Using Social Media to Detect Outdoor Air Pollution and Monitor Air Quality Index (AQI): A Geo-Targeted Spatiotemporal Analysis Framework with Sina Weibo (Chinese Twitter)

PLoS One. 2015 Oct 27;10(10):e0141185. doi: 10.1371/journal.pone.0141185. eCollection 2015.

Abstract

Outdoor air pollution is a serious problem in many developing countries today. This study focuses on monitoring the dynamic changes of air quality effectively in large cities by analyzing the spatiotemporal trends in geo-targeted social media messages with comprehensive big data filtering procedures. We introduce a new social media analytic framework to (1) investigate the relationship between air pollution topics posted in Sina Weibo (Chinese Twitter) and the daily Air Quality Index (AQI) published by China's Ministry of Environmental Protection; and (2) monitor the dynamics of air quality index by using social media messages. Correlation analysis was used to compare the connections between discussion trends in social media messages and the temporal changes in the AQI during 2012. We categorized relevant messages into three types, retweets, mobile app messages, and original individual messages finding that original individual messages had the highest correlation to the Air Quality Index. Based on this correlation analysis, individual messages were used to monitor the AQI in 2013. Our study indicates that the filtered social media messages are strongly correlated to the AQI and can be used to monitor the air quality dynamics to some extent.

MeSH terms

  • Air Pollutants
  • Air Pollution*
  • China
  • Cities
  • Environmental Monitoring*
  • Humans
  • Social Media*
  • Spatio-Temporal Analysis*

Substances

  • Air Pollutants

Grants and funding

This work is funded by the National Natural Science Foundation of China (Grant No. 41271399), China Special Fund for Surveying, Mapping and Geoinformation Research in the Public Interest（Grant No. 201512015), the Specialized Research Fund for the Doctoral Program of Higher Education (Grant No. 20120141110036), and the National Key Technology R&D Program of China (Grant No. 2012BAH35B03).