Journal cover Journal topic
Atmospheric Chemistry and Physics An interactive open-access journal of the European Geosciences Union
Atmos. Chem. Phys., 17, 7541-7554, 2017
https://doi.org/10.5194/acp-17-7541-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Research article
22 Jun 2017
Status update: is smoke on your mind? Using social media to assess smoke exposure
Bonne Ford1, Moira Burke2, William Lassman1, Gabriele Pfister3, and Jeffrey R. Pierce1 1Department of Atmospheric Science, Colorado State University, 1371 Campus Delivery, Fort Collins, CO 80523, USA
2Facebook, Menlo Park, CA 94025, USA
3National Center for Atmospheric Research, 3450 Mitchell Lane, Boulder, CO 80301, USA
Abstract. Exposure to wildland fire smoke is associated with negative effects on human health. However, these effects are poorly quantified. Accurately attributing health endpoints to wildland fire smoke requires determining the locations, concentrations, and durations of smoke events. Most current methods for assessing these smoke events (ground-based measurements, satellite observations, and chemical transport modeling) are limited temporally, spatially, and/or by their level of accuracy. In this work, we explore using daily social media posts from Facebook regarding smoke, haze, and air quality to assess population-level exposure for the summer of 2015 in the western US. We compare this de-identified, aggregated Facebook dataset to several other datasets that are commonly used for estimating exposure, such as satellite observations (MODIS aerosol optical depth and Hazard Mapping System smoke plumes), daily (24 h) average surface particulate matter measurements, and model-simulated (WRF-Chem) surface concentrations. After adding population-weighted spatial smoothing to the Facebook data, this dataset is well correlated (R2 generally above 0.5) with the other methods in smoke-impacted regions. The Facebook dataset is better correlated with surface measurements of PM2. 5 at a majority of monitoring sites (163 of 293 sites) than the satellite observations and our model simulation. We also present an example case for Washington state in 2015, for which we combine this Facebook dataset with MODIS observations and WRF-Chem-simulated PM2. 5 in a regression model. We show that the addition of the Facebook data improves the regression model's ability to predict surface concentrations. This high correlation of the Facebook data with surface monitors and our Washington state example suggests that this social-media-based proxy can be used to estimate smoke exposure in locations without direct ground-based particulate matter measurements.

Citation: Ford, B., Burke, M., Lassman, W., Pfister, G., and Pierce, J. R.: Status update: is smoke on your mind? Using social media to assess smoke exposure, Atmos. Chem. Phys., 17, 7541-7554, https://doi.org/10.5194/acp-17-7541-2017, 2017.
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Short summary
We explore using the percent of Facebook posters mentioning smoke or air quality to assess exposure to wildfire smoke in the western US during summer 2015. We compare this de-identified, aggregated Facebook dataset to satellite observations, surface measurements, and model-simulated concentrations, and we find good agreement in smoke-impacted regions. Our results suggest that aggregate social media data can be used to supplement traditional datasets to estimate smoke exposure.
We explore using the percent of Facebook posters mentioning smoke or air quality to assess...
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