Category: Analysis

Anything that I have tested empirically, or explain with detailed calculations, or just generally bother to get all the facts straight.

Post #1813: Won’t be hiring ChatGPT as my AI staff writer, either.

 

AI: It’ll always give you an answer.  It might give you a very convincing answer.  And it might sometimes even give you the right answer.  But the last part is far from guaranteed.

Inconceivable!

In my ongoing quest to automate this blog, I threw my test questions at ChatGPT.  These questions are:

  1. Explain why ceiling fans are more efficient than box fans.
  2. Explain why box fans are more efficient than ceiling fans.

I already know the answer.  Ceiling fans are more efficient, and I already worked out why, in a post that I have currently withdrawn.  To move the same volume of air per minute, a small fan has to move the air much faster, and that eats up energy, compared to moving a larger volume of air, more slowly.  Ceiling fans are more efficient at moving a given volume of air simply because they are larger, so they can move that required volume of air at a much slower speed.

To save you the reading, ChatGPT produces nicer text, and more of it.  But at root, the answers had the same problems as Bard.ai.

  • Just like Bard.ai, ChatGPT will do its best to give you the answer you’re looking for.  Even if you’re wrong, as in the second question above.
  • Just like Bard.ai, ChatGPT conflates the definition of efficiency (CFM/watt) with the reason for efficiency.
  • Just like Bard.ai, I get a lot of extraneous stuff that I now realize probably came from various ads and marketing materials.  Aerodynamic blade shapes and whatnot.

Even when I tried to point ChatGPT in the exact direction I wanted — explain the physics of why ceiling fans are more efficient in terms of CFM/watt — I got back nothing useful.

Worse, when I gave it the “wrong ” question, and specifically worked it in terms of CFM/watt, damned if ChatGPT didn’t spit back the wrong answer, forcefully and clearly worded in terms of CFM/watt.

Worse yet, when I gave it a neutrally-worded question, it did in fact pick ceiling fans over box fans.  But the explanations were just so much nice-sounding nonsense.  It never did get down to the basic physics of it.

But even worse, when I gave it a different, simpler wording, of that neutrally-worded question (“Which is more efficient …”), it gave me the wrong answer.  And backed that up with nonsense reasoning.  (e.g., Box fans are more efficient in terms of CFM/watt because they are compact and portable.)  That’s the last fan question entry below.  So not only did it seem to give me either answer (ceiling is more efficient than box, box is more efficient than ceiling), but which answer I got appeared to depend on irrelevant and arbitrary changes in the wording of the question.

Finally, I gave it a couple of questions for which a) I have a well-researched and definitive answer, and b) for which I am a national expert.  Does the hospice benefit reduce Medicare spending?

The plain answer to that is no, but you have a whole lot of people who wish the answer were “yes”, and a whole lot of bad research that seems to give them that answer.  (Along with considerable research supporting the answer of “no”.) So when I asked ChatGPT, twice, what I got back was a highly selective reading of the scholarly literature, and the usual pat answers about why hospice saves Medicare money.

(It doesn’t.  Among other things, a third of Medicare hospice outlays are for those who are outside of their last year of life.  Other than my own study, typical research methodologies exclude these costs.)

My take on it at this point is that if you want bland filler, that does in fact say some stuff, about some defined topic, these writing AIs are fine.

If you want the contents to be factually correct?  Not so much.  The answer you get depends heavily on the question you asked.   Seemingly neutral changes to a neutral question got ChatGPT to flip its answer.  And for a separate question where I have expert knowledge, ChatGPT presented a selective reading of the literature that more-or-less mimicked the for-profit industry’s party line.

To me that says that you really can’t ask these writing AIs for reasoned analysis.  Or right and wrong.  At best, what you’re really asking them is one of two things:

  • Give me some arguments that support my point of view.
  • What does the majority of text, that has been input to this AI, say about a particular topic.

You may think you’re asking it to provide you with some true information.  But at root, what you’re actually doing is a lot closer to one of the two bullet points above.

