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Impacts of fire smoke plumes on regional air quality, 2006–2013

Journal of Exposure Science & Environmental Epidemiology volume 28, pages 319–327 (2018)Cite this article

Abstract

Increases in the severity and frequency of large fires necessitate improved understanding of the influence of smoke on air quality and public health. The objective of this study is to estimate the effect of smoke from fires across the continental U.S. on regional air quality over an extended period of time. We use 2006–2013 data on ozone (O3), fine particulate matter (PM2.5), and PM2.5 constituents from environmental monitoring sites to characterize regional air quality and satellite imagery data to identify plumes. Unhealthy levels of O3 and PM2.5 were, respectively, 3.3 and 2.5 times more likely to occur on plume days than on clear days. With a two-stage approach, we estimated the effect of plumes on pollutants, controlling for season, temperature, and within-site and between-site variability. Plumes were associated with an average increase of 2.6 p.p.b. (2.5, 2.7) in O3 and 2.9 µg/m3 (2.8, 3.0) in PM2.5 nationwide, but the magnitude of effects varied by location. The largest impacts were observed across the southeast. High impacts on O3 were also observed in densely populated urban areas at large distance from the fires throughout the southeast. Fire smoke substantially affects regional air quality and accounts for a disproportionate number of unhealthy days.

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Fig. 1: Geographic distribution of environmental monitoring sites for O3, PM2.5, species of PM2.5, and HMS smoke plumes on 14 June 2008.
Fig. 2: Frequency of days with HMS smoke plumes visible above O3 monitoring sites (a) and above PM2.5 FRM monitoring sites (b).
Fig. 3: Geographic distribution of the estimated change in pollutant concentrations on plume days for O3 monitoring sites (a) and PM2.5 FRM monitoring sites (b) by quartiles of distribution.

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Acknowledgements

The research described in this article has been reviewed by the National Health and Environmental Effects Research Laboratory, U.S. Environmental Protection Agency, and approved for publication. Approval does not signify that the contents necessarily reflect the views and policies of the agency, nor does the mention of trade names of commercial products constitute endorsement or recommendation for use. A.L. was supported in part by NIGMS grant 5T32GM081057. B.J.R. was supported by JFSP grant 14-1-04-9 and NIH R21ES022795-01A1.

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Authors and Affiliations

  1. Department of Statistics, North Carolina State University, Raleigh, NC, USA

    Alexandra E. Larsen & Brian J. Reich

  2. National Oceanic and Atmospheric Administration/National Environmental Satellite, Data, and Information Service, Camp Springs, MD, USA

    Mark Ruminski

  3. US Environmental Protection Agency, Office of Research and Development, National Health and Environmental Effects Research Laboratory, Research Triangle Park, NC, USA

    Ana G. Rappold

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Correspondence to Ana G. Rappold.

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Appendix A. Stage 2 analysis details

Appendix A. Stage 2 analysis details

Stage two of our analysis on the impact of HMS-detected smoke plumes on air pollution is as follows. We assume the model

$$\begin{array}{l}\!\!\!\!\!\!\!\!\widehat \beta |\tilde \beta \sim {\cal N}\left( {\tilde \beta ,\Sigma _1} \right)\\ \tilde \beta |\mu \sim {\cal N}\left( {\mu {\mathbf{1}}_{\mathbf{m}},\Sigma _2} \right),\end{array}$$

where m is number of stations (from stage one, s = 1,...,m), \(\hat \beta\) is the m-vector of stage-one plume effect estimates, \(\tilde \beta\) is the m-vector of true plume effects at the m stations, \(\Sigma _1\) and \(\Sigma _2\) are m-dimensional spatial covariance matrices and μ is the nation-wide average effect of plume episodes on a given pollutant. We aim to estimate and present μ for all pollutants and stage-two estimates of \(\tilde \beta\), denoted as \(\widehat {\widetilde \beta }\).

