We determined the temporal correlation of these datasets to several other datasets that are commonly used for estimating exposure to LFS on a daily timescale. We use 24 h average concentrations of total PM2.5 mass from the EPA Air Quality System (AQS; data from www.epa.gov/aqs), which includes monitor data from different agencies, and sites from the Interagency Monitoring of Protected Visual Environments (IMPROVE; data from http://views.cira.colostate.edu/fed/). At IMPROVE network sites, surface measurements of atmospheric composition are taken over a 24 h period every third day (Malm et al., 1994). Depending on the measurement method at the site, 24 h average concentrations are provided daily, every third day, or every sixth day at EPA-AQS sites. To maximize our data availability, we use measurements from sites using the Federal Reference Method and the Federal Equivalent Method (FRM/FEM; 88101) as well as non-FRM/FEM (88502) sites (both are also used by the EPA for AQI summaries).

We determined the temporal correlations between the daily surface measurement and the internet behavior datasets at every site. However, in the “Results and discussion” section, we only show example time series for four of these locations. These four locations are shown because they were all impacted by wildfire smoke during the study period, but the response in the percent of Facebook posters varied among the sites, likely due to differences in surface concentrations, distance to fire, population, and cloud cover (discussed in “Results and discussion”).

Several models and model frameworks are also routinely used to estimate smoke exposure. Here, we use a chemical transport model, the Weather Research and Forecasting model with Chemistry (WRF-Chem). The simulation was completed for 5 June to 1 October 2015. We use Global Forecast System (GFS) meteorology, biogenic emissions from the Model of Emissions of Gases and Aerosols from Nature (MEGAN; Guenther et al., 2006), National Emissions Inventory 2011 (NEI) anthropogenic emissions, FINN biomass burning emissions (Wiedinmyer et al., 2011), MOZCART aerosol species and chemistry, and (MOZART) chemical boundaries (Emmons et al., 2010). Horizontal resolution is 15 km and there are 27 vertical levels. Concentrations are output for each model hour, which we then average to provide daily 24 h average surface concentrations in order to compare to the “Percent of Facebook posters” dataset and surface measurements.

We present a test case to evaluate the feasibility and usefulness of including the “Percent of Facebook posters” dataset in a statistical model. We compare two geographically weighted regression (GWR) models that use MODIS AOD and WRF-Chem PM2.5 with and without the “Percent of Facebook posters” dataset. GWR has previously been used in several different studies to predict surface air quality (Hu et al., 2013; Lassman et al., 2017; Song et al., 2014; You et al., 2016). For our test case, we focus on Washington state because of the extensive network of surface PM2.5 measurements available for validating results. In our regression model, we determine the dependent variable (surface PM2.5 at each measurement site) from a linear combination of these different predictor variables (MODIS AOD, WRF-Chem PM2.5, and gridded percent of Facebook posters). A separate set of regression coefficients is determined at each surface monitor location, which is then interpolated across the domain. We use the leave-one-out cross-validation (LOOCV) method to test our models, in which the regression coefficients determined at a single monitor are removed from the interpolation scheme. The resulting PM2.5 predicted by the regression model is compared to the observed PM2.5 concentrations. We calculate the temporal correlation, slope, and mean absolute error (MAE) for the two regression models.

An example of the data used in this study is given in Fig. 1 for 29 June 2015, which shows a dense smoke plume from wildfires in Canada causing degraded air quality over the midwestern US and smoke from local fires in the northwest over Washington, Oregon, and Idaho. The impact of this smoke plume is evident in the HMS smoke product, the anomalously high surface PM2.5 concentrations, the elevated MODIS AOD values, and the WRF-Chem PM2.5. The spatial pattern in the percent of Facebook posters is somewhat consistent with regions of degraded surface air quality, suggesting that some people were aware of the degraded air quality. The extent of the “Facebook plume” does not extend as far east or south as the smoke plume observed by the satellite products (MODIS AOD and HMS smoke product), and hot spots in the percent of Facebook posters are centered around the border between eastern Montana and Canada. It should be noted that the surface measurements also do not show a strong increase in surface concentrations as far south (Missouri and Arkansas), suggesting that the plume observed by the satellites might have been lofted above the surface. Additionally, while the HMS smoke product suggests only light smoke over northeastern Montana and MODIS AOD is only moderately higher than the surrounding region, surface PM2.5 concentrations are elevated, which agrees with the spatial pattern of Facebook posters. In cases of a lofted plume or smoke concentrated at the surface, this new dataset might be more representative of surface air quality changes than these satellite products.