This, by the way, dovetails with a friend’s report of AIs that had been trained to be racist, by groups of individuals who chatted with the AI using racist language.  The AI was just a reflection of what it had been fed.

If you are looking for any sort of deep explanation, you’re out of luck.  Even if you know what that deep explanation is.  In terms of explaining why ceiling fans are more efficient, none of these went much beyond the tautology of telling me that they are more efficient because they move more CFM/watt.  Which is, in this context, the definition of efficiency.

I wonder if this explains what I see on blog sites when I’m researching a topic in gardening.  On any given topic, a lot of sites will reproduce some bland version of the standard talking points.  Many of them don’t seem to have first-hand knowledge of the topic.  And for sure, almost none do what I do, and actually test things to see if they are true or not.  Maybe a lot of what I’ve been seeing is based on AI-generated text.  A lot of that text has the same bland, one-hand-or-other-hand style that seems to be the default style of ChatGPT.

In short, decent writing?  Yes.  Good sense?  No.

But, in hindsight, these are billed as writing AIs.  They write.  That’s what they do.  Plausibly, you could use them to generate multiple answers to a question, and you’d pick the one you liked.  But in no sense that I see can you trust them  them to answer a question, in terms of sorting fact from fiction.

The might get it right.  They might not.  They might waffle.  For sure, if you ask them the same question, enough ways, you might get back an answer that you like.

If the AI manages to hit the nail on the head, it provides you with cheap, well-composed text.  But it’s going to be up to you to judge whether the AI nailed it, or flubbed.

It’ll always give you an answer.  It might give you a very convincing answer.  And it might sometimes even give you the right answer.  But the last part is far from guaranteed. 

It boils down to this.  In trying to find an artificial intelligence to write my blog, I keep using “AI”, but I do not think “AI” means what you think it means.  For sure, AI writers don’t do what I had hoped they would do.

Here’s how it went.

Explain why ceiling fans are more efficient than box fans.

ChatGPT response, emphasis mine

Ceiling fans are generally considered more efficient than box fans due to several factors:
  1. Air circulation: Ceiling fans are mounted on the ceiling, allowing them to circulate air throughout the entire room. They create a downward airflow that creates a wind chill effect, making you feel cooler. This circulation helps maintain a consistent temperature in the room and reduces the reliance on air conditioning or other cooling systems. On the other hand, box fans are typically placed on the floor or a surface, and their airflow is limited to the immediate area in front of them.
  2. Coverage area: Ceiling fans have larger blades and are positioned overhead, allowing them to cover a larger area compared to box fans. They can effectively cool an entire room by distributing air more evenly. In contrast, box fans are smaller and have a limited range of coverage. They are better suited for localized cooling or for smaller spaces.
  3. Energy consumption: Ceiling fans are generally more energy-efficient compared to box fans. They consume less power to provide the same level of cooling due to their design and the way they circulate air. Ceiling fans typically use around 15-90 watts of electricity, while box fans can consume 50-200 watts depending on their size and settings. This lower energy consumption translates into cost savings and reduced environmental impact.
  4. Integration with HVAC systems: Ceiling fans can complement the existing HVAC (heating, ventilation, and air conditioning) system in a room. By using a ceiling fan in conjunction with an air conditioner, you can set the thermostat at a slightly higher temperature while still feeling comfortable due to the cooling effect of the fan. This can lead to significant energy savings by reducing the load on the air conditioning system. Box fans do not integrate with HVAC systems in the same way.
  5. Convenience and space-saving: Ceiling fans are fixed in place and do not require additional floor or surface space, making them more convenient and space-saving compared to box fans. They are also operated by a switch or remote control, allowing for easy adjustment of speed and direction. Box fans, on the other hand, need to be placed in a suitable location, and their portability can be a disadvantage in terms of convenience and aesthetics.
It's important to note that the efficiency and effectiveness of fans can vary based on the specific models, settings, and conditions. However, in general, ceiling fans tend to provide more efficient and widespread cooling compared to box fans.

Continue reading Post #1813: Won’t be hiring ChatGPT as my AI staff writer, either.

