Particulate matter, or “PM,” is a combination of liquid droplets and airborne solid particles that can be inhaled and may pose serious health issues. PM includes particles with different properties — namely, optical properties, shape, composition, and size — however, it is most commonly classified into sub-categories on the basis of particle size information.
In general, different PM categories are reported under the common nomenclature of PMx, where “x” defines the maximum particle diameter in the airborne particle mixture or “aerosol.” For instance, PM2.5 specifies inhalable particles with a diameter of typically 2.5 μm and smaller, PM10 specifies particles with a diameter of 10 μm and smaller, and so on. The specific PM categories of PM2.5 and PM10 have been historically identified by national governments as key monitoring levels for evaluating the quality of the air inhaled1,2 since PM10 particles irritate exposed mucous such as the eyes and throat and PM2.5 particles travel all the way through the lungs into the alveoli.
New categories such as PM1.0 and PM4.0 are also finding their way into air quality monitoring devices because these new outputs offer additional information to the conventional PM10 and PM2.5 levels, allowing a better particle pollution analysis and the development of new device-specific actions based on the detected aerosol type (e.g. house dust vs. smoke).
The common definition of PM includes particles that are at least 100 nm in size. Particles smaller than 100 nm are instead reported as “ultrafine particles” or “UFPs” and are not discussed in this article. Within the above-mentioned PM definition, which comprises of particles of size in the range of 0.1 to 10 μm, the smaller the particles, the deeper they can penetrate through the respiratory system and into the bloodstream, causing a higher risk to health.
The World Health Organization (WHO) describes airborne particulate matter as a Group 1 carcinogen3 and as the biggest environmental risk to health, responsible for around one in every nine deaths every year.4 Shown in Figure 1 is the size range of common pollutant sources, including filtration technologies used for the removal of such contaminants (adapted from John Wiley and Sons, Best Practices Guide to Residential Construction, 2006).
Figure 1. Size range of common pollutant sources (adapted from John Wiley and Sons, Best Practices Guide to Residential Construction, 2006).
Traditionally, PM values are measured as “mass concentration” in μg/m3. The reason behind this is that the conventional and most accurate way to measure PM is the gravimetric method. This method involves utilizing a pre-weighed filter to gather ambient particles that are physically pre-sorted depending on their size (for example, all particles below 2.5 μm are let in). At the end of the sampling period, typically 24 hours, the filter is weighed to determine the total accumulated PM mass in micrograms. Mass concentration is then obtained by dividing the mass increase of the filter by the 24-hour total volume of air that passed through the filter, yielding a value in μg/m3.5
Gravimetric methods are long established as the most accurate way of determining mass concentration; however, they have some practical restrictions to their diffusion in everyday applications: these instruments are very costly, heavy, cannot perform real-time sampling, process only one PM size per measurement (e.g. PM2.5), and cannot output the particle number count.
As a result, real-time optical particle counters (OPCs) have gradually found their way into the air quality monitoring market. These instruments are based on different optical principles, usually absorption or scattering, with light scattering being the most frequently used. In these OPCs, the particle traverses through the light source (usually a laser beam) and causes scattering (or absorption) of the incoming light, which is then detected by a photodiode and converted into mass concentration values and real-time particle count.
Presently, optical detection is the most prevalent technique because of its unbeatable cost-performance ratio and ease of use. Recently, OPCs have become small enough to be incorporated into air quality monitors, air purifiers, and air conditioners, and are used to regulate and control air quality in cars, households, and outdoor environments.
The basic principle of OPCs might seem simple at first; however, from an implementation perspective, not all OPCs function in the same way and the quality of their measurement depends significantly on the engineering and design of such devices. The optical principle works very well for particle counting; however, since these devices are used mainly for the estimation of the PM mass concentration, they will be vulnerable to estimation errors due to different mass densities and the different optical properties of the particles (e.g. shape and color). The quality of the mass estimation will thus differ greatly depending on the manufacturer algorithm used to convert the measured optical signