Abstract
Steady improvements in ambient air quality in the USA over the past several decades, in part a result of public policy1,2, have led to public health benefits1,2,3,4. However, recent trends in ambient concentrations of particulate matter with diameters less than 2.5 μm (PM2.5), a pollutant regulated under the Clean Air Act1, have stagnated or begun to reverse throughout much of the USA5. Here we use a combination of ground- and satellite-based air pollution data from 2000 to 2022 to quantify the contribution of wildfire smoke to these PM2.5 trends. We find that since at least 2016, wildfire smoke has influenced trends in average annual PM2.5 concentrations in nearly three-quarters of states in the contiguous USA, eroding about 25% of previous multi-decadal progress in reducing PM2.5 concentrations on average in those states, equivalent to 4 years of air quality progress, and more than 50% in many western states. Smoke influence on trends in the number of days with extreme PM2.5 concentrations is detectable by 2011, but the influence can be detected primarily in western and mid-western states. Wildfire-driven increases in ambient PM2.5 concentrations are unregulated under current air pollution law6 and, in the absence of further interventions, we show that the contribution of wildfire to regional and national air quality trends is likely to grow as the climate continues to warm.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 51 print issues and online access
$199.00 per year
only $3.90 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
Data availability
Data to reproduce all results in the paper are available at https://github.com/echolab-stanford/wildfire-influence.
Code availability
Code to reproduce all results in the paper are available at https://github.com/echolab-stanford/wildfire-influence.
References
Currie, J. & Walker, R. What do economists have to say about the clean air act 50 years after the establishment of the environmental protection agency? J. Econ. Perspectives 33, 3–26 (2019).
Aldy, J. E., Auffhammer, M., Cropper, M., Fraas, A. & Morgenstern, R. Looking back at 50 years of the Clean Air Act. J. Econ. Lit. 60, 179–232 (2022).
Benefits and Costs of the Clean Air Act 1990-2020. Report Documents and Graphics (US Environmental Protection Agency, 2011); https://www.epa.gov/clean-air-act-overview/benefits-and-costs-clean-air-act-1990-2020-report-documents-and-graphics.
Correia, A. W. et al. The effect of air pollution control on life expectancy in the United States: an analysis of 545 US counties for the period 2000 to 2007. Epidemiology 24, 23–31 (2013).
Clay, K., Muller, N. Z. & Wang, X. Recent increases in air pollution: evidence and implications for mortality. Rev. Environ. Econ. Policy 15, 154–162 (2021).
Burke, M. et al. The changing risk and burden of wildfire in the United States. Proc. Natl Acad. Sci. USA 118, e2011048118 (2021).
Abatzoglou, J. T. & Williams, A. P. Impact of anthropogenic climate change on wildfire across western US forests. Proc. Natl Acad. Sci. USA 113, 11770–11775 (2016).
Parks, S. A. & Abatzoglou, J. T. Warmer and drier fire seasons contribute to increases in area burned at high severity in western US forests from 1985 to 2017. Geophys. Res. Lett. 47, e2020GL089858 (2020).
Juang, C. S. et al. Rapid growth of large forest fires drives the exponential response of annual forest-fire area to aridity in the western United States. Geophys. Res. Lett. 49, e2021GL097131 (2022).
Boisramé, G. F., Brown, T. J. & Bachelet, D. M. Trends in western USA fire fuels using historical data and modeling. Fire Ecol. 18, 8 (2022).
Balch, J. K. et al. Human-started wildfires expand the fire niche across the United States. Proc. Natl Acad. Sci. USA 114, 2946–2951 (2017).
Landrigan, P. J. et al. The Lancet commission on pollution and health. Lancet 391, 462–512 (2018).
McClure, C. D. & Jaffe, D. A. US particulate matter air quality improves except in wildfire-prone areas. Proc. Natl Acad. Sci. USA 115, 7901–7906 (2018).
O’Dell, K., Ford, B., Fischer, E. V. & Pierce, J. R. Contribution of wildland-fire smoke to US PM2.5 and its influence on recent trends. Environ. Sci. Technol. 53, 1797–1804 (2019).
Xie, Y., Lin, M. & Horowitz, L. W. Summer PM2.5 pollution extremes caused by wildfires over the western United States during 2017–2018. Geophys. Res. Lett. 47, e2020GL089429 (2020).
Childs, M. L. et al. Daily local-level estimates of ambient wildfire smoke PM2.5 for the contiguous US. Environ. Sci. Technol. 56, 13607–13621 (2022).
David, L. M. et al. Could the exception become the rule? ‘Uncontrollable’ air pollution events in the US due to wildland fires. Environ. Res. Lett. 16, 034029 (2021).
Tschofen, P., Azevedo, I. L. & Muller, N. Z. Fine particulate matter damages and value added in the US economy. Proc. Natl Acad. Sci. USA 116, 19857–19862 (2019).
Burney, J. A. The downstream air pollution impacts of the transition from coal to natural gas in the United States. Nat. Sustain. 3, 152–160 (2020).
Shapiro, J. S. Pollution trends and US environmental policy: lessons from the past half century. Rev. Environ. Econ. Policy 16, 42–61 (2022).
Muggeo, V. M. Estimating regression models with unknown break-points. Stat. Med. 22, 3055–3071 (2003).
Brey, S. J., Ruminski, M., Atwood, S. A. & Fischer, E. V. Connecting smoke plumes to sources using hazard mapping system (HMS) smoke and fire location data over North America. Atmospheric Chem. Phys. 18, 1745–1761 (2018).
Goss, M. et al. Climate change is increasing the likelihood of extreme autumn wildfire conditions across California. Environ. Res. Lett. 15, 094016 (2020).
Brey, S. J., Barnes, E. A., Pierce, J. R., Swann, A. L. & Fischer, E. V. Past variance and future projections of the environmental conditions driving western US summertime wildfire burn area. Earth’s Future 9, e2020EF001645 (2021).
Xie, Y. et al. Tripling of western US particulate pollution from wildfires in a warming climate. Proc. Natl Acad. Sci. USA 119, e2111372119 (2022).
Neumann, J. E. et al. Estimating PM2.5-related premature mortality and morbidity associated with future wildfire emissions in the western US. Environ. Res. Lett. 16, 035019 (2021).
Wang, Y. et al. Location-specific strategies for eliminating US national racial-ethnic PM2.5 exposure inequality. Proc. Natl Acad. Sci. USA 119, e2205548119 (2022).
Policy Assessment for the Reconsideration of the National Ambient Air Quality Standards for Particulate Matter (US Environmental Protection Agency, 2022); https://www.epa.gov/system/files/documents/2022-05/Final%20Policy%20Assessment%20for%20the%20Reconsideration%20of%20the%20PM%20NAAQS_May2022_0.pdf.
Confronting the Wildfire Crisis: A Strategy for Protecting Communities and Improving Resilience in America’s Forests, FS-1187a (US Forest Service, 2022); https://www.fs.usda.gov/sites/default/files/fs_media/fs_document/Confronting-the-Wildfire-Crisis.pdf.
Exceptional Events Guidance: Prescribed Fire on Wildland that May Influence Ozone and Particulate Matter Concentrations (US Environmental Protection Agency, 2019); https://www.epa.gov/sites/default/files/2019-08/documents/ee_prescribed_fire_final_guidance_-_august_2019.pdf.
Williams, E. Reimagining exceptional events: regulating wildfires through the Clean Air Act. Wash. L. Rev. 96, 765 (2021).
Particulate Matter (PM25) Trends (US Environmental Protection Agency, 2022); https://www.epa.gov/air-trends/particulate-matter-pm25-trends.
Karl, T. & Koss, W. J. Regional and National Monthly, Seasonal, and Annual Temperature Weighted by Area, 1895-1983. NOAA Climatology History Series 4-3 (NOAA, 1984).
Outdoor Air Quality Data (US Environmental Protection Agency, 2023); https://www.epa.gov/outdoor-air-quality-data/download-daily-data.
What Does the POC Number Refer To? (US Environmental Protection Agency, accessed September 2023); https://www.epa.gov/outdoor-air-quality-data/what-does-poc-number-refer.
Burke, M. et al. Exposures and behavioral responses to wildfire smoke. Nat. Hum. Behav. 6, 1351–1361 (2022).
Hazard Mapping System Fire and Smoke Product (National Oceanic and Atmospheric Administration, accessed September 2023); https://www.ospo.noaa.gov/Products/land/hms.html#about.
Schroeder, W. et al. Validation analyses of an operational fire monitoring product: the hazard mapping system. Int. J. Remote Sens. 29, 6059–6066 (2008).
Rolph, G. D. et al. Description and verification of the NOAA smoke forecasting system: the 2007 fire season. Weather. Forecast. 24, 361–378 (2009).
Ruminski, M., Kondragunta, S., Draxler, R. & Zeng, J. Recent changes to the hazard mapping system. In Proc. 15th International Emission Inventory Conference Vol. 15 (EPA, 2006).
Koplitz, S. N., Nolte, C. G., Pouliot, G. A., Vukovich, J. M. & Beidler, J. Influence of uncertainties in burned area estimates on modeled wildland fire PM2.5 and ozone pollution in the contiguous US. Atmos. Environ. 191, 328–339 (2018).
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).
Reid, C. E., Considine, E. M., Maestas, M. M. & Li, G. Daily PM2.5 concentration estimates by county, zip code, and census tract in 11 western states 2008–2018. Sci. Data 8, 112 (2021).
Bergé, L. Efficient estimation of maximum likelihood models with multiple fixed-effects: the R package FENmlm. DEM Discussion Paper Series (Department of Economics, Univ. Luxembourg, 2018).
Zhuang, Y., Fu, R., Santer, B. D., Dickinson, R. E. & Hall, A. Quantifying contributions of natural variability and anthropogenic forcings on increased fire weather risk over the western United States. Proc. Natl Acad. Sci. USA 118, e2111875118 (2021).
Jain, P., Castellanos-Acuna, D., Coogan, S. C., Abatzoglou, J. T. & Flannigan, M. D. Observed increases in extreme fire weather driven by atmospheric humidity and temperature. Nat. Clim. Chang. 12, 63–70 (2022).
Abatzoglou, J. T. Development of gridded surface meteorological data for ecological applications and modelling. Int. J. Climatol. 33, 121–131 (2013).
Hausfather, Z. & Peters, G. P. Emissions–the ‘business as usual’ story is misleading. Nature 577, 618–620 (2020).
Wooldridge, J. M. Introductory Econometrics: A Modern Approach (Cengage Learning, 2015).
Acknowledgements
We thank members of Stanford ECHOLab and seminar participants at University of California Berkeley, Columbia, Duke, Montana State, Minnesota and University of California Santa Barbara for helpful comments. Some of the computing for this project was performed on the Sherlock cluster, and we thank Stanford University and the Stanford Research Computing Center for providing computational resources and support that contributed to these research results. M.L.C. was supported by an Environmental Fellowship at the Harvard University Center for the Environment. M.Q. was supported by a fellowship at Stanford’s Center for Innovation in Global Health. M.B. thanks the Keck Foundation for research support.
Author information
Authors and Affiliations
Contributions
M.B. and M.L.C. conceived the project. M.B., M.L.C., B.d.l.C., M.Q. and J.L. analysed data. All authors interpreted results and wrote the paper.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Peer review
Peer review information
Nature thanks Vito Muggeo 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 Pollution stations and method used to construct non-smoke PM2.5 estimates.
a. Example of total and non-smoke partitioning for a single station in CA in 2020. On days without a smoke plume overhead (no grey points), all PM2.5 is assumed to be from non-smoke sources. On days with a plume overhead (grey points), PM2.5 anomalies from the non-smoke month- and station-specific 3-year median are attributed to smoke, and total PM2.5 minus anomalies are attributed to non-smoke (blue). b. Annual average total and non-smoke PM2.5 for the same station are produced by aggregating daily total observed PM2.5 (black) and the daily estimates of non-smoke PM2.5 (blue). c. Locations of PM2.5 stations throughout the contiguous US. Stations are coloured by the number of years with at least 50 observations.