Post G23-040: Radiative cooling experiment, a puzzling worse-than-total-failure.

 

This is a simple controlled trial of whether I can get a few degrees F of “radiative cooling” in my raised beds, using radiant barrier.  See a few posts back for details.

In a nutshell, total failure.  And I have no clear idea why.

Continue reading Post G23-040: Radiative cooling experiment, a puzzling worse-than-total-failure.

Post G23-038: Tomato non-ripening and a radiative cooling experiment.

 

Non-ripening tomatoes and nighttime temperatures

In 2020, we had an extended period when tomatoes would not ripen.  That was new to me, but apparently that’s pretty common in the South.  The lack of ripening is due to excess heat.  But it’s not a daytime excess.  It’s due to warm nights, as many varieties of tomatoes will not begin the ripening process (enter the “breakers” stage) if nighttime temperatures consistently exceed 70 or maybe 72 F.  See Post #G22-43 for full details.

Source:  Calculated from historical weather data from NOAA, for Dulles International Airport.

As explained in that prior post, the non-ripening is a subtle thing.  Tomatoes that have already begun the ripening process will continue to ripen.  But those that have not yet started that will remain green.  So, at some ill-defined lag after the nights warm up, the supply of ripe tomatoes gradually dries up.

That “warm nights” thing is a pity, because climate models have long predicted that global warming will raise nighttime temperatures more than daytime temperatures.  So it would seem that warmer nights are in the pipeline.

If you look at the graph above, that 2020 stretch of warm nights began in the middle of July. 

Here’s the extended forecast for Vienna VA today:

It’s worth pointing out two things.

First, we’re surely in for at least a few nights above 70F.  And, depending on whom you believe (and your misplaced trust in 10-day forecasts), we might be in for an extended period with nighttime temperatures over 70F.

Second, it won’t take one whole lot of warming to push all those forecasts above the 70F threshold.  That’s going to make it tough to grow a whole lot of varieties of tomatoes around here, I think.  But we’re likely talking the better part of a century from now.  I hope.

Everybody talks about the weather, but nobody does anything about it:  Radiative cooling.

Not me.  I’m going to try a radiative cooling experiment.  I’m going to see if I can use radiant barrier to reduce the nighttime temperature in parts of my garden.

It’s an unusual idea, but it’s not rocket science.

As I noted in earlier posts on this topic (G21-014 ,  G21-015 ,  G22-005 , etc.) a garden bed is like a big window, looking straight up into outer space.  As such, it continuously radiates heat energy (long-wave infrared) upward, toward the cold of outer space.  By my calculation, on a cold spring night, you lose more heat from radiation than from conduction.

That’s why a radiant barrier is what you want, for frost protection, for your garden beds.  That can be a space blanket or similar material.  But that’s also why a glass cloche works to prevent freezing overnight.  And why a simple, thin-walled glass mason jar provides excellent frost protection for tender plants (Post G22-006).  And, by contrast, why polyethylene sheeting does diddly-squat to prevent overnight freezing (G22-005).

But, weirdly enough, you can also use a radiant barrier for cooling, by preventing ambient radiation from reaching your garden bed at night.  In effect, you make it so that your garden bed “sees” only the cold of outer space, directly over head.  If the air is sufficiently transparent to long-wave infrared, your garden bed then cheerfully radiates energy off into outer space, and cools as a consequence.

This technique works OK in the dry desert, with a clear sky, which may explain in part why various Middle Eastern cultures have used it for millennia, to make small amounts of ice, in the desert (reference).  That said, even under those optimal conditions, temperatures had to be near-freezing to start with.  This reference suggest an upper limit of 5 C, or about 41 F.  Ideally, a combination of insulation, evaporative heat transfer, and radiative losses would generate small amounts of ice, under those conditions.

By contrast, the main problem with using that here is water.  Water vapor is the most important greenhouse gas.  It’s plentiful in the atmosphere, and it absorbs and re-emits infrared across many parts of the infrared spectrum.  Between the humidity and the clouds, a lot of what gets radiated into space will be, in effect, reflected (re-emitted) right back down to earth.

