I know what you are thinking after reading our first two parts of this series: What dataloggers should I buy to measure my climate? There are loads out there and some are pretty expensive!

Here’s the thing: What makes dataloggers expensive is accuracy, especially when it comes to measuring relative humidity. You can buy pretty cheap models, but what makes them cheap is usually cheap sensors. Now, you probably wonder how bad it is to have a cheap sensor. As nearly always, it’s a “depends”. Depending on what? Depending on how stable your “room climate” (although we already saw that there is no such thing) is and how accurate you need your measurement to be.

One thing you have to know is that sensors have a range they operate in, humidity sensors usually and logically between 0 and 100% relative humidity. Within this range they might vary in accuracy. Humidity sensors are typically most accurate in the middle range around 50% rH and become less accurate at the edges under 10% or over 90% rH, they also might become less accurate in very cold or very hot surroundings. Usually, you find these error ranges as graphs in the datasheet of each sensor or device.

That’s pretty theoretically, so I made a test to show you what this can mean (in retrospective I should have used a more diverse set of colors for the diagram, my apologies for that). I put in the same spot three sensors:

DHT22 (light green), corrected DHT22 (blue), generic SHT31 (dark blue), Sensirion SHT35 (green)

1. A pretty cheap DHT22/AM2302 ¹ which is a very common temp/rH sensor in the maker/microcontroller scene and which you will find frequently in DIY devices. It is given with a normal accuracy of +/- 2% RH and +/- 5% RH maximum discrepancy (in which range it reaches this maximum discrepancy isn’t given).
   This is the light green line.
2. A mid-priced generic Chinese version of a Sensirion SHT31 ² which you will also find in more refined DIY projects. It is also given with a +/- 2% RH accuracy.
   This is the dark blue line.
3. A high-priced original Sensirion SHT35 ³ which you will find in professional devices. It is given with an accuracy of +/- 1.5 % RH in a range between 0 and 80% relative humidity, which means it becomes less accurate above these values.
   This is the green line.
4. In addition, you will find a blue line: This is the linear correction of the DHT22 sensor which is based on the measurements I did with this sensor compared to an Assmann psychrometer. I found the DHT22 was 31 percentage points off the “real” relative humidity I found with the Assmann at 55%. So, I subtracted 31 from the value the DHT22 measured. ⁴

So, what does the graph show us and what can we learn from it?

All sensors are pretty much the same when it comes to temperature. This tells us that if we only want to measure temperature, we can go with a cheap device.

When it comes to humidity, things become really interesting:

1. The original DHT22 readings show a much too high humidity, something we already expected because of our measurements with the psychrometer. Naturally, as the sensor can only read 100% max, the lines are cut at 99.9%. As our correction of the sensor is mainly a linear subtraction, it transfers the straight line to 68.9% for our corrected values in these cases (blue line).
2. The expensive original SHT35 and the cheaper generic SHT31 are not really far apart. When humidity crawls towards the 70% and over it, it seems that the cheaper one (dark blue line) measures a little less humidity than the expensive one (green line). ⁵
3. What is really funny is that the cheap sensor seems to have a tendency to over-dramatize events. As we take a closer look at a drop (and later rise) in humidity on July 27 the DHT22 shows a dramatical decrease from 100% in the morning down to 49.7% at 3 p.m. Its corrected version sees an equally dramatic event from 68.9% to 18.7%. As we take a look at the other two sensors nothing this dramatic happens. The most expensive one sees a drop from 66.3% to 40.7%. Still a mild catastrophe if this were a gallery or storage room (it wasn’t), but a huge difference to have a 25% drop than a 50% drop.


A close look at July 27

Especially the last point tells us a lot about cheap and expensive dataloggers. Not because cheap devices necessarily contain an old, beat up DHT22, but because it shows us the general problem with sensors: they are not necessarily acting linear. They’ll need adjusting.

Now, every adjusting is an expensive step. Devices are adjusted to reference points which means the manufacturer does more or less what I did with the DHT22: they measure it against a calibrated source (i.e. a salt solution) and then adjust the output accordingly.

If it is a cheap device it might be only adjusted to one reference point, resulting in what we see in the DHT22. Just because it was 31 percentage points off at 55%, this isn’t necessarily true for the whole range. Instead, it is very unlikely for a sensor to react linear throughout the whole spectrum. It will react differently for different humidity ranges. This is why expensive devices are measured to more reference points and calibrated accordingly, resulting in much more accurate readings throughout the whole range.

I assume that the generic SHT31 was also only tested against one reference point and that the difference we see in the readings against the original Sensirion when the humidity rises is already a sign of it, but as it is still within the possible error range, I can’t prove that.

For me personally, I wouldn’t use DHT22 anymore, for obvious reasons. I can live with the generic SHT31 for cases where I need to get an idea of a setting and in less problematic areas. I’d always go with the high-priced original parts if I have to depend on the readings for loans or critical storage environments.

So, more generally speaking: can I cut costs by buying a cheap device? Yes, if you just have to measure temperature. And yes, if the only thing you need is a rough idea of what happens in regards of rising and dropping or the humidity, but not detailed values. You have to be aware of the fact that it might show a more dramatic drop than what actually happened as well as it is thinkable that it shows you a less dramatic change than what actually happened.
You might want to turn to high quality products when it comes to your more critical applications and it’s always a good idea to take a critical look at the datasheets to know what you are buying.

