We wanted to create a solution which could predict health risks based on pollution metrics

What it does

AirCheck integrates pollution stats with crowd-sourced symptoms based on: - Location (City, Country) , Pollution metrics(Relative Humidity, Sulfur DiOxide ,Air quality), Occurrence and severity (Mild, Moderate, Severe) of symptoms of allergies and respiratory diseases.

The server database integrates symptoms with location based pollution information. Users are also given an approximate health hazard statistics of the current location or any other requested location.


  1. Build the global user database on health data

  2. Provide analyzed data on the occurance and severity of symptoms in areas of interest.

  3. Provide scrutinized prediction on health risks in particular zones .The prediction algorithm uses NASA's real time WorldView data .

How we built it

Challenges we ran into

  1. Analyzing NASA's worldview imagery

  2. Retrieving location without GPS

Accomplishments that we're proud of

  1. Retrieving Environmental Metrics from NASA's imagery

  2. Finding Correlation between symptoms and pollution statistics

What's next for airCheck

  1. Have substiantial impact on user perspective by using more adept prediction algorithm

  2. Use IBM BlueMix to crowdsource related data from twitter.

  3. This global database can be used for data mining in future researches for finding new correlations between air quality data and public health.

  4. Provide custom-tailored data to users to better inform him about his health conditions from our web-server.

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