How North India's pollution levels sound through musical notes

Explained in one graph and sound.

 |  2-minute read |   04-11-2016
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How do you represent data? Through images and graphs? But what about our other senses, such as listening? Have you ever wondered how data would sound? We tried representing air pollution data of three cities - Delhi, Varanasi and Patna - in sound through data sonification.

Data sonification means representing data as non-speech sound. The basic principles are similar to visualisation, but where visualisations use elements such as lines, shapes, and colours, sonification relies on sound properties such as volume, pitch, and rhythm.

Visibility remained extremely low in New Delhi and NCR on November 4 morning even as smog after Diwali continues choking the capital. This year, India’s air quality was among the world’s worst. Furthermore, it was 40-100 per cent worse in five north Indian cities than at the same time the preceding year, according to global air pollution data.

In this experiment, we took the last ten days' data from Delhi, Varanasi and Patna's Air Quality Index (AQI). For example, on October 23, 2016 – one week before Diwali – Varanasi recorded a 24-hour average pm 2.5 level of 96 µg/m³, nearly half the level recorded last year.

Similarly, average pollution levels in Agra and Delhi were 38 per cent and 30 per cent lower respectively, than those recorded on the corresponding day in 2015 (November 4, 2015). Lucknow was 11 per cent less polluted this year, while only Patna recorded 38 per cent higher levels, compared to 2015.

We examined the average of each city's AQI before and after Diwali and this is what we found:

pollu_110416013153.jpg
Delhi, Patna and Varanasi's Air Quality Index- before and after Diwali.

Wondering how this would sound through musical notes? Listen here:

High amplitude denotes the record high AQI in each city.

Also read: How Kejriwal can help Delhi breathe easy

Writer

Shadab Nazmi Shadab Nazmi @shadabnazmi

Senior sub-editor (Digital) at India Today, data miner.

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