In Fig. 2, we also show example time series of the percent of Facebook posters and other datasets (surface PM2.5 measurements, MODIS AOD, MODIS CF, HMS smoke product) used in this study for four different locations in the western US: Fort Collins, CO; Pinehurst, ID; Bellingham, WA; and Great Falls, MT. All four of these locations were impacted by wildfire smoke during the study period, but the response in the percent of Facebook posters varied among the sites, likely due to differences in surface concentrations, distance to fire, population, and cloud cover. From these time series, we see the two main fire event periods that impacted large areas of the US during the summer of 2015: (1) the Canadian wildfires in late June through early July and (2) the wildfires in the northwestern US (mainly Washington and Idaho) in August. The magnitude of impact on these different metrics for estimating air quality varies by location and event. For Pinehurst, ID, where the population was ∼ 1600 in 2015, population weighting the Facebook poster time series improves the correlation with the 24 h average surface measurements (R2= 0.55 for gridded and R2= 0.00 for raw). In more populated regions, such as Fort Collins, CO (pop. ∼ 161 000), Bellingham, WA (pop. ∼ 85 000), and Great Falls, MT (pop. ∼ 60 000), population weighting the Facebook posters has little impact on the time series and resulting correlation with the surface measurements (as shown in Fig. S3). A further discussion of these time series is presented throughout this section.

In order to assess how well changes in the fraction of people posting about smoke and air quality on Facebook represent actual changes in surface air quality, we compare time series of the percentage of Facebook posts matching our criteria to time series of PM2.5 measured at all of the different surface sites across the summer of 2015, such as shown in the example time series in Fig. 2. The coefficients of determination for all surface PM2.5 measurement sites with the gridded, population-weighted Facebook posts are shown in Fig. 3a, which suggests that the best agreement between the two datasets is in regions that experienced heavy smoke and/or anomalously high PM2.5 concentrations during the summer. This is to be expected based on our search criteria. For example, the Mt. Hood IMPROVE site in Oregon (Fig. 3) had 39 measurement days (5 June to 30 September) and 14 days on which the HMS smoke product suggested smoke over the location. This site provides the best R2 between the percent of Facebook posters and the measured surface PM2.5 with a value of 0.97.

We also compare the agreement of the percent of Facebook posters against simulated concentrations from a chemical transport model simulation (WRF-Chem; Fig. 3b), which again shows the highest correlation in the northwestern US. The area was impacted by wildfire smoke for many days in the summer of 2015. We would expect this as our Facebook post search criteria are aimed at smoke and poor air quality and would likely only show changes in postings in regions where air quality was noticeably degraded.

Agreement between MODIS AOD and Facebook posting trends is shown in Fig. 3c, which also shows the best agreement in the northwestern US. Because thick smoke can occasionally be classified as cloud in the MODIS algorithm (van Donkelaar et al., 2011), we filter out MODIS AOD observations for which the cloud fraction was > 75 %. The impact of this filtering is shown in the time series in Fig. 2. The criterion reduced our number of useable observations but improved correlations at most sites (Fig. S4). Comparisons between Facebook posters and MODIS AOD are spatially similar to WRF-Chem PM2.5 and surface measurements, but the coefficients for MODIS AOD and Facebook posts are generally worse. However, this satellite product is derived for the full atmospheric column and is not necessarily directly relatable to surface concentrations. Smoke plumes (and transported pollution from other sources) can be lofted above the surface and may not impact surface-level exposure where astute Facebook posters would take notice.

Finally, we show R2 for the values estimated by the HMS smoke product and the Facebook posters in Fig. 3d. Again, we see similar trends with the best agreement occurring in regions that experienced numerous smoke days. The correlation values are not as high as for MODIS AOD or WRF-Chem PM2.5. The HMS smoke product only provides estimates for smoke, which is the primary focus of our search criteria, although it also includes phrases related to general air quality degradation. Additionally, as with MODIS AOD, the HMS smoke product may not be representative of actual surface-level exposure. Finally, the HMS smoke product only provides categorical estimates of “heavy,” “moderate,” or “light” smoke and likely cannot represent subtle changes in exposure concentration levels compared to MODIS AOD.

