![\"urbanair_logo-04\"](\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2014\/03\/urbanair_logo-04.png\"){kind=logo}
Using a diversity of big data to infer and predict fine-grained air quality throughout a city, and finally tackle air pollutions.<\/p>\nUsing a diversity of big data to infer and predict fine-grained air quality throughout a city, and finally tackle air pollutions.<\/p>\n
\u00a0<\/span><\/span><\/p>\n http:\/\/urbanair.msra.cn\/<\/strong><\/a>\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0Install Mobile Apps<\/strong><\/a><\/p>\n Many countries are suffering from air pollutions. Many cities have built a few\u00a0air quality monitoring stations to inform people urban air quality every hour. Influenced by multiple complex factors, however,\u00a0urban air quality is\u00a0highly skewed in a city, varying by locations significantly and changing over time differently in different places. Thus, we\u00a0do not know the air quality of a location without a monitoring station. We do not what the air quality\u00a0at a place will be tomorrow either, let alone the root cause the air pollution.<\/p>\n This project aims to predict the fine-grained air quality of current time throughout a city and forecast the air quality of future time at each monitoring\u00a0station. We also expect to identify the root cause of air pollution. For example, what’s the proportion of PM2.5 in the environment\u00a0derived from vehicular emission. what is the spatio-temporal causality interaction between the air pollutions\u00a0of different cities?<\/p>\n Led by Dr. Yu Zheng<\/a>, Urban Air is also a sub-project of Urban Computing<\/a>, which is a research theme that aims to tackle big challenges in cities by using big data.<\/p>\n\t\t\t![\"urbanair_getworse_small\"](\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2014\/03\/urbanair_getworse_small.gif\")
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