribution of PM2.5 concentrations across the five maps from 2000 to 2016. Note that Hawaii and Alaska are not shown. Imagery provided courtesy of Esri, HERE, Garmin, FAO, NOAA, USGS, ©OpenStreetMap contributors, and the GIS User Community.

Extended Data Fig. 4 Distribution of racial/ethnic populations above a PM2.5 threshold of 8 μg m−3 for 2000 and 2016.

a, US ZCTAs for each race/ethnicity are ranked on the basis of the ratio of the race/ethnicity population to the total ZCTA population. Dark blue indicates fractions close to 1 (ZCTAs in which the corresponding race/ethnicity most lives), and light yellow indicates fractions close to 0 (ZCTAs in which the corresponding race/ethnicity least lives). b, US ZCTAs with PM2.5 concentrations higher than 8 μg m−3 in 2000. c, US ZCTAs with PM2.5 concentrations higher than 8 μg m−3 in 2016. Supplementary Videos 58 show the distribution of the different racial/ethnic groups across multiple ranges of PM2.5 concentrations for 2000 and 2016. Note that Hawaii and Alaska are not shown. Imagery provided courtesy of Esri, HERE, Garmin, FAO, NOAA, USGS, ©OpenStreetMap contributors, and the GIS User Community.

Extended Data Fig. 5 Supplementary measures of relative disparities in exposure to PM2.5 among racial/ethnic groups for 2000–2016.

a, The Atkinson index is computed to measure relative disparities among the racial/ethnic groups (Black, white, Asian, Native American and Hispanic or Latino). b, The Gini index is computed to measure relative disparities among the racial/ethnic groups (Black, white, Asian, Native American and Hispanic or Latino). The trends in both indices are similar to that measured by CoV (Fig. 4): racial/ethnic disparities in exposure to air pollution relative to pollution levels at or below the EPA standard are increasing. The Atkinson and Gini indices were computed using the inequality package ‘ineq’ in R software. The input is the proportion of the racial/ethnic (or income) groups living above the set PM2.5 threshold. We set the Atkinson aversion parameter, ε, to 0.75 (ref. 7); the sensitivity of the index to different values of ε is shown in Extended Data Fig. 6.

Extended Data Fig. 6 Sensitivity of the Atkinson index to the inequality aversion parameter ε.

a, Sensitivity of the Atkinson index relative to a PM2.5 threshold of 8 μg m−3. b, Sensitivity of the Atkinson index relative to a PM2.5 threshold of 10 μg m−3. c, Sensitivity of the Atkinson index relative to a PM2.5 threshold of 12 μg m−3. A consistent trend is shown across the parameter values.

Extended Data Fig. 7 Replication of the main findings across urban and rural areas.

A ZCTA’s population density is used as a metric to control for urbanicity in our study. We classify urban and rural areas on the basis of the percentage of the urban population in each ZCTA; such percentages are available from the US Census Bureau for 2010. ZCTAs with an urban population of more than 50% are classified as urban, whereas those with an urban population of less than 50% are classified as rural. For nationwide, urban and rural US, we reproduce our main results: namely, the average PM2.5 concentrations for the total population (ac), for racial/ethnic groups (df) and for income groups (gi), as well as disparities among racial/ethnic groups (jl). Similarities in the results across the national, urban and rural US are apparent and findings are consistent regardless of the urbanicity of ZCTAs. Note that in the case of the rural US, we only compute disparities (l) for the years in which the proportion of the population exposed to PM2.5 concentrations above the thresholds of interest is non-zero. For example, the proportion of the population in the rural US that is exposed to PM2.5 concentrations above T = 12 μg m−3 reaches near-zero levels in 2009, and hence disparities after this year are not computed.

Extended Data Fig. 8 Sensitivity of our main findings to estimates of PM2.5.

We replicated our analysis using an independent pollution data set43,44, and we show here the sensitivity of our findings to the new PM2.5 estimates. a, Replication of Extended Data Fig. 1b using the alternative data set. b, Replication of Extended Data Fig. 1d using the alternative data set. c, Replication of Fig. 4 using the alternative data set. Our main findings are robust and consistent across the two data sets. (Minor differences resulting from the different pollution estimates can be spotted, as expected.).

