Source: a friend of mine recently took a 1h flight. She used a Radiacode 102 to measure the radiation dose rate (in units of nSv/h), due essentialy to cosmic rays. As expected, radiation dose rate grows with altitude. The pressure sensor of her smartphone provided the cabin pressure data, which can be considered a sort of proxy for altitude. Again, as expected dose rate and pressure are anti-correlated.
Tools: this plot was made with Python (pandas, matplotlib, numpy).
iamnogoodatthis on
Cool! You should look up the flight on flightradar24 and download the gpx to get the altitude data to add to the chart 🙂
(I don’t know if you need to subscribe to be able to do that or whether you get a few days’ history for free)
avgprius on
Pilots, especially fighter pilots have increased rates of cancer. Not having the thickest part of the atmosphere protecting you from solar radiation tends to do this. Especially since they are essentially in front of a giant windows, at least as a passenger you are mostly encased. Tradeoffs 🤷🏾♂️
udmh-nto on
Background radiation level looks low. Was the counter calibrated?
Hattix on
Cabin pressure is a poor proxy for altitude. The cabin is pressurised usually to 8,000 feet maximum so there’s a hard limit there. Most newer airliners will use a lower cabin altitude, usually 5,000 to 6,000 feet, while the aircraft itself will be cruising between 30,000 and 45,000 feet.
At the estimated 780 hPa here, the pressure altitude was 7,000 feet.
So why did the radiation dose plateau as the cabin pressure flattened off?
It didn’t! it continued to rise as the aircraft did, increasing from 400 nSv/h to over 700 nSv/h. It then plateaued as the aircraft reached maximum altitude.
5 Comments
Source: a friend of mine recently took a 1h flight. She used a Radiacode 102 to measure the radiation dose rate (in units of nSv/h), due essentialy to cosmic rays. As expected, radiation dose rate grows with altitude. The pressure sensor of her smartphone provided the cabin pressure data, which can be considered a sort of proxy for altitude. Again, as expected dose rate and pressure are anti-correlated.
Tools: this plot was made with Python (pandas, matplotlib, numpy).
Cool! You should look up the flight on flightradar24 and download the gpx to get the altitude data to add to the chart 🙂
(I don’t know if you need to subscribe to be able to do that or whether you get a few days’ history for free)
Pilots, especially fighter pilots have increased rates of cancer. Not having the thickest part of the atmosphere protecting you from solar radiation tends to do this. Especially since they are essentially in front of a giant windows, at least as a passenger you are mostly encased. Tradeoffs 🤷🏾♂️
Background radiation level looks low. Was the counter calibrated?
Cabin pressure is a poor proxy for altitude. The cabin is pressurised usually to 8,000 feet maximum so there’s a hard limit there. Most newer airliners will use a lower cabin altitude, usually 5,000 to 6,000 feet, while the aircraft itself will be cruising between 30,000 and 45,000 feet.
At the estimated 780 hPa here, the pressure altitude was 7,000 feet.
So why did the radiation dose plateau as the cabin pressure flattened off?
It didn’t! it continued to rise as the aircraft did, increasing from 400 nSv/h to over 700 nSv/h. It then plateaued as the aircraft reached maximum altitude.