Extended Data Fig. 2 Distribution in estimated breakpoints for different sample restrictions and/or statistical specifications.
a. Annual average PM2.5. b. Extreme (> 35 µg/m3) daily PM2.5. Histograms show distribution of estimated breakpoints for different sample restrictions, pooling data from all CONUS monitors. Panels labeled with number of observations and years are various sample restriction choices, while those labeled “Drop” retain our primary inclusion criteria – more than 50 observations per year for at least 15 years – but remove one of the last three years of the sample to understand their influence on estimates. Discontinuous models allow for separate intercepts on either side of the break year. Strong bunching in the discontinuous models for average PM2.5 occur because only integer years are permitted as candidate breakpoints. “Positive smoke PM2.5 anomalies” tests the sensitivity of results to bottom-coding daily smoke PM2.5 estimates to zero, i.e., not allowing negative smoke PM2.5 anomalies.
Extended Data Fig. 3 Influence of wildfire smoke on daily PM2.5 extremes is mainly concentrated in states in the West, Northwest, and Great Plains.
Black lines in each plot show percent of days in each state-year where PM2.5 values exceed 35 µg/m3, calculated using the sample of stations with over 50 observations in at least 15 years, as in Fig. 3. Blue lines show estimated percent of days that exceed 35 µg/m3 after smoke PM2.5 has been removed. Vertical dotted line indicates median CONUS-wide estimated breakpoint (2012).
Extended Data Fig. 4 Sensitivity of estimated differences in slope coefficients used to classify states.
Left column shows differences in early (β1) and recent period (β2) estimates of changes in total PM2.5, and the confidence interval on estimated differences, that are used to classify states into stagnating/reversing categories. Middle column shows differences in recent period total PM2.5 (β2) and non-smoke PM2.5 (\({\beta }_{2}^{{\prime} }\)) slopes that are used to classify states as smoke-influenced. Right column shows same, but for recent period changes in extreme days. Colors match sample as denoted in the legend at right.
Extended Data Fig. 5 Sensitivity of total PM2.5-trend classification to different sample restrictions and/or statistical specifications.
a. State-specific total PM2.5-trend classification under alternate estimates. b. Counts of states in each classification. Model specifications and samples match those in Extended Data Fig. 2.
Extended Data Fig. 6 Sensitivity of smoke-influence classification to different sample restrictions and/or statistical specifications.
a. State-specific smoke-influence classification under alternate estimates. b. Counts of states in each classification. Model specifications and samples match those in Extended Data Fig. 2. c. Regional trends in total and non-smoke PM2.5, and regional smoke-influence classifications with region-specific breakpoint estimates (vertical dashed lines).
Extended Data Fig. 7 Sensitivity of smoke-influence classification on portion of extreme days (> 35 µg/m3) to different sample restrictions.
a. State-specific smoke-influence classification under alternate estimates. b. Counts of states in each classification. Model specifications and samples match those in Extended Data Fig. 2.
Extended Data Fig. 8 Distribution of proportion of extreme days due to wildfire smoke by state.
a. Density plots show, for each year, the distribution across CONUS states of the proportion of days above 35 µg/m3 due to smoke, i.e., days that would have had concentrations < 35 µg/m3 were smoke not present. Tick marks show values for individual states. b. Cumulative distributions of the number of states where the proportion of extreme PM2.5 days due to wildfire smoke in a time period met or exceeded a given percentage threshold. For instance, the intersection of a vertical line drawn at 50% and each of the depicted lines in the plot would provide estimates of the number of states in each period where at least 50% of extreme days were due to wildfire smoke.
Supplementary information
Supplementary Methods
This file information contains additional methods information related to our empirical approach and Supplementary Fig. 1.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Burke, M., Childs, M.L., de la Cuesta, B. et al. The contribution of wildfire to PM2.5 trends in the USA. Nature 622, 761–766 (2023). https://doi.org/10.1038/s41586-023-06522-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41586-023-06522-6
This article is cited by
-
Clearing the air: evaluating institutions’ social media health messaging on wildfire and smoke risks in the US Pacific Northwest
BMC Public Health (2024)
-
Healthier air, healthier planet
Nature Geoscience (2023)
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.