Supplementary information

Peer Review File

Supplementary Video 1

Average PM2.5 concentration levels across the US by ZCTA and by year from 2000 to 2016. The colour ramps from green to red represent PM2.5 levels of 0–7, 7–8, 8–9, 9–10, 10–11, 11–12, 12–30 µg m3. As the animation moves forward, we sequentially see the PM2.5 levels from 2000, 2001, 2002, up to 2016. Note that Hawaii and Alaska are not shown. Imagery provided courtesy of Esri, HERE, Garmin, FAO, NOAA, USGS, ©OpenStreetMap contributors, and the GIS User Community.

Supplementary Video 2

Average PM2.5 concentration levels for the ZCTAs where the racial/ethnic communities are overrepresented for the years 2000 to 2016. The colour ramps from green to red represent PM2.5 levels of 0–7, 7–8, 8–9, 9–10, 10–11, 11–12, 12–30 µg m3. At the top-left, we highlight ZCTAs where the Black population fraction is higher than 7%. At the top-right we highlight ZCTAs where the white population fraction is higher than 84%. At the bottom-left we highlight ZCTAs where the Hispanic/Latino population fraction is higher than 9%. At the bottom-right we highlight ZCTAs where the Asian population fraction is higher than 2%. As the animation moves forward, we sequentially see the PM2.5 levels from 2000, 2001, 2002, up to 2016. Note that Hawaii and Alaska are not shown. Imagery provided courtesy of Esri, HERE, Garmin, FAO, NOAA, USGS, ©OpenStreetMap contributors, and the GIS User Community.

Supplementary Video 3

An extension of Supplementary Video 2 to the Native American population. We show ZCTAs where the Native American population fraction is higher than 1%. Note that Hawaii and Alaska are not shown. Imagery provided courtesy of Esri, HERE, Garmin, FAO, NOAA, USGS, ©OpenStreetMap contributors, and the GIS User Community.

Supplementary Video 4

Average PM2.5 concentration levels across low- and high- income ZCTAs for the years 2000 to 2016. The colour ramps from green to red represent PM2.5 levels of 0–7, 7–8, 8–9, 9–10, 10–11, 11–12, 12–30 µg m3. On the left, we highlight low-income ZCTAs where the median household income is at the bottom 30%. On the right, we highlight high-income ZCTAs where the median household income is at the top 30%. As the animation moves forward, we sequentially see the PM2.5 levels from 2000, 2001, 2002, up to 2016. Note that Hawaii and Alaska are not shown. Imagery provided courtesy of Esri, HERE, Garmin, FAO, NOAA, USGS, ©OpenStreetMap contributors, and the GIS User Community.

Supplementary Video 5

Distribution of the racial/ethnic communities across levels of PM2.5 concentrations in 2000. The continuous colour ramps from light yellow to dark blue and represents the quantile of the percentage of racial/ethnic communities across ZCTAs from low to high. As the animation moves forward, we sequentially see which racial/ethnic communities are exposed to PM2.5 levels above 0, 7, 8, 9, 10, 11, 12 µg m3. Note that Hawaii and Alaska are not shown. Imagery provided courtesy of Esri, HERE, Garmin, FAO, NOAA, USGS, ©OpenStreetMap contributors, and the GIS User Community.

Supplementary Video 6

An extension of Supplementary Video 5 to the Native American population. Note that Hawaii and Alaska are not shown. Imagery provided courtesy of Esri, HERE, Garmin, FAO, NOAA, USGS, ©OpenStreetMap contributors, and the GIS User Community.

Supplementary Video 7

Distribution of the racial/ethnic communities across levels of PM2.5 concentrations in 2016. The continuous colour ramps from light yellow to dark blue and represents the quantile of the percentage of racial/ethnic communities across ZCTAs from low to high. As the animation moves forward, we sequentially see which racial/ethnic communities are exposed to PM2.5 levels above 0, 7, 8, 9, 10, 11, 12 µg m3. Note that Hawaii and Alaska are not shown. Imagery provided courtesy of Esri, HERE, Garmin, FAO, NOAA, USGS, ©OpenStreetMap contributors, and the GIS User Community.

Supplementary Video 8

An extension of Supplementary Video 7 to the Native American population. Note that Hawaii and Alaska are not shown. Imagery provided courtesy of Esri, HERE, Garmin, FAO, NOAA, USGS, ©OpenStreetMap contributors, and the GIS User Community.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Jbaily, A., Zhou, X., Liu, J. et al. Air pollution exposure disparities across US population and income groups. Nature 601, 228–233 (2022). https://doi.org/10.1038/s41586-021-04190-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41586-021-04190-y

Further reading

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing