Studies that characterize the performance of low-cost particulate matter (PM) sensors are needed to help practitioners understand the accuracy and precision of the mass and number concentrations reported by different models. We evaluated Plantower PMS5003, Sensirion SPS30, and Amphenol SM-UART-04L PM sensors in the laboratory by exposing them to: (1) four different polydisperse aerosols (ammonium sulfate, Arizona road dust, NIST Urban PM, and wood smoke) at concentrations ranging from 10 to 1000 μg m−3, (2) hygroscopic and hydrophobic aerosols (ammonium sulfate and oil) in an environment with varying relative humidity (15%–90%), (3) polystyrene latex spheres (PSL) ranging from 0.1 to 2.0 μm in diameter, and (4) extremely high concentrations of Arizona road dust (18-h mean total PM = 33,000 μg m−3; 18-h mean PM2.5 = 7300 μg m−3). Linear models relating PMS5003- and SPS30-reported PM2.5 concentrations to TEOM-reported ammonium sulfate concentrations up to 1025 μg m−3, nebulized Arizona road dust concentrations up to 540 μg m−3, and NIST Urban PM concentrations up to 330 μg m−3 had R2 ≥ 0.97; however, an F-test identified a significant lack of fit between the model and the data for each sensor/aerosol combination. Ratios of filter-derived to PMS5003-reported PM2.5 concentrations were 1.4, 1.7, 1.0, 0.4, and 4.3 for ammonium sulfate, nebulized Arizona road dust, NIST Urban PM, wood smoke, and oil mist, respectively. For SPS30 sensors, these ratios were 1.6, 2.1, 2.1, 0.6, and 2.2, respectively. Collocated PMS5003 sensors were less precise than collocated SPS30 sensors when measuring ammonium sulfate, nebulized Arizona road dust, NIST Urban PM, oil mist, or PSL. Our results indicated that particle count data reported by the PMS5003 were not reliable. The number size distribution reported by the PMS5003 (a) did not agree with APS data and (b) remained roughly constant whether the sensors were exposed to 0.1 μm PSL, 0.27 μm PSL, 0.72 μm PSL, 2.0 μm PSL, or any of the other laboratory-generated aerosols. The size distribution reported by the SPS30 did not always agree with APS data, but did shift towards larger particle sizes when the sensors were exposed to 0.72 PSL, 2.0 μm PSL, oil mist, or Arizona road dust from a fluidized bed generator. The proportions of PM mass assigned as PM1, PM2.5, and PM10 by all three sensor models shifted as the PSL size increased. After the sensors were exposed to high concentrations of Arizona road dust for 18 h, PM2.5 concentrations reported by SPS30 sensors remained consistent, whereas 3/8 PMS5003 sensors and 2/7 SM-UART-04L sensors began reporting erroneously high values.
This article is an eye-opener for the public getting to know, PM is not PM!
For quite some time students learn in Physics, dust-particles in air-masses close to due-point cause gas-H2O molecules form clusters with the particles dependant on the chemical structures of particles. The number of H2O molecules, depending on particle-chemical-composition, is responsible for the change of status from gas to liquide. In case the cluster has a energy difference, the condition of H2O may turn directly from gas to solid or stay gas even the surrounding temperature turns below 0 deg,C.
That all is physics and chemistry highschool-level. But, to turn this into solving PM sensing challanges, is a long way. The article gives a good impression, what it is all about compensating, even for known conditions. Generally speaking: For what we are doing, it is hardly possible normalizing results.
Comming back to the known challange of high humidity: Humidity in air is a relative measure with two high-impact conditions: Temperature / Presure. Since the presure-variance is very small, the temperature is the driving factor.
But first to pinpoint the humidity: The rate in % is a theoretical factor. Meteos find the due-pont in temperature, that is when the air can’t take more moisture, by mooving two thermometers through the air where 1 is covered with a wet cover. Doing that for a while, will show two temperature values: 1 is the air-temperature, 2 is the temperature where the air can’t take on more moisture, means, that is the temperature, the moisture around the thermometer can’t cool it further down by evaporating H2O. Talking practice: If the temperature difference is less than 2 deg.C, mist is very likely.
Now the point of the article above:
Free Oxigene is one of the most destructice chemicals we know. Actually, single Oxigene-Athoms(O) are very rare. Like Hydrogene(H), it is very reactive with other chemicals or elements or molecules. Oxidation is the term for processes: Burning or reaction. Remember, CO2(the clima-gas) is one athom carbon and 2 athoms oxigene.
When we operate the PM-Sensors, we do that in real-conditions. In the article, the particles where controlled in size and composition. Under normal condition, moderate temperature, moderate humidity, particles and air-gases stay preaty stable, thus, they are succed in and spit out again, no reason why they shall stay in the sensing-chamber or hitting the transmitter or reciever diode. The condition will stay good for the job a long time.
If that is not the case, especially with liquide water in pure-form (clusters of H2O molecules forming droplets) or even worth, clusters of PM piling with H2O reacting, those much heavier (bigger mass) touch the casing or the sensing diodes, may stick there and do their harmfull work. Surfaces of diodes (regardless IR or Lazer) need to be as produced to deliver expected behaviour. Especially the inner surface of the counting-chamber has effects on the air-flow. Sticking material changes that, temporarily or permanently.
An other physical effect of floating gasses is the tendency of gaining static electricity. Not the flow we are creating with our little fan, but wind close to it creating high-speed turbolences called fortexes.
What does that all impact us with our little vergine sensor?
Best practice for the operation is to controll the flow of the air-body we will test on, preferably before the succing point. We have to make sure, there is no venturi-effect close to the inlet (when in any pathway of a flowing medium the diameter changes or obsticles appear, flow-rate is changing and the temperature too)
Secondly, we shall make sure, the relative humidity is below the point when H2O turn liquide. This is easyly sayed as the discussion shows.
All that above is not depending of the actual brand or modell of PM-Sensor. It is actually to help understand, that citzens science is no nuclear sience. Generally it is applying what we learned or not because we were bored by stupid physics or chemistry or teachers in some very rear cases.
By understanding fluidized bed generator, I’m performing a CFB simulation by following the instructions of the tutorial downloaded from the comsol website. However, the simulation does not converge. It reaches a constant value of 1000. I have tried to change the value of the absolute tolerance and the relative tolerance, but the problem is the same. The comsol version that I have installed is 4.3.