While we have shown that our new dataset often correlates well with more traditional datasets that have been used to estimate smoke and/or PM2.5 concentrations and exposure, we also investigate whether the percent of Facebook posters can be used to improve estimates when combined with the other datasets. In Fig. 4, we compare how well each dataset estimates PM2.5. We show the coefficients of determination for Facebook posters (4a; similar to 3a but for days where CF < 0.75), MODIS AOD (with CF < 0.75; Fig. 4b), the HMS smoke product (Fig. 4c), and WRF-Chem PM2.5 (Fig. 4d) with the surface monitors. From Fig. 4, we can evaluate which dataset best correlates with surface measurements in different regions of the western US.

We summarize these initial findings in Fig. 4e, which shows the dataset that was best correlated with the surface measurement at each site (and the R2 had to be greater than 0.5). This figure shows that our “Percent of Facebook posters” dataset is better correlated with actual surface measurements at most sites in our domain for the given time period (5 June to 30 September 2015) compared to other datasets that are typically used to estimate exposure. We find that MODIS AOD and WRF-Chem PM2.5 are better predictors in regions with low populations, such as North Dakota, eastern Montana, and eastern Washington. Additionally, WRF-Chem PM2.5 and MODIS AOD are better predictors over much of the eastern US (not shown; R2 values all less than 0.5), which is dominated by anthropogenic emissions during the time period. These “normal” day-to-day changes in anthropogenic pollution may be less likely to be picked up by our Facebook post search criteria. We did not optimize the configuration of our WRF-Chem simulation to match surface observations. Changing emissions, meteorology, parameterization choices, grid resolution, or time steps may have improved surface concentration estimates, but the optimal configuration would likely differ by region and time period. However, our results shown in Fig. 4 suggest that Facebook posting could be used to help estimate exposure in conjunction with the other datasets.

However, if the aggregate percent of Facebook posters is used to estimate exposure, there may be a few limitations. While trends in Facebook posting seem to represent the variability in surface air quality over our study period at many sites, there is not a simple relationship between posting and PM2.5. There did not appear to be a threshold PM2.5 concentration at which it was guaranteed that people would start posting, region wide or in individual cities (e.g., there were cases with high smoke but little posting, such as the July event in Fort Collins, CO). There are several potential reasons for this. (1) As noted, on cloudy days, people may not be able to distinguish poor air quality, especially if it is from long-range transport where residents are not aware of a nearby fire. (2) There could be a point of saturation or response fatigue; people experiencing multiple days of smoke may find it less interesting to post about, or they could experience a cognitive bias causing them to perceive improved air quality in comparison to previous air quality. To test this, we looked at the time series of the ratio of the percent of Facebook posters to surface concentrations, and this ratio does appear to decrease over time during smoke events lasting several days. A decrease throughout the season is only evident at a few sites, although this is difficult to compare because the major smoke event at most sites occurred in late August or early September with few to no smoke events occurring afterwards. (3) We noted that regions with a high Facebook posting percent were occasionally centered over areas where the population had experienced poor air quality on preceding days rather than the current regions of poor air quality. This time shift could suggest a lag in either individual awareness or in the time it takes to spread information among community-level social networks. Additionally, there could also be persistence in Facebook posts; air quality might improve in a location, but people are still posting about it. Conversely, awareness of events could spread through social networks more quickly than an air quality event (such as a smoke plume) is transported such that individuals discuss an event before it impacts them. Quantitatively, this is difficult to assess as it may be more event related than season specific. We compared ±1-day lag correlations between Facebook posts and surface measurements for all sites that had daily measurements (as opposed to every third day). Using the same day provided the best correlation at ∼ 90 % of sites. Slightly better correlations were found using the previous day's measurement at several sites in Utah, and using the following day produced better estimates at several sites in Washington and Oregon, where there were broad regions and extended periods of degraded air quality due to local fires.

