Data Case Introduction

Particulate matter (PM) is the term used for a mixture of solid particles and liquid droplets found in the air. In particular, fine particles with a diameter less than 10 µm are called PM10. Prediction of PM10 is an important issue in control and reduction of pollutants in the air. Predictive models for PM10 vary from extremely simple to extremely complex, but the ability to accurately forecast PM10 concentration index remains elusive. Prediction of particulate matter with a diameter less than 10 µm (PM10) is an important issue in control and reduction of pollutants in the air. In order to do so, the dataset chosen should be carefully considered and updated properly with data that research suggests.

 

 

Goal

This example aims to achieve results in predicting the PM10 high peaks of concentration and forecast the pollution level. Still, to be as accurate as possible and to have a maximum value, we would like to predict those peaks and concentration levels within a 24-hour period.  The area that the research will be focused on and the data is supporting is Bulgaria, where public factors are available to you that consists of meteorological data: weather, humidity, wind; traffic data; PM10 data; et cetera. Add more data that you might consider as significant for the more accurate predictions of PM10 concentration levels and forecast of the PM10 pollution.

Details

More information about the Telelink case can be found at www.datasciencesociety.net/the-telelink-case-one-step-closer-to-a-better-air-quality-and-city/