Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.5194/acp-17-7541-2017 |
Status update: is smoke on your mind? Using social media to assess smoke exposure | |
Ford, Bonne1; Burke, Moira2; Lassman, William1; Pfister, Gabriele3; Pierce, Jeffrey R.1 | |
2017-06-22 | |
发表期刊 | ATMOSPHERIC CHEMISTRY AND PHYSICS |
ISSN | 1680-7316 |
EISSN | 1680-7324 |
出版年 | 2017 |
卷号 | 17期号:12 |
文章类型 | Article |
语种 | 英语 |
国家 | USA |
英文摘要 | Exposure to wildland fire smoke is associated with negative effects on human health. However, these effects are poorly quantified. Accurately attributing health end-points 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 (R-2 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. |
领域 | 地球科学 |
收录类别 | SCI-E |
WOS记录号 | WOS:000404049500002 |
WOS关键词 | GEOGRAPHICALLY WEIGHTED REGRESSION ; FINE PARTICULATE MATTER ; AIR-QUALITY ; WILDFIRE SMOKE ; MODEL ; MORTALITY ; RESOLUTION ; POLLUTION ; OZONE ; PM2.5 |
WOS类目 | Environmental Sciences ; Meteorology & Atmospheric Sciences |
WOS研究方向 | Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/30383 |
专题 | 地球科学 |
作者单位 | 1.Colorado State Univ, Dept Atmospher Sci, 1371 Campus Delivery, Ft Collins, CO 80523 USA; 2.Facebook, Menlo Pk, CA 94025 USA; 3.Natl Ctr Atmospher Res, 3450 Mitchell Lane, Boulder, CO 80301 USA |
推荐引用方式 GB/T 7714 | Ford, Bonne,Burke, Moira,Lassman, William,et al. Status update: is smoke on your mind? Using social media to assess smoke exposure[J]. ATMOSPHERIC CHEMISTRY AND PHYSICS,2017,17(12). |
APA | Ford, Bonne,Burke, Moira,Lassman, William,Pfister, Gabriele,&Pierce, Jeffrey R..(2017).Status update: is smoke on your mind? Using social media to assess smoke exposure.ATMOSPHERIC CHEMISTRY AND PHYSICS,17(12). |
MLA | Ford, Bonne,et al."Status update: is smoke on your mind? Using social media to assess smoke exposure".ATMOSPHERIC CHEMISTRY AND PHYSICS 17.12(2017). |
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