May your storages and galleries always have a nice and stable climate!

Angela

Picture by Alexas_Fotos via pixabay CC0

One of the difficulties you face when you decide on where to put a datalogger is that only in very rare cases there is something like a “room climate”. In most rooms, there are several climates. There is that outside wall that is always a little colder than the rest of the room. There is that wall that has bright sunlight from 11 a.m. to 2 p.m. in winter and 9 a.m. to 4 p.m. in summer. There is the space at the entrance which always gets a flush of cold and damp air as soon as someone opens the door. Then, there are your display cases.

All things try to reach equilibrium, which means that they strive to reach a common room climate, but while doing so, they create different and sometimes problematic room climates. The warmer air will inevitably go to the colder areas, but when reaching there, the once nice 51 % at 21 °C (70 °F) might become 68% in that 16 °C (61 °F) corner of two outside walls. ¹

To get a good idea of what happens where in your room or gallery, you might wish to place several sensors. Life and the museum sector being what it is, you probably don’t have money to do that. What you can do, however, is measure at different areas, moving your datalogger to get an idea what happens where. Then, for the permanent location of your datalogger, choose the place that captures the climate your artifacts experience best.

Next up, we want to take a look at the difference between cheap and pricey sensors, which is – besides other things like WiFi functionality – what determines the price of a datalogger.

Stay tuned for part 3 of this article.

Picture by Brett_Hondow CC0 via pixabay

When I think about my early childhood, there is one picture that comes up from time to time: It’s my grandfather standing before the so called “weather station”, a wooden panel with three brass gauges: a thermometer, a hygrometer and a barometer. Then my grandfather gently knocked against the glass lid of the barometer with his knuckle and checked the barometer again. As a child I was convinced he woke up the little worker inside, who in return checked if the weather would become fair, rainy or if it was changing. This made perfect sense to me, because I knew there was a special force responsible for the weather: the weather fire dwarfs. After the evening news the news anchor would say “And now the weather fire dwarfs for tomorrow”. It was much later that I asked my mom and she explained to me that it wasn’t the “Wetterfeuerzwerge” (weather fire dwarfs), but the “Wettervorhersage” (weather forecast). Until then, I was convinced that being a weather fire dwarf was just a job like being a firefighter, a baker or a teacher. They looked out for the weather and made assumptions on how it was going to be. It also made perfect sense that this job was done by dwarfs, given the small instruments they needed to operate in.

You might wonder where I’m heading to with those seemingly random childhood memories? Now, my grandfather certainly did neither wake up the little weather fire dwarf, nor did he perform some other kind of magic. He just made sure the barometer, which had a small metal box called an aneroid for measuring the pressure inside, was showing the tendency. When you tap them, the pointer will move to the current pressure, giving you an indication if the air pressure is rising or falling. He also knew that the little markers that read “very stormy”, “rain”, “change”, “fair” and “very dry” were not to be take literally. In winter “fair” weather often meant it became very cold. He not only looked at the barometer – he also knew how to interpret what he saw.

Fast forward to 2018 I struggle with explaining the use and the accuracy of sensors to colleagues. I can’t fight the feeling that digital has somehow taken a toll on our imagination and our expectations towards instruments. It’s especially true for hygrometers, as humidity is far more complicated to measure exactly than temperature ¹. A digital hygrometer usually provides you with two digits behind the comma. So instead of reading a little above 50% on the analog hygrometer it provides you with a straight 51.23 % relative humidity. That’s astounding. Sadly, it doesn’t mean what people think it means. Because it seems to provide such an exact figure, people assume it’s more accurate than the analog reading of grandpa’s old weather station. But it isn’t necessarily so. If it’s a very good digital humidity sensor with a 1.5% accuracy the reading will mean it’s somewhat 50 to 52ish. If it is a more common sensor with a given 5% accuracy it tells you that it can be anything along the lines of 46 to 56% ²

Picture by Alexas_Fotos via pixabay CC0

But why are they giving two digits behind the comma anyway, is this just a fancy feature? Yes and no. For that, we have to understand the difference between accuracy and resolution, both specifications you will find when you are buying a datalogger or sensor. What we just talked about is the accuracy. The resolution is often higher than the accuracy. Your humidity sensor might have an accuracy of +/- 2% RH, but a resolution of 0.1. This seems like a contradiction at first, but it isn’t. Let us imagine a little weather fire dwarf who is able to feel how wet it is. He says: “It’s 52%”. Now, while we saw that this can mean anything between 50% and 54%, if we send him out again, he will be able to tell us if it is becoming wetter or dryer than before. And he will be able to tell us more detailed than he can do it with the general humidity. He is able to say: “It’s getting damper, now it’s 52.1%.” So, while the digits behind the comma mean nothing in terms of general humidity, they can help us understand where the climate of a room is heading to and how severe the changes are. If you measure every 5 minutes and get a reading of 52.1%, 52.3%, 52.2%, 52.1%, 52.2% and 52.1% over half an hour it will tell you something else than a series that reads 52.0%, 52.2%, 52.3%, 52.4%, 52.6%, 52.8%. While both series can still mean it’s 2% more or less humid, the tendency of the second series shows that something is going on which makes the room damper. Think of the resolution as a measurement that helps you to see change, just like grandpa tapping on that old barometer.

Next up, we will ask the question if sensors really read the temperature and humidity of a room.
Here comes the 2nd part…

Angela