We included the CF criterion for the above analysis for all datasets. We found that filtering out days with high CF improved the agreement of Facebook posts and MODIS AOD (Figs. 2 and S5). This led us to also hypothesize that people may have difficulty distinguishing poor air quality on cloudy days, especially farther downwind of a source. To test this, we also sampled the percent of Facebook posters and surface measurement time series at each site (with filtering) using the MODIS cloud fraction. Compared to correlations between surface measurements and Facebook posts for the full time period, using only the days with CF < 0.75 improved correlations most noticeably at sites that were generally more than 500 km downwind of fires (such as in Colorado, Wyoming, and Utah; Fig. S5) but had less impact at sites closer to the 2015 wildfires (Oregon, western Montana, Washington, and Idaho; see Fig. S1a for fire locations). Cloudiness as a possible impact on Facebook awareness is seen in the time series for Fort Collins, Colorado in Fig. 2a. Although concentrations were greater during the July event than the August event, the response in Facebook posts was much lower. Bellingham, WA was also impacted by smoke during the same period in July. Although lower surface concentrations were measured, the response in Facebook posts was greater. We noted that during the July event, however, the MODIS product reported a cloud cover of 100 % over Fort Collins. For the full time period, filtering out days with CF > 0.75 improved the R2 between Facebook posts and surface measurements in Fort Collins from 0.33 to 0.54. Alternatively, in Great Falls, MT, which had many nearby fires, filtering only changed the R2 from 0.77 to 0.79 even though roughly the same number of days met the 0.75 criteria for exclusion.

We compared Google Trends data to surface measurements of PM2.5. Our results are shown in Fig. 5 for each search term. As with the aggregate “Percent of Facebook posters” dataset, correlations are best in the northwestern US, specifically Washington, Montana, and Oregon, which are states that were heavily impacted by smoke in 2015. Although we compare to total PM2.5, the best correlations were found not only for “air quality”, but also “wildfire” and “smoke”; as with the Facebook posters, we might expect this since wildfire smoke was the source of the most variability in surface PM2.5 during this time period. The search terms that are more related to urban pollution (“pollution”, “smog”, “haze”, and “ozone”) have much lower correlations, and sites that do have R2 > 0.1 are generally in urban areas or far downwind of smoke. “Ozone” in particular was not well correlated with PM2.5 measurements (all R2 < 0.22), which should be expected since ozone concentrations and PM2.5 concentrations are not always well correlated (e.g., Reisen et al., 2011).

We used the Google Trends data to analyze our Facebook search term criteria because we were not able to do this within the “Percent of Facebook posters” dataset. We chose several words that might be associated with “air quality” and determined the correlations between each word for each DMA as shown in Fig. 8. As with the actual concentrations of PM2.5, we find that “air quality” is generally more associated with “smoke” and “wildfire” than words more commonly associated with urban sources like “smog”, “haze”, “pollution”, and “ozone”. Sachdeva et al. (2016) found that the distance from the fires impacted the content of postings about the fire, and we also note some differences in our correlation maps based on distance. For example, closer to the fires (WA, OR, ID, MT), “air quality” is more associated with “smoke”. Farther away (CO, NV, UT, WY), “air quality” is more associated with “wildfire”. At these sites, “air quality” is also better correlated with “wildfire” than “smoke”, which may suggest that people are aware of the impact of the wildfires on air quality, but not able to see smoke. However, Google Trends is scaled by popularity in each region and data are only available on very popular terms. This could lead to a discrepancy in that the same number of people may be searching for these terms in different regions, but the relative popularity may be very different compared to other search terms, especially if there are other physical sources of “smoke” or impacts on “air quality” in a region. “Ozone”, “smog”, and “pollution” (terms that may be more associated with urban air pollution) are not well correlated with “air quality”, “smoke”, or “wildfires” over our study period; however, “haze” is moderately correlated in WA, OR, and CO (Fig. 6).

As a first test case to evaluate the usefulness of this aggregate “Percent of Facebook posters” dataset in a statistical model, we compared two geographically weighted regression model estimates using MODIS AOD and WRF-Chem PM2.5 with and without the Facebook posters. From Fig. 4, we see that WRF-Chem PM2.5, MODIS AOD, and the “Percent of Facebook posters” dataset are all correlated with surface PM2.5 in Washington state, and the best-correlated variable varies between surface sites. Therefore, a regression model could allow us to leverage the strengths from each dataset to create an improved estimate.

In Fig. 7, we show the results for our regression models with and without the Facebook posts. We see that including the Facebook posts in the regression model leads to improved R2 values at many of the sites in Washington (only one site shows a decrease; Fig. 7e). Additionally, for the full dataset (of all sites and all days), there is an improved R2 (0.66 compared to 0.58) and slope (0.60 compared to 0.52) with a smaller error. While these improvements may be small, we find this is in part because the “Percent of Facebook posters” dataset explains much of the same variability as WRF-Chem PM2.5 (and better explains variability in the urban region around Seattle, WA). We did not account for cloudy days in our regression analysis. Including information on cloud cover could potentially improve our regression model, which will be investigated further in ongoing work on this analysis.