Which is, in a nutshell, the greenhouse effect.

OTOH, I only need a few degrees.  If this can pull a 9F differential in the dry desert, maybe it can drop the temperature 3F on a cloudless Virginia summer night.  After all, I’m just trying to trick those tomatoes into starting the ripening process.  My understanding is, once that gets going, they will continue to ripen.

So it’s worth a shot, just out of intellectual curiosity.  I’m going to set up a small enclosure made of radiant barrier — basically, a big tube with the open ends facing ground and sky.  Cap that with a piece of clear polyethylene sheet to provide an IR-transparent barrier to the outside air.  Then use temperature loggers to track nighttime temperatures inside and outside the enclosure.   I might get lucky.

Otherwise …

The nice thing about this method is that there’s zero energy consumption.

Probably ought to consider a shade cloth, as well, but I can’t quite figure out how that would be much help in terms of nighttime temperatures.  Plausibly, the cooler the soil stays during the day, the cooler the area may be at night.

But if I’m willing to expend a bit of energy, I think a mist-cooling device would plausible achieve a sufficient drop in temperature.  Mist coolers work by converting sensible heat (temperature) into latent heat (water vapor, instead of liquid water).  I went over that in my post on the true energy cost of humidifiers, Post #1669.

That said, bathing my plants in mist all night just seems like a recipe for every tomato leaf disease known to mankind.  So that’ll only be used as a last resort.

Otherwise, short of sticking a window AC under a tarp, and using that, I guess I’m at the mercy of Mother Nature here. If it’s too hot to ripen tomatoes, then it’s too hot to ripen tomatoes.  Grow something else for the time being.

Any sufficiently advanced technology is indistinguishable from magic.  (Arthur C. Clarke)

My garden beds emit “black body” radiation.  That is, they toss out radiation at every frequency, with a peak in the long infrared.  As a consequence, some of that is bound to be absorbed and re-emitted by atmospheric gasses.

But suppose, through some miracle of modern science, you could create a material that radiates infrared only on those bands of frequencies where the atmosphere is transparent to infrared.  That is, frequencies that aren’t absorbed and re-emitted by common atmospheric greenhouse gasses.

Then — and frankly, this is where I lose it — you could, in theory, create a material that would literally cool itself below ambient temperature.  If the air outside is 80F, your miracle-o’-modern-science could be 78F, with no power input.  Just from enhanced “emissivity” in the right part of the spectrum.

Or, as these folks put it, emphasis mine:

... the PDRC coating demands a significant solar reflectance (Rsolar) in the spectral region (0.3–2.5 m) and a significant thermal emissivity (LWIR) in the environmental long-wave infrared (LWIR) propagation region (8– 13 m). As a result, during the day, the energy loss to frigid space ... is far more than the warming from daylight, resulting in electricity-free spontaneous refrigeration.

In other words, you could sit a piece of this stuff out in the sunshine, and it would remain cooler than the ambient air.  With no energy input.

That’s close enough to magic for me.

Post 1808: Some thoughts on AI, part 1.

 

AIs of various sorts seem to be able to do a lot of jobs that traditionally required the use of a human mind.  They are particularly good with text generation.  They can also easily do “literature reviews”, to offer a summary of current understanding or thinking regarding a topic.  Some say they are good at writing computer code, but I’d like to see evidence of that before I’d believe it. Continue reading Post 1808: Some thoughts on AI, part 1.

Post #1804: Speed limits in Fairfax County, how much slack?

 

Background:  A speed limit + 10 rule?

Source:  WTOP.

There was an article today on the local news-radio website (WTOP) regarding speeding in school zones.  I took note, because I routinely drive through one of those zones where Fairfax County VA operates speed cameras.  The zone is clearly marked, and you’d have to be blind to miss it, as shown above.

And yet:

In total, 23,431 cars were caught going 10 or more miles per hour above the speed limit in April, May and June combined.

I supposed I should be impressed by the sheer numbers.  But instead, a completely different figure caught my eye:  They only ticketed folks for going at least 10 MPH over the limit.

