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Exposures and behavioural responses to wildfire smoke

Nature Human Behaviour volume 6, pages 1351–1361 (2022)Cite this article

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Abstract

Pollution from wildfires constitutes a growing source of poor air quality globally. To protect health, governments largely rely on citizens to limit their own wildfire smoke exposures, but the effectiveness of this strategy is hard to observe. Using data from private pollution sensors, cell phones, social media posts and internet search activity, we find that during large wildfire smoke events, individuals in wealthy locations increasingly search for information about air quality and health protection, stay at home more and are unhappier. Residents of lower-income neighbourhoods exhibit similar patterns in searches for air quality information but not for health protection, spend less time at home and have more muted sentiment responses. During smoke events, indoor particulate matter (PM2.5) concentrations often remain 3–4× above health-based guidelines and vary by 20× between neighbouring households. Our results suggest that policy reliance on self-protection to mitigate smoke health risks will have modest and unequal benefits.

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Fig. 1: Trends in smoke exposure across the United States.
Fig. 2: Behavioural responses to wildfire smoke exposure.
Fig. 3: Smoke salience does not differ, but other responses do differ, among more and less wealthy populations.
Fig. 4: Infiltration rates decline strongly with outdoor PM2.5 concentration during both fire and non-fire periods, but not with income, and they differ greatly across households, resulting in extreme differences in indoor exposure during wildfires.

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Data availability

The data to replicate all the results in the main text and supplementary material are available at https://github.com/echolab-stanford/wildfire-exposure-behavior-public.

Code availability

The code to replicate all the results in the main text and supplementary material is available at https://github.com/echolab-stanford/wildfire-exposure-behavior-public.

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Acknowledgements

We thank the Robert Wood Johnson Foundation and Stanford’s Center for Population Health Sciences for funding (M.B., S.H.-N., J.L. and A.D.), SafeGraph for data access, Stanford University and the Stanford Research Computing Center for computational resources and support, and members of the ECHOLab and seminar participants at Cornell, Columbia, MIT, Stanford, UC Berkeley and UC Santa Barbara for helpful comments. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Authors and Affiliations

  1. Department of Earth System Science, Stanford University, Stanford, CA, USA

    Marshall Burke, Jeff Wen & Carlos F. Gould

  2. Center on Food Security and the Environment, Stanford University, Stanford, CA, USA

    Marshall Burke, Sam Heft-Neal, Jessica Li, Anne Driscoll & Matthieu Stigler

  3. National Bureau of Economic Research, Cambridge, MA, USA

    Marshall Burke

  4. Department of Economics, University of British Columbia, Vancouver, British Columbia, Canada

    Patrick Baylis

  5. Department of Agricultural and Resource Economics, University of California, Davis, Davis, CA, USA

    Joakim A. Weill

  6. Global Policy School, University of California, San Diego, San Diego, CA, USA

    Jennifer A. Burney

  7. Emmett Interdisciplinary Program in Environment and Resources, Stanford University, Stanford, CA, USA

    Marissa L. Childs

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Contributions

All authors contributed to the conception and design of the study. S.H.-N., J.L., A.D., J.W. and M.L.C. constructed the smoke data. J.L. and A.D. constructed the Google search data. P.B. constructed the Twitter dataset. M.S. and J.A.W. constructed the mobility dataset. S.H.-N. constructed the PurpleAir dataset. M.B. and S.H.-N. led the econometric analysis. All authors contributed to analysing the results and writing the paper.

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Correspondence to Marshall Burke.

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The authors declare no competing interests.

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Nature Human Behaviour thanks Priyanka deSouza, Francesca Dominici and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Counties included in analyses that use EPA pollution monitors, and correlation in smoke PM2.5 between monitor pairs as a function of distance between monitors.

a. Counties in red are those with EPA pollution monitors from which we construct smoke PM2.5 measures for the behavioral analyses. b Colors depict a heatmap of the 85,102 pairwise correlations, with lighter colors depicting areas with more data and shown in legend at right; solid black line is the median correlation at each distance. Sample is restricted to stations with at least 1000 days of data. Mean width of counties in our data is 55 km, and mean width of metro areas is 228 km. Source for a: US Census Bureau.

Extended Data Fig. 2 Time spent indoors at home in America.

Data are from repeated rounds of the American Time Use Survey. Top panels show data by average income, age, season, and race/ethnicity. Bottom map shows averages by state across survey rounds. Source for e: US Census Bureau.

Extended Data Fig. 3 Effect of smoke PM2.5 on different mobility measures, and heterogeneity by income.

a Percent of mobile phones estimated to be completely at home on a given day at the US county level, 2019–2020. Black lines are regression point estimates from spline fits conditional on fixed effects, with shaded areas showing bootstrapped 95% confidence intervals. Number of observations in each regression is shown in upper left corner of each panel. Histograms at the bottom show the log distribution of smoke PM2.5 exposure in each sample. b Same but for % fully away from home on that day. c-d Effect of smoke PM2.5 on mobility as a function of income. Lines show the marginal effect of a heavy smoke exposure (50ug smoke PM2.5 on that day) on percent of individuals completely at home on that day (c) or completely away from home (d), as a function of median household income in that county. Colors represent models run with either date fixed effects (blue) or state-by-date fixed effects (orange). Dark lines show regression point estimates, shaded area the bootstrapped 95% CI.

