Our heavily modified app ran on a tiny, battery powered Raspberry Pi Zero computer tethered to the sensor’s serial port. The sensor in the Airbeam 2 is well-regarded, but shortcomings in its mobile app led us to fork an open source project that reads real-time data off the sensor. We settled on the Arduino-based Airbeam 2 sensor by HabitatMap, which took exposure measurements of the kids every five seconds as we documented their day with our cameras. We needed a small, mobile solution that let us keep up with our subjects. Most air quality monitoring hardware is big and stationary. To add to the challenge, there wasn’t a turnkey, commercial project that did what we needed. But the data we wanted didn’t exist, so we had to collect it. We usually build charts and other graphics with data given to us by outside experts. It’s unusual for The Times to collect data for a project like this. What was the hardest part of this project? The combination of deeply informed on-the-ground reporting and creative data-gathering and visualization helps make the project distinctive. It is important to note that just as important as specific technologies used were the months of on-the-ground reporting from reporters in the New Delhi bureau to identify appropriate families, gain entry to their homes and schools, and understand the broader social context behind Monu’s and Aamya’s lives. The timelines and side-by-side photos, using this new data, were made interactive in the browser with JavaScript, leaning heavily on the D3 JavaScript library. This new, combined data allowed us to generate all the time code-based visuals you see in the story. We used Google Sheets and a custom Node.js app to parse the various metadata and sync it all via their timecodes. The video files included sidecar metadata generated by the cameras. We recorded the air pollution data as CSV files. We worked with researchers from ILK Labs in Bangalore to design a data collection and processing protocol involving three types of portable air quality sensors and custom software running on a battery-powered Raspberry Pi computer. The visual contrast reflects a dispiriting reality: A long-term, consistent disparity like we observed that day could steal around five years more life from someone in Monu’s position, compared with an upper-middle-class child like Aamya.Īrden Pope, one of the world’s foremost experts on health and air pollution, called the piece “an engaging, important, and sobering story.” Scott Murray called it “the finest piece of data-driven visual journalism I have seen, ever, hands-down.” Most importantly, it is impossible to address health inequalities if they are not understood, and this piece provides an opening. We watch as both children brush their hair, hang out with friends and sit down for dinner, and see overlaid the spikes and valleys of their real-time pollution exposure. Monu, who lives in a slum and attends school outside, is exposed to about four times as much pollution as Aamya, whose school and home are guarded by air purifiers. We showed, moment to moment, what that exposure looks like. The most harmful pollutants are commonplace, legal and largely invisible. Few researchers have collected this data. While Delhi’s poor air quality is well-known, disparities in individual exposure based on class or circumstances are poorly understood. This project went to extraordinary lengths to make visible this dangerous reality. Impact reached:Įveryone does not breathe the same air. We measured the air pollution that two children in New Delhi breathed as we followed them around the city on a normal day to see how wealth inequality affected their exposure.
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