Forecasting air pollutant concentrations and identifying source regions

Matevž Ropret (2016) Forecasting air pollutant concentrations and identifying source regions. MSc thesis.

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Abstract

Air pollutants are hazardous to human health. Pollutants can be transported by air masses from one region to another. We were interested if we could use air mass movement to predict daily pollution concentrations and to visualize where this pollution came from. This area is rich in related work and there already exist methods that solve this problem. Our goal was to use machine learning to create new and better performing methods. We created two new methods. The first is based on a 2D grid, while the second is based on raw coordinate data. We compared these two methods with existing CF and RCF methods. Results show that some of our methods perform more than twice as good as existing methods. However, the results are still below our initial expectations. We cannot rely on source attribution visualization, because we were able to get it working only with the 2D grid method, which is not much better than existing CF and RCF methods. We also tried combing results of multiple stations in hopes that we could make better source attribution visualization, but this also performed worse than expected.

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