First, I thought it was intemperate of the reporter to note that exact figure.  Anybody reading the article realizes that there’s no risk of a ticket at anything up to the posted speed plus 9 MPH.  So, presumably, if generally known, that now becomes the de facto speed limit.

But second, I’d heard that same figure before, a few years back, in a discussion of red-light cameras and speeding cameras in Vienna, a town in Fairfax County.  There it was phrased as “we don’t ticket unless they are going at least 11 MPH over the posted limit.”  The explanation given at the time was that Fairfax County courts would not accept cases for any infraction less than that.

Is that the rule here, and if so, why?

Short answer is, yes and no.

In theory, by Virginia statute, you can get ticketed for traveling one mile an hour over the speed limit in a residential area.  This is my interpretation of § 46.2-878.2 of Virginia statute, which says :

Operation of any motor vehicle in excess of a maximum speed limit ... in a residence district of a county, city, or town ... shall be unlawful and constitute a traffic infraction punishable by a fine of $200, in addition to other penalties provided by law.

The black-letter law provides no slack.  If you’re in a residential district, on a highway (meaning, in Virginia, any public alley, street, road, or highway), and you’re going a mile over the limit, you can, in theory, be ticketed and will owe a minimum of $200.

Except that the law spells out a different set of penalties for drivers caught by speed cameras in school zones.  My interpretation is that because the process is, in effect, automated, they cut drivers a lot more slack than they would if they’d been pulled over, in person, by a uniformed officer.

In the case of speed cameras in a school zone (§ 46.2-882.1), emphasis mine:

1. The operator ... shall be liable for a monetary civil penalty ... if such vehicle is found ... to be traveling at speeds of at least 10 miles per hour above the posted ...  speed limit ... .  Such civil penalty shall not exceed $100 ...

...

4. Imposition of a penalty pursuant to this section ... shall not be made part of the operating record of the person upon whom such liability is imposed, nor shall it be used for insurance purposes in the provision of motor vehicle insurance coverage.

The bottom line

There appears to be no hard-and-fast rule regarding ticketing for how much “slack” you get, speeding in a residential area in Virginia.  The plain language of State law in Virginia law says that if you exceed the posted limit, you can be ticketed.  I believe that pre-empts any local law, as we are a Dillon Rule state.   That is, local governments can only make their own rules where the Commonwealth grants them permission to do so.  And nothing in Commonwealth statute appears to do that, with a few limited and explicit exceptions spelled out in the law.

Except that “ten miles over the limit” is written into law, in Virginia, for speed-zone cameras.  There, the Commonwealth leans heavily in the direction of protecting drivers’ rights, and avoiding Big Brother information harvesting.  So, in exchange for what is basically an automated process, you face a small fine.  There’s a monetary penalty, but (as I read it) no points on the license.

Extras for experts:  Two non-obvious reasons not to speed in residential areas.

First, if you are speeding, in Virginia, you lose any claim to having right-of-way.  So if some bonehead does something to get you in a car accident, where you had the right of way, but you were speeding at the time … tough luck.  You cannot claim right-of-way while you are speeding.

§ 46.2-823. Unlawful speed forfeits right-of-way.

The driver of any vehicle traveling at an unlawful speed shall forfeit any right-of-way which he might otherwise have under this article.

The reason for that is pretty clear.  Traveling at excess speed makes it difficult for other drivers to judge whether or not an accident will occur.

We had a horrific accident in this area, last year, that is a classic illustration of that.  The culprit was a bozo who was driving a BMW about 80 MPH in a 35 MPH zone.  His car got struck by a car turning left, and his car subsequently jumped the sidewalk and killed two high school student who were on the sidewalk, walking home from school.

Normally, the car going straight has the right of way.  Should the car turning left have therefore been charged with the accident, for failure to yield right-of-way?   I don’t think any sane person would suggest that.  If nothing else, on a curved road, excessive speed of that magnitude more-or-less prevents drivers from seeing you coming in time.  The accident was entirely the fault of the speeding driver.

Second, if you speed significantly in areas with red lights, you will run red lights. 

Not may, will.

