Sounds like it would be a good fit to monitor the air quality in my 3D printer space.
Could probably do an indoor air quality jig.
Could connect to a smaller screen with SPI, if it doesn't use those, or it could use an external screen like the DSI one from Raspberry Pi Foundation. Looks interesting, if there's a roadtest I'd consider applying.
A good idea would be to use a LSTM AI Keras network to gather data and predict anomalies from a time series.Also build a real time dashboard to show the particulates density in real time.
The Renesas sensor says that it can detect foul odors. Uh... control a bathroom exhaust fan. Specifically, the shop bathroom.
There have been a number of historically bad wildfires across the western United States including the area I live in. I recently bought a portable HEPA filter To improve indoor air quality and it would be interesting to see how effective it is. Absent fire it might also be used to see the influence of solder fumes, 3D printing, cooking and such on a living space and what impact the filter has.
I'm currently running the to detect for pm2.5 and pm10 levels in the apartment (I've got a project log for that in hackaday https://hackaday.io/project/174293-bleifying-a-honeywell-pm-sensor). This seems to be measuring something else: total volatile organic compounds (tvoc) and not only returns the raw values, but also some sort of air quality level based on a standard. The air quality level seems the most interesting because it already defines for you what "bad quality" means.
Unlike the HPMS though, I don't see any fan or application note regarding air flow recommendations nor orientation. So I'm guessing installation would rely on the passive movement of air around it?
I can't see well from the attached picture but I'm guessing the view is from the top and will sit on top of the pi, and helpfully extends the gpios. This however would likely interfere with common pi cases, or will require that the pi's case have a removable top (and not connect the top). Which might be problematic because the pi4 (almost always) requires a heatsink and a fan even during idle.
kmikemoo's use case (option 4 in the manual) is interesting and is probably the industrial application (especially since it seems it can operate with a very bare mcu, as it can automatically trigger a signal based on quality changes), but I'm just worried that since we can't really enclose the pi we might be subjecting it to a far more corrosive environment than necessary. We can of course use it with a far cheaper pi zero but the hat form factor (as opposed to the pHat) means the hat will overhang the pi zero. Then again, with the extra real estate we also get a relative humidity and temp sensor, which again points to Renesas' intention to have the hat be a comprehensive environmental sensor.
I've got mixed feelings, and like the others I'm looking forward to read the road tests (and depending on the results, might get the sensor to complement my HPMS)
From the road test IDT ZMOD4410 Indoor Air Quality Raspberry Pi HAT
> The Avnet IDT ZMOD4410 Indoor Air Quality Raspberry Pi Hat is an evaluation, development and quick-prototyping tool
Well, that makes sense they didn't design it for deployment but more for evaluation, which does away with the issues of the hat form factor (and heat). Although it might still play a role during dev and eval; as lui_gough mentioned, the heat from the pi (especially since the hat would likely interfere with any active cooling fans present) might distort some of the measurements being done.
This board has some interesting features.
If it can detect ethenol, you could test to see if it can measure sobriety.
With winter coming, you can always use it to test CO2 build up and basic indoor air quality during the heating season.
After reading your posts I came up with a simplistic test using the device.
My daughter and her family have moved into the basement of my home. I have a large 52" ceiling fan that I purchased to move air to and from the basement during the summer and winter months. I find the house design causes air to settle air in the basement and put the fan would make a difference.
Now that I have tenants I'm curious what difference does the fan make in air? I know they continuously turn the fan off because it shares power from a common switch. When the fan loses power, it forgets that it is a fan and needs to be reprogrammed. When no one was using the switches it wasn't a problem.
As I review my methodology I can see flaws in my reasoning. I need to monitor both upper and lower environments plus not every day is the same in terms of air. It is a great experiment. I think I would also deploy it with the POE Hat Raspberry Pi4B (4GB) plus POE Hat - Review I received so it could be positioned without the restrictions of power.
