Inferring urban air quality based on social media

Yan dong Wang, Xiao kang Fu, Wei Jiang, Teng Wang, Ming Hsiang Tsou, Xin yue Ye

Research output: Contribution to journalArticlepeer-review

26 Scopus citations


Outdoor air pollution is a serious environmental problem in many developing countries; obtaining timely and accurate information about urban air quality is a first step toward air pollution control. Many developing countries however, do not have any monitoring stations and therefore the means to measure air quality. We address this problem by using social media to collect urban air quality information and propose a method for inferring urban air quality in Chinese cities based on China's largest social media platform, Sina Weibo combined with other meteorological data. Our method includes a data crawler to locate and acquire air-quality associated historical Weibo data, a procedure for extracting indicators from these Weibo and factors from meteorological data, a model to infer air quality index (AQI) of a city based on the extracted Weibo indicators supported by meteorological factors. We implemented the proposed method in case studies at Beijing, Shanghai, and Wuhan, China. The results show that based the Weibo indicators and meteorological factors we extracted, this method can infer the air quality conditions of a city within narrow margins of error. The method presented in this article can aid air quality assessment in cities with few or even no air quality monitoring stations.

Original languageEnglish (US)
Pages (from-to)110-116
Number of pages7
JournalComputers, Environment and Urban Systems
StatePublished - Nov 2017
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Geography, Planning and Development
  • Ecological Modeling
  • General Environmental Science
  • Urban Studies


  • Air quality
  • Ensemble model
  • Feature extraction
  • Sina Weibo
  • Social media


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