This point is courtesy of Road Guy Rob on YouTube.  Yellow light duration is set based on expected traffic speed.  (Plus regional variation, I guess).  High-speed roads have long yellow lights, low speed roads have short yellow lights.  In both cases, the length of the yellow allows drivers that are far from the intersection to stop before the light turns red.

If you drive at high speed, on a low speed road, there will be a stretch of pavement, and a rage of excess speeds, so that if you see the yellow light while you’re in that zone, you will literally be unable to avoid running the red.  That’s because, between your reaction time and the car’s stopping distance, your car will travel much further than the engineers who set up the light expected.  If you are within just the right range of excess speeds, if you see the light turn yellow, you both a) can’t make it through the intersection before the light turns red, and b) can’t stop before entering the intersection.  No  matter what you do — hit the brakes, hit the gas — you go through the red light. 

And so, at some level, excessive speeding and running red lights go hand-in-hand.  There’s a certain pleasing symmetry to that.  My guess is, the folks who don’t care about the first, don’t much care about the second either.

Post #1803: Why are fine particulates (PM 2.5) so variable? It’s over my head.

 

One thing I’ve noticed about the AQI for particulates is how variable it is.  On any given day, my local hourly estimate from Accuweather will differ significantly from the EPA’s Airnow map.  Which, in turn, differs from readings just a few miles away.  For example, above, my AQI for particulates (as of 1 PM 7/6/2023 is either 63 (Airnow) or 33 (Accuweather).  Or somewhere between.

And readings within a few miles go as low as 13.  At the same time, the seemingly accurate meter I just bought shows “9”, sitting on my back screen porch.

At first, I chalked that up to instrumentation.  Maybe particulates are hard to measure, and what I’m looking at is more-or-less instrumentation error.

Because, serious, how could the air be so different, just a few miles away?  If I were to take some other measure of the atmosphere — temperature, humidity, pressure — it would vary smoothly over vast areas.   E.g., if it’s 90 degrees here in Vienna, VA, there isn’t going to be a pocket of 45 degree air five miles away in the City of Fairfax.  Yet you see that sort of apparent PM 2.5 disparity all the time.

So I thought, it must be poor instrumentation.  Then I bought a cheap air quality meter, noted above.  Not only are the readings stable from hour to hour, they are frequently in good agreement with the Accuweather numbers.  They clearly respond to ambient conditions in a hurry.  (The 4th of July fireworks briefly sent the meter into the “purple” AQI range, consistent with predictions from the Airnow map.)  The stated accuracy of the PM 2.5 measurement is +/- 10%.  All that, from a device that measures all five of the key air pollutants and costs under $75.

So, this isn’t due to instrumentation error.  Or shouldn’t be.  You can get reasonably reliable PM 2.5 measurements with a cheap off-the-shelf device.

Maybe my local variation is due to the presence of large local point-sources of PM 2.5.  But, to a large degree, we have no large point sources of particulate emissions in this area.  Largely because we are almost devoid of industry, in the DC area, and our power plants are (mostly) located outside of the metro area.

Which also matches my observation, because it’s not as if one area is consistently dirty.  It’s that the readings consistently vary a lot from place-to-place in this region.

So why do the PM2.5 readings in my area appear to be so highly localized?  Is there really that little mixing of the air between PM2.5 emitters, and local air?

Trying to understand how air mixes — a fool’s errand.

After about an hour of looking, I’m going to say that short of getting a graduate degree in atmospheric science, this ain’t gonna happen. 

It’s surprisingly complicated, but the joker in the deck is “turbulent mixing”?  Once I found out about that, I realized it was time to call it quits on trying to understand this.

First, physicists distinguish “bulk flow” (e.g., a breeze) from “diffusion processes” (molecules or particles moving through still air).  In this case, the latter would be the movement of water molecules or fine particulates through still air.

So, smoke spreads out because it 1) blows smoothly downwind, and because 2) the particles diffuse outward into surrounding clean air.

