Pollution is one of the greatest global killers, affecting above 100 million souls. That’s similar to global illnesses like malaria and HIV, according to the World Health Organization (WHO), which mentions that 4.2 million deaths each year happen as a consequence of ambient (outdoor) air pollution. Moreover, 95% of the global population are exposed to average particulate matter concentrations, which exceed the WHO prescribed limit of 10 micrograms per cubic meter, according to ‘our world in data.
We want to see whether TADA can predict pollution peaks based on a dataset comprising 602 daily air quality records. They include 19 fields of information, which can be summarised as:
The prediction of pollution peaks has been traditionally made using mathematical models.
TADA’s Machine Learning platform can help meteorologists predict pollution peaks before their occurrence. A well-anticipated pollution peak gives the means to take proactive actions to protect populations: ask fragile people to stay home and limit traffic.
It poses the following meteorology question:
Can a pollution peak be anticipated before it occurs?
TADA manages to predict pollution peaks with a 90% accuracy and a 95% AUC in this classification case. Air pollution is a significant problem, especially in more urban areas. It is typically caused by fossil fuel combustion from the transportation and industrial sectors, which emit harmful pollutants such as PM2.5 and nitrogen dioxide. While people from all walks of life are affected, the most polluted areas are typically developing. As of 2019, the most polluted country globally is Bangladesh, which has dangerously high PM2.5 particulate matter concentrations. In this same year, Delhi, India, was ranked as the world’s most polluted capital city for the second year. People worldwide suffer from high air pollution exposure, causing millions of premature deaths every year.
TADA has selected the following four main criteria out of the 19 available in the dataset:
It is interesting to note that the ozone levels are an essential indicator of incoming pollution peaks, combined with the season.
In one week, meteorologists were equipped with an accurate model predicting pollution peaks; they managed to: