New! Sign up for our free email newsletter.
Science News
from research organizations

Tweets prove to be reliable indicator of air quality conditions during wildfires

Date:
July 26, 2018
Source:
USDA Forest Service - Northern Research Station
Summary:
Whether it is caused by wildfire or prescribed fire, smoke can have serious health ramifications. Scientists evaluated 39,000 tweets originating in California during the state's 2015 wildfire season to learn whether what people tweet can be used to predict air quality in areas affected by fire.
Share:
FULL STORY

Tweets originating in California during the state's 2015 wildfire season suggest that social media can improve predictions of air quality impacts from smoke resulting from wildfires and have the potential to improve rescue and relief efforts, according to research by two USDA Forest Service scientists.

The study by Sonya Sachdeva of the Forest Service's Northern Research Station and Sarah McCaffrey of the Rocky Mountain Research Station, "Using Social Media to Predict Air Pollution during California Wildfires," was recently published by the International Conference on Social Media & Society.

Whether it is caused by wildfire or prescribed fire, smoke can have serious health ramifications, including aggravating respiratory and cardiovascular conditions. In a previous study, Sachdeva looked at 700 tweets related to the King Fire in California and found that they were a reliable predictor of air quality related to that fire. In new research, Sachdeva and McCaffrey evaluated 39,000 tweets that included the names of the state's most destructive wildfires of the 2015 season.

"With wildfire seasons becoming longer and more people living in fire-prone areas, smoke is becoming a greater public health concern," Sachdeva said. "Models for predicting the extent and range of impact of smoke dispersion from wildfire events can be a critical tool in safeguarding public health, and we're finding that information people share in social media has great potential for improving those models."

Sachdeva and McCaffrey combined ground-based monitoring of fine particulate air pollution levels obtained from the Environmental Protection Agency (EPA) AirData air quality database with a topic model mapping the content of citizens' tweets. Tweets were geocoded so they could be associated with specific air quality monitoring stations. Tweets and air quality data were connected in time by using the date of the tweet and the daily fine particulate air pollution report by the EPA.

Twitter also offered insight into people's perspective on wildfire. When people were near a fire, their tweets were often focused on the status of the firefighting effort, concern for firefighters, and the status of evacuation orders. Further away, people were interested in the cause of fire. The study suggested that social media could help predict air quality in remote areas that are not monitored for air quality, and that tweets could also have potential in linking people who need help with people who have the resources to offer assistance.


Story Source:

Materials provided by USDA Forest Service - Northern Research Station. Note: Content may be edited for style and length.


Journal Reference:

  1. Sonya Sachdeva, Sarah McCaffrey. Using Social Media to Predict Air Pollution during California Wildfires. International Conference on Social Media & Society, 2018 DOI: 10.1145/3217804.3217946

Cite This Page:

USDA Forest Service - Northern Research Station. "Tweets prove to be reliable indicator of air quality conditions during wildfires." ScienceDaily. ScienceDaily, 26 July 2018. <www.sciencedaily.com/releases/2018/07/180726161029.htm>.
USDA Forest Service - Northern Research Station. (2018, July 26). Tweets prove to be reliable indicator of air quality conditions during wildfires. ScienceDaily. Retrieved December 21, 2024 from www.sciencedaily.com/releases/2018/07/180726161029.htm
USDA Forest Service - Northern Research Station. "Tweets prove to be reliable indicator of air quality conditions during wildfires." ScienceDaily. www.sciencedaily.com/releases/2018/07/180726161029.htm (accessed December 21, 2024).

Explore More

from ScienceDaily

RELATED STORIES