I enjoy these analytical projects. It was great white boarding material while having coffee with the folks I worked with.
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I think I would compare it with other sensors on the market, as I haven't been entirely satisfied with any of them for CO2. I think the gold-standard is still IR-based type sensors, but they are more expensive and bulky.
I currently have experience with the Sensiron SGP30 which is a CO2eq and TVOC sensor, and a Bosch BME680 which also claims to output VOC readings. The SGP30 seems to have a lot of "cross-sensitivity" effects which result in wacky CO2eq readings when hit by VOCs (e.g. just open up a bottle of ethanol next to it and it will go nuts). The Bosch BME680 is not entirely satisfactory either - instead of providing ppm/ppb concentrations, it reports VOC as a resistance change of an internal (presumably metal oxide) element.
Perhaps evaluating the cross-sensitivity behaviour under regular conditions and high-VOC concentrations would be a good activity, along with using it ordinarily in a bedroom to evaluate correlation in sensor values between the SGP30 and the Renesas solution. It would be good to see how well each of the sensors manage to handle the situations, as many of them are "self-calibrating" to a "background" of 400ppm, so the drift and accuracy in the long term would be good to know.
I think that the decision to evaluate the sensors in a Pi Hat could also cause problems which I'd be keen to check out - for example, the positioning of the sensors look close to where the Pi4 has the USB3 controller and previous Pis have a USB Hub and Network IC - all of these chips are known to be hot ICs, so I wonder whether the "high precision" of the sensors would be wasted given the close proximity when used "as a hat". Also having previously evaluated the Bosch CISS, Omron 2JCIE-EV-AR01 and BU/BL01 solutions, it might be nice to compare the temperature/RH performance versus the "industry standard" Sensirion SHT30 used in the latter solutions.
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Actually, it seems like Renesas/IDT actually have contracted KSI to do some testing on their sensors which confirms my suspicion that MOX based CO2eq/eCO2 numbers are a bit funky in general, but IR-based CO2 is much more reliable:
The document on their site shows comparison between the IDT/Renesas ZMOD4410, Sensirion SGP30 and AMS CCS811 MOX type sensors; and Sensirion SCD3x, Senseair K30 and Cubic CM1106 IR-type sensors. The most interesting Fig 14 seems to show just how the different MOX sensors fared:
Having used the SGP30 myself, it seems that the SGP30 has a longer decay time than the other sensors and reports higher than the other sensors, but all of them can deviate quite far from the ground truth.
During their calibration of the SCD3x (note their labelling error), K30, CM1106 with their MX1102, we can see the IR sensors are generally quite good at following the trends.
Curiously, they didn't attempt using the same dry mixture to calibrate the eCO2 values for the MOX sensors. Perhaps the baseline drift algorithm would have prevented that from working, but their observation that ethanol makes the sensors go crazy is pretty much apparent, although the behaviour of the ZMOD seems a little interesting in that it seems to "clamp" low into 400ppm for anything less than the maximum tested value, which is perhaps a nicer sensor behaviour. There is a significant unit-to-unit difference in response, as noted in https://www.idt.com/us/en/document/rep/ksi-report-zmod4410-gas-sensors-compliance-ubas-iaq-study?language=en , but this seems to be calibrated out to achieve their claimed accuracy ranges:
It's nice to see this level of detail available openly, as I have not seen this level of detail for other sensors, but the performance perhaps depends on the algorithm (the latest being their V2) using AI. There are also separate firmwares for fan control and sulfur odour detection. They also say the following in their datasheet (which has no information on I2C registers):
For implementing the sensor module in a customer-specific application, detailed information on the programming is available. The recommended requirements for the host MCU are 16kB flash for ZMOD4410 related firmware code, 1kB RAM for ZMOD4410 related operations, and the capability to perform I2C communication, timing functions, and floating-point instructions.
