I recently read an article where a blogger described their decision to start masking on the subway:

I found that the subway and stations had the worst air quality of my whole day by far, over 1k ug/m3, ... I've now been masking for a week, and am planning to keep it up.

While subway air quality isn't great, it's also nowhere near as bad as reported: they are misreading their own graph. Here's where the claim of "1k ug/m3" (also, units of "1k ug"? Why not "1B pg"!) is coming from:

EDIT: this post is based on a misreading of a graph; see Subway Particle Levels Aren't That High.

Back when I was still masking on the subway for covid (to avoid missing things) I also did some air quality measuring. I found that the subway and stations had the worst air quality of my whole day by far, over 1k ug/m3, and concluded:

Based on these readings, it would be safe from a covid perspective to remove my mask in the subway station, but given the high level of particulate pollution I might as well leave it on.

When I stopped masking in general, though, I also stopped masking on the subway.

A few weeks ago I was hanging out with someone who works in air quality, and they said subways had the worst air quality they'd measured anywhere outside of a coal mine. Apparently the braking system releases lots of tiny iron particles, which are bad for your lungs like any tiny particles. This reminded me that I'd looked at this earlier, and since I spend ~3hr in the system weekly (platform + train) it seemed worth going back to masking. I've now been masking for a week, and am planning to keep it up.

Airplanes are strange places. Whatever you have on board when you take off is the most you'll have until you land. Want a sandwich? You can only have one if there's one on board. There are many things people reliably want, such as some kind of food, drink, and entertainment. Perhaps headphones, a pillow, or a blanket. Airlines provide these, either for free or at some fixed cost. But people's desires are broad, airlines would like to make more money, and economically inclined people want to turn everything into auctions and markets. So...

Imagine you load up your seatback entertainment, and one of the options is a burrito. But there's only one. You can put in a bid, and 1/3 of the way into the flight the person who bids the most gets it. Surely on an airplane of two hundred people there's at least one person with an unusually strong desire for a burrito at 30k feet. And it's not just burritos: other meals, decent headphones, headphone splitters, airplane pillows, diapers, charging cables, cozier blankets, a range of snacks, etc. Anything airlines don't typically provide where there's a high chance that someone might want it.

People often think 'affordable' housing is much more expensive than it actually is, and then conclude it's a scam to make housing for rich people. But this is often based on a misunderstanding of how the prices are set.

Let's say a unit is "50% AMI" somewhere with an area median income (AMI) of $100k. You might think, and I've seen a bunch of people with this confusion, that units would rent for 50% of $100k: $50k/y ($4,170/month), but it's much cheaper than that.

Cross-posted from my NAO Notebook.

Manufacturers often give optimistic estimates for how much data their systems produce, but performance in practice can be pretty different. What have we seen at the NAO?

We've worked with two main sample types, wastewater and nasal swabs, but Simon already wrote something up on swabs so I'm just going to look at wastewater here.

We've sequenced with both Illumina and Oxford Nanopore (ONT), and the two main flow cells we've used are the:

Cross-posted from my NAO Notebook.

This is something I wrote internally in late-2022. Sharing it now with light edits, additional context, and updated links after the idea came up at the Microbiology of the Built Environment conference I'm attending this week.

Metagenomic sequencing data is fundamentally relative: each observation is a fraction of all the observations in a sample. If you want to make quantitative observations, however, like understanding whether there's been an increase in the number of people with some infection, you need to calibrate these observations. For example, there could be variation between samples due to variation in: