Recently, I shared an illustration that demonstrates, based on scientific research, how deep pollution particles can go into the human body.

Particulate matter (PM) are basically sneaky and invisible (to the naked eye) particles of various sizes that trigger inflammatory responses all over the human body depending on their size/origin and how deep inside us they can go. They are even able to destroy human tissue when the origin of the particles comes from the combustion of fossil fuel or biomass burning.

The smaller they get the easier it is to enter our bloodstream and affect every cell and organ in our body, even our brain. For this reason, it is very important to start monitoring the unregulated ultrafine particles aka nanoparticles or PM0.1 or UFP.

The IPS7100 by Piera Systems (a Canadian company) is a new generation low-cost laser scattering sensor capable of measuring those ultrafine particles PM0.1 but also fine particles PM0.3/PM0.5/PM1/PM2.5 and coarse particles PM5/PM10. In total, the 7100 series offers 7 output bins.

Tech Specifications

• Ultra-high sensitivity for detecting airborne particulates (PM0.1 to PM10*) 
• Fast data acquisition and sampling 
• Supports UART and I2C 
• IoT/Network support* 
• Plug-and-play 
• Adjustable sampling/data acquisition timing 
• Sensitivity control* 
• Power saving mode 
• Fan control and cleaning mode 
• Built-in output data unit conversion options 
• OTA firmware update capability* 
• Built-in VSD (Vape/Smoke Detection) module* 
• Particle Count (PC) accuracy ±10%
• Mass Concentration (PM) limit 6,000 μg/m³ (Referenced at ≤2.5μm particle size)
• CE certified

* Some features may be only available for specific models 

Observations

Unfortunately, I don’t have a reference monitor of this class to compare the sensor, but a few tests can help me understand how the sensor works and behaves.

The evaluation kit that I got, comes with software that allows me to attach the sensors to my computer (Mac/Windows) and investigate and log measurements in order to compare the result against other sensors.

By placing the sensor in the outlet of a good H13 HEPA filter I make sure that almost all PM2.5 and above are removed and I can focus on the ultrafine/fine particles which interest me the most.

Once more when we dive into the “nano-world” we realise that mass concentration (μg/m³) is such an inadequate way to measure nanoparticles. Take for example the images below. When I measure the mass concentration of PM0.1 and PM0.3 we get zero value but when I switch to Raw counts the opposite phenomena happens. Big particles give almost a few hundred particles count and small particles like PM0.1 and PM0.5 give us thousands of particles. Keep in mind, PC0.1 and PM0.1 are estimated at the moment as the company tries to validate the measurements against reference monitors, which are hard to find even in North America.

Take for example the image above and how big a PM2.5 particle is in comparison to a 100nm (PM0.1) particle.

The Piera Intelligent Particle Sensor (IPS) which is a photon-counting readout-based sensor has been calibrated against the GRIMM 11-D. GRIMM 11-D can measure particles between 0.253 and 35.15 μm, which focuses on PM10, PM4, PM2.5 PM1. Piera sensors use a propitiatory Particle Counting Integrated Circuits (PCIC) as a core processor which is responsible for fast data acquisition and sampling.

The smaller the size of the particles (PM0.3/PM0.5/PM1) the better correlation results tend to have most of the low-cost sensors against reference instruments.

I wanted to see how the Piera-7100 will compete against other low-cost sensors like the PMS5003, SPS30, and HPMA115S for this reason, I have placed them side by side. I was “lucky” because during my experiment, we had Saharan dust over Europe and I was able to obtain good and real PM.

In the graph above, we can see the PM2.5 mass concentration for all four sensors. A day worth of samples with 5-min average values. The IPS7100 took samples every second, so I ended up with 86400 measurements only for PM2.5 − fast data acquisition and sampling. Relative humidity was around 50%.

The similarity is extraordinary with some minor misbehaviors from the PMS5003 as it has registered some high peaks of 155μg/m3 while the air was purified with a double H13 filter and the SPS30 as it didn’t reach near-zero concentrations during 2 hours of treaded air. It turns out that the SPS30 can’t measure below 1μg/m3, this is a firmware limitation.

It is known that compared to a Tapered Element Oscillating Microbalance (TEOM), a Plantower PMS5003 overestimates PM2.5 concentrations, and this is the reason we can observe on the graph the line of the PMS5003 above all. The Pearson correlation coefficient between the PMS5003 and the IPS7100 was 0,962.

PM1.0 wise, a Pearson correlation coefficient between the PMS5003 and the IPS7100 was 0.982.

Unfortunately, I wasn’t able to compare measurements below PM1.0 because of some limitations, so I compared the Particle Count PC0.3 numbers between the IPS7100 and the PMS5003. The Pearson correlation coefficient between these two sensors was 0.945. The Plantower sensor uses a PM2.5 analog front end and the other 2 bins are derived (or factored). The smallest bin is estimating particles from 0.3 to 1.0, so the correlation tests for 0.3 and 1.0 for the 5003 should be identical because it’s the same bin.

Below we can see that for the sample period the IPS7100 saw 9530 particulates in the 0.3 bin and only 209 particulates in the 2.5 bin. The estimated mass of the 2.5μm particulates in the 2.5 bin is more than 12 times as large.  It has a huge impact on the quality of this estimation and so the more accurately a sensor can estimate the size of particulates the more accurately that sensor can estimate particle mass for particulates.

Although the sensor sees and counts the different particle sizes in bin 5 and bin 10, you will think that it performs poorly at the conversion of PM10 as it gives the exact mass concentration for PM5 and PM10. However, PM10 particulate matter is the conversation in which 50% of particles have an aerodynamic diameter of less than 10 µm (including PM5).

The Vape/Smoke Detection (VSD) feature can identify the presence of smoke or vape and help us understand the source of the pollution in an indoor environment in order to make the necessary actions. The event is detected after it lingers for 30 sec to prevent false positives. Also I pushed the limits of the sensor quite hard with lots of smoke!

Conclusion

The Piera IPS7100 sets a new standard in the low-cost sensor market thanks to the introduction of PC0.1, which is under validation but according to the datasheet it are extrapolated. We are currently breathing thousands of nanoparticles even if the current regulations and monitors indicated that the air is “clean”. Remember, these nanoparticles can reach every organ in our body, even the brain. The sensor performs very well in comparison to other sensors and is really fast, ideal for environments that are constantly changing, like a moving network of AQMs on vehicles like cars and buses. The VSD is an excellent feature that brings context to the air we breathe. The ability of Piera sensors to do vape/smoke detection is a great demonstration of what can be achieved with their technology; imagine all the things that you can do now because you can accurately count sub-micron particles and classify the actual pollutants.