I suspect this is a conventional non-AI algorithm and that even the I2C register map is subject to agreement on some terms/conditions? I suppose the RoadTesters may be able to find out.
Application #2. While disinfecting my training room after a meeting... WHEW! The smell of disinfecting spray was almost overwhelming. While this strong smell says "clean", it's also unpleasant for prolonged periods.
I could connect the sensor to the thermostat to force the circulating fan to ON from its normal AUTO setting. At least this would stir the air overnight and make the room a little more tolerable the next day.
Hummm you project got me to thinking about my heat exchanger taking a trigger from the air quality sensor. The heat exchanger has all the plumbing to move the air, I wonder if there is a input on the unit. Got to look.
This looks very useful HaT for air quality monitoring.
During the winter months when indoor air becomes more contaminated because windows and doors are closed, this is a problem during the night time and also day time when one has to open the window time to time for fresh air. When some people in a meeting or gathering do not get a fresh air they have problems to concentrate!. This HaT can be used to provide indicator to the user for air quality.
Back on July 31st lightning started the Pine Gulch fire, the largest wildfire in Colorado's history. 139,007 acres burnt. It wasn't until just 3 days ago they finally were able to get it completely contained. We were able to watch it flaming up at night and had to wipe ash off our cars in the morning for a few days. We are still going through a massive haze in this area and this would definitely be interesting to see what it shows for indoor air quality. I have already been through multiple house air filters trying to keep out as much as possible.
While the local smoke scene has improved (previously I was unable to see the edges of my yard/road) it still remains a serious issue here in Colorado with recommendations for remaining inside being the prevalent advice being offered. It would be intriguing to see what a device such as this actually would show as quality and concerns of inside air compared to the outside air which is currently a health hazard.
I would probably use it for testing multiple different devices for cleaning the air in rooms and in houses. Since Covid there has been a ton of new products and devices and it would be interesting to test in a isolated area if these devices where actually functioning to their specifications or at all.
I would use it to monitor and log the co2 level in a building to insure that the fresh air return system is maintaining a healthy co2 level.
Researchers have determined buildings with a CO2 levels below 600 ppm, stops the outbreak completely. An increase in fresh air ventilation can decrease the transmission by 97%. Since the COVID-19 is spread through the air, a higher CO2 levels in a room likely means there is a higher chance of transmission if an infected person is present.
There's a new Pi Hat that has been launched by Avnet that I want to tell you about. It uses a Renesas ZMOD4410 Indoor Air Quality Sensor. Since I am planning to roadtest it, I wanted to introduce it to you and see what you think. Let me give you some facts about this relatively new Pi Hat.
This Indoor Air Quality Pi HAT is an evaluation, development and quick-prototyping tool that features an on-board calibrated ZMOD4410 sensor that measures the concentrations of Total Volatile Organic Compounds (TVOC) and can estimate carbon dioxide (eCO2) levels. These are important indicators for monitoring indoor air quality. In addition to the indoor air quality sensor, the HAT incorporates a Renesas HS3001 Precision Relative Humidity and Temperature Sensor, along with software-controlled status LEDs.
To validate the HAT’s operation and begin measuring TVOC and eCO2 “out of the box” with a Raspberry Pi solution, Avnet provides a pre-compiled test application built with those algorithm libraries that runs under the Raspberry Pi operating system (formerly Raspbian).
Other nice to know facts about this Pi Hat:
- Detects a wide range of TVOC, from parts-per-billion to parts-per-million and provides eCO2 levels
- Sensors are chemically tested and factory calibrated
- On-board user-adjustable power supply option and current measurement connection points
- Configurable alarm/interrupt output
- Supplied with pre-compiled Raspberry Pi OS test/validation application
- Renesas offers licensed downloadable compiled code, enabling a product road map of indoor air measurement innovation
To learn more about the Renesas ZMOD4410 Indoor Air Quality Sensor, click here.
So, what do you think? What would you do with this Pi Hat?
Feel free to leave a comment below.