That said, it also spreads due to 3) turbulent mixing.  Any time the flow of air is not smooth (laminar, or layered), turbulent mixing is said to occur.  This sort of mixing can apparently distribute that smoke fully and more-or-less uniformly in the adjacent clean air.

Turbulent mixing occurs a lot in the atmosphere.  I’m pretty sure that it occurs at the level at which clouds form above the ground.  It occurs within clouds.  I occurs if sufficiently strong wind sweeps past fixed objects, e.g., tree branches.  And so on.  Anything sufficient energetic will kick the flow of the atmosphere from laminar flow to turbulent flow and turbulent mixing.

The bottom line is that there is no back-of-the-envelope way to determine how well PM 2.5 (including smoke) typically mixes into the surrounding atmosphere.  In the end, it’s all empirical, and depends on how hard the wind is blowing horizontally, how turbulent the atmosphere is in vertical profile, and so on.

Presumably, both water vapor and PM 2.5 move at the same speed, and mix at the same rate, when it comes to bulk transport and to turbulent mixing.  In both those cases, they are merely being carried along by the surrounding air.

But PM 2.5 diffuses a lot less rapidly than (say) water vapor.  A theoretical rule (via Einstein and Stokes) is that rate of diffusion is inversely proportional to the radius of the particle trying to diffuse.  Getting hold of some data (but not showing the calculation), that suggest that PM 2.5 diffuses about a thousand times more slowly than water vapor.

Diameter of a water molecule seems to be given as 2.75 Angstrom, where an Angstrom is 1/(10^10) meters.  Ah, round down to 2.5.  But PM 2.5 is in microns, or 1/(10^6) meters.  This means PM2.5 particle is about 10^4 = 1000 times larger than a water molecule.  Thus under this  simple theory, water (humidity) diffuses through still air roughly a thousand times faster than a PM 2.5 particle would.

At the end of the day, I have no clue whether that matters or not, with regard to widely varying PM 2.5 readings across my area. 

All I know is that even without big local point-sources of PM 2.5, it’s common to see big difference in (what appears to be) actual PM 2.5, across different locations in my area.  Whereas for other parameters of the atmosphere — temperature, pressure, humidity — true local variation in those quantities is tiny.

Seems kind of crazy to worry about it, but there has to be some good reason why this aspect of the atmosphere is so qualitatively different from others.

Maybe Hawaii wasn’t just a nice place to hang out.

Maybe my only clue comes from the Keeling curve (above) and how that is measured.  When Keeling started measuring atmospheric C02 in the late 1950s, he established his laboratory on the windward side of Mauna Loa.

And found average atmospheric C02 around 315 PPM.  Currently, it’s around 422 PPM.

But the point is why he chose that locale.  His goal was to get “well mixed” atmospheric gasses, and, apparently, having circa 6000 (?) miles of open ocean to windward was just the ticket for getting that.

By contrast, you can frequently find city air with C02 levels in the 1000-PPM range, near congested roads (reference).  That air hadn’t had a chance to get mixed with the rest of the atmosphere.

So, maybe Keeling located there for some reason other than it’s being a nice place.  Maybe you really need that much distance to ensure uniform mixing.  And maybe the mere 500 miles or so between me and the nearest Canadian mega-fire isn’t enough to ensure uniform mixing of the air.

So I’m guessing that the atmosphere doesn’t mix all that uniformly.  For whatever reason.  And that the small-area variation in PM 2.5 is true.  And that I should not expect it to get any smaller as the summer progresses.

Post #1803: What’s normal for PM 2.5 in my area?

 

Currently our AQI is a mere 87, for fine particulates (PM 2.5).  That’s a relief.  Just a normal amount of air pollution.

Or is it?  I’ve kind of lost track of what was normal for my area.  It’s not like I paid attention to the AQI for most of the past decade.

So here, for Fairfax County, VA, I’m posting a table of AQI statistics, for PM 2.5. based on the period 2010-2022.  Just so that I can refer to it as needed.  Briefly, only 1% of days exceed the 99th percentile.  Half of days exceed the 50th percentile.  And so on. Continue reading Post #1803: What’s normal for PM 2.5 in my area?