1. Electric trains wont generate diesel particulate, but will generate road noise.
They don't generate diesel particulate, by they do generate "other PM10 and finer particulate", from the brakes for example.
In Paris there's a lawsuit against the RATP (local transit authority) for particulate pollution inside the stations. [0]
There's also the fact that, at least in Paris, some metros are running on tires. For those that run on metal wheels and rails, some turns are quite sharp, and you can hear the screeching of the metal. This is likely to contribute to particulate pollution.
I suppose for overground trains, this is less of an issue.
Except road noise was associated with dementia and train noise wasn't, which suggests it's pollution related according to your (1)
The other problem is that people that live near noise are poorer. Did they control for wealth?
These epidemiological observational studies are so hard to take seriously. I get why so many of them are done though. It's a relatively lower effort way to get publications out the door, all you need is a dataset and a few days in python.
Of course they controlled for wealth (individual income, occupational status, highest attained education and neighbourhood socioeconomic status).
It's hard to take seriously HN commenters who assume people working outside software development are all idiots who need to be told how to do their job.
Epidemiological studies in general are flawed and hard to trust due to omitted variables that you don't know about, p-hacking and publication bias making it difficult or impossible to interpret causality. You say "of course" as if it's a foregone conclusion that the results should be trusted and the authors have done a good job, when the default should be disbelief and scepticism in this particular approach unless shown otherwise with unusually strong results. I only have a positive reaction and belief by default in large scale RCTs. Epidemiological studies have not earned the benefit of the doubt. At best they are indications of future experimental research directions, i.e. they are a means to an end.
I've worked in academic applied statistics research and seen how the cookie crumbles, it's not pretty, and yes there are groups and industries of people who may be individually intelligent but nevertheless act like idiots given the incentives they face to churn out low-effort publications. Search for "hegemony" or "Fuzzy neural net Dow Jones" in Google Scholar to see 140 IQ people mass produce drivel. Just because I am not trained in medicine doesn't mean I am not allowed to draw this conclusion, I have years of experience in statistics and know a bullshit application of these tools when I see it, and I don't appreciate appeals to authority or other arguments that try to invalidly shut down people's opinions without knowing why those opinions were formed in the first place.
Comments
Two ways I can think of:
1. Electric trains wont generate diesel particulate, but will generate road noise.
2. It's relatively straightforward to measure pollution and noise, so you can do analysis to try and separate them.
In many situations these would not be correlated exactly. For example, terrain, wind patterns, noise dampening structures, etc.
They don't generate diesel particulate, by they do generate "other PM10 and finer particulate", from the brakes for example.
In Paris there's a lawsuit against the RATP (local transit authority) for particulate pollution inside the stations. [0]
There's also the fact that, at least in Paris, some metros are running on tires. For those that run on metal wheels and rails, some turns are quite sharp, and you can hear the screeching of the metal. This is likely to contribute to particulate pollution.
I suppose for overground trains, this is less of an issue.
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[0] https://www.rfi.fr/en/france/20210323-french-ngo-sues-paris-...
They could adapt the system of
[1] https://en.wikipedia.org/wiki/Montreal_Metro#Design
Popcorn!
Except road noise was associated with dementia and train noise wasn't, which suggests it's pollution related according to your (1)
The other problem is that people that live near noise are poorer. Did they control for wealth?
These epidemiological observational studies are so hard to take seriously. I get why so many of them are done though. It's a relatively lower effort way to get publications out the door, all you need is a dataset and a few days in python.
Of course they controlled for wealth (individual income, occupational status, highest attained education and neighbourhood socioeconomic status).
It's hard to take seriously HN commenters who assume people working outside software development are all idiots who need to be told how to do their job.
Epidemiological studies in general are flawed and hard to trust due to omitted variables that you don't know about, p-hacking and publication bias making it difficult or impossible to interpret causality. You say "of course" as if it's a foregone conclusion that the results should be trusted and the authors have done a good job, when the default should be disbelief and scepticism in this particular approach unless shown otherwise with unusually strong results. I only have a positive reaction and belief by default in large scale RCTs. Epidemiological studies have not earned the benefit of the doubt. At best they are indications of future experimental research directions, i.e. they are a means to an end.
I've worked in academic applied statistics research and seen how the cookie crumbles, it's not pretty, and yes there are groups and industries of people who may be individually intelligent but nevertheless act like idiots given the incentives they face to churn out low-effort publications. Search for "hegemony" or "Fuzzy neural net Dow Jones" in Google Scholar to see 140 IQ people mass produce drivel. Just because I am not trained in medicine doesn't mean I am not allowed to draw this conclusion, I have years of experience in statistics and know a bullshit application of these tools when I see it, and I don't appreciate appeals to authority or other arguments that try to invalidly shut down people's opinions without knowing why those opinions were formed in the first place.