Extended Data Fig. 4 Exposure to average and acute smoke PM2.5 at the county level does not differ systematically by income.

Daily smoke PM2.5 exposures by income decile across US counties, 2006-2020. Dots represent daily observations where smoke PM2.5 was non-zero. Plot is truncated at 300ug for clarity; not plotted are 71 days (0.001% of the sample) in which smoke PM2.5 exceeded 300. Statistics at right show the percent of observations across the study period with daily smoke PM2.5 observations above the listed value.

Extended Data Fig. 5 Higher income US census tracts are more likely to have PurpleAir monitors.

Grey bars show the distribution of tract-level median household income across all US census tracts in the contiguous US, red bars the income in tracts with at least one outdoor PurpleAir sensor, and blue bars the income in tracts with at least one indoor PurpleAir sensor. Vertical lines give the median of each distribution.

Extended Data Fig. 6 Infiltration estimates are highly correlated across alternate statistical models and methods of deriving PM2.5 concentrations from Purple Air data.

Correlation between infiltration estimates from statistical models with different lag structures and different PM2.5 concentration estimates (see Supplementary Table 11 for details). 1a is our preferred specification presented in the main results.

Extended Data Fig. 7 Understanding variation in household-specific infiltration estimates.

a Posterior estimates of monitor-specific infiltration rates from a Bayesian hierarchical model are very similar to “raw” estimates from our monitor-specific time-series regressions, indicating that true heterogeneity rather than sampling noise is what is driving observed differences in estimated infiltration. b Ability of random forest (RF) or gradient boosted trees (GBT) model to explain variation (r2) in infiltration across monitors remains low; models use predictors in (c). c For each predictor, we calculate the effect on infiltration of moving from the 5th to the 95th percentile of that predictor in the test dataset, holding the other predictors constant at their average value in the test dataset; estimates are shown for RF and GBT models and for four alternate spatial buffers used to construct housing predictors. Housing Index is constructed by averaging standardized values of home value, number of stories, number of baths, number of bedrooms, height, and area. A/C measures the inverse distance weighted proportion of matched CoreLogic houses that have air conditioning. Median Income is the median household income in the Census tract population. Race variables (i.e. all demographic covariates except Hispanic) are measured among the non-Hispanic/Latino population. AI/AN stands for American Indian and Alaska Native. NHPI stands for Native Hawaiian and other Pacific Islander. HDD and CDD stand for heating degree days and cooling degree days, respectively. d Range of household-level infiltration estimates for the full sample and for sub-samples when behavior (ie opening/closing of doors, use of air purifier) is expected to matter less: when it’s raining, nighttime, and periods when it is cold ( < 10∘C) and low PM2.5 ( < 30μg/m3). e-f during periods when behavioral factors are more likely minimized, infiltration varies more strongly with income and housing age.

Extended Data Fig. 8 Monitor-specific infiltration estimates using indoor/outdoor ratios versus regression-based approaches.

I/O estimates are only modestly correlated with our preferred regression-based estimates that measure the marginal effect on indoor PM2.5 concentrations of a unit increase in outdoor concentrations. For each monitor I/O ratio was calculated across all observations with hourly indoor PM2.5 less or equal to outdoor PM2.5. δI/δO was estimated as described in Methods.

Extended Data Fig. 9 Outdoor and indoor PM2.5 concentrations on a smoke day in CA.

Very similar outdoor PM2.5 concentrations during a smoke event on Aug 20th, 2021 over a high-income area of the peninsular Bay Area were associated with widely varying contemporaneous indoor PM2.5 concentrations. © OpenStreetMap contributors.

Extended Data Fig. 10 Variation in indoor PM2.5 across monitors with similar outdoor PM2.5 during the Aug/Sep 2020 smoke event in the Bay Area.

Each dot is average outdoor PM2.5 and average indoor PM2.5 for an individual monitor in the Bay Area over the Aug/Sep 2020 smoke event, with monitors grouped into 5 μg/m3bins based on outdoor exposure. Numbers at top show the number of monitors in each bin (black), and the ratio of maximum to minimum indoor PM2.5 across monitors within each outdoor PM2.5 bin (red). Monitors with average outdoor PM2.5 exposures within 5 μg/m3of each other experienced > 20x differences in indoor PM2.5 exposures.

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Burke, M., Heft-Neal, S., Li, J. et al. Exposures and behavioural responses to wildfire smoke. Nat Hum Behav 6, 1351–1361 (2022). https://doi.org/10.1038/s41562-022-01396-6

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