We consider four special cases of this general two-stage spatial model by changing settings on the spatial covariance matrices. Define V as the mxm diagonal matrix of standard errors of the stage-one plume effect estimates, v s, and let Ω(ρ 1) and Ω(ρ 2) be m×m exponential correlation matrices with (i, j) elements exp(−d ij /ρ 1) and exp(−d ij /ρ 2), respectively, where d ij is the distance in kilometers between site i and site j, and ρ 1 and ρ 2 are spatial range parameters. We let

$$\begin{array}{l}\Sigma _1 = V\left[ {\left( {1 - r_1} \right)I_m + r_1\Omega \left( {\rho _1} \right)} \right]V\\ \quad \quad \quad \quad \left( {{\mathrm{covariance}}\,\,{\mathrm{of}}\,\,{\mathrm{the}}\,\,{\mathrm{stage}}\,-{\mathrm{one}}\,\,{\mathrm{errors}}} \right) \\ \Sigma _2 = \sigma ^2\left[ {\left( {1 - r_2} \right)I_m + r_2\Omega \left( {\rho _2} \right)} \right]\quad \left( {{\mathrm{covariance}}\,{\mathrm{of}}\,\,{\mathrm{true}}\,\,\beta } \right)\end{array},$$

where r 1 and r 2 represent the proportion of variance due to spatial patterns and σ 2 is the variance of the true effect. Changing these parameters allows us to investigate if spatially correlated (r ≠ 0) or independent (r = 0) stage-one plume errors and/or constant (σ 2 = 0 and thus \(\tilde \beta _s = \mu\) for all s) or spatially varying (σ 2 > 0 and thus \(\tilde \beta _s \,\ne \,\mu\) for all s) true plume effects are the best fit for a given pollutant. Table 3 summarizes these four models.

Table 3 Description of the four models fit for each pollutant
Full size table

For each pollutant, we fit the four models in Table 3 and determine best fit with BIC (see results in Table 2 in the main text).

To estimate the nation-wide average plume effect, μ, for each pollutant, we computed the GLS estimate, \(\hat \mu\), and the variance of \(\hat \mu\) in R. The derivation for the GLS is as follows:

$$\begin{array}{ccccc}\\ \!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\widehat \beta = \tilde \beta + e_1,\quad e_1 \sim N\left( {0,\Sigma _1} \right)\\ \\ \!\!\!\!\!\!\!\!\!\!\!\!\tilde \beta = \mu {\mathbf{1}}_{_{\boldsymbol{m}}} + e_2,\quad e_2 \sim N\left( {0,\Sigma _2} \right)\\ \\ \!\!\!\!\!\!\Rightarrow \widehat \beta = \mu {\mathbf{1}}_{_{\boldsymbol{m}}} + e^\ast ,\quad e^\ast \sim N\left( {0,\Sigma _1 + \Sigma _2} \right)\\ \\ \ \ \ \ \ \ \ \!\!\Rightarrow \hat \mu = \left( {{\mathbf{1}}_{\boldsymbol{m}}^T\left( {\hat \Sigma _1 + \hat \Sigma _2} \right)^{ - 1}{\mathbf{1}}_{_{\boldsymbol{m}}}} \right)^{ - 1}{\mathbf{1}}_{_{\boldsymbol{m}}}^T\left( {\hat \Sigma _1 + \hat \Sigma _2} \right)^{ - 1}\hat \beta \\ \\ \!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\Rightarrow Var(\hat \mu ) = \left( {{\mathbf{1}}_{_{\boldsymbol{m}}}^T\left( {\hat \Sigma _1 + \hat \Sigma _2} \right)^{ - 1}{\mathbf{1}}_{_{\boldsymbol{m}}}} \right)^{ - 1}\\ \end{array}$$

where \(\hat \Sigma _1\) and \(\hat \Sigma _2\) are functions of the maximum likelihood estimates of the spatial covariance parameters ϕ calculated using the R package, optim. To estimate the stage-two estimates of the true plume effect, \(\widetilde \beta\), we compute:

\(\widehat {\widetilde {\beta} } = \left( {\hat \Sigma _1^{ - 1} + \hat \Sigma _2^{ - 1}} \right)^{ - 1}\left( {\hat \Sigma _1^{ - 1}\widehat \beta + \hat \Sigma _2^{ - 1}\hat \mu {\mathbf{1}}_m} \right).\)

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Larsen, A.E., Reich, B.J., Ruminski, M. et al. Impacts of fire smoke plumes on regional air quality, 2006–2013. J Expo Sci Environ Epidemiol 28, 319–327 (2018). https://doi.org/10.1038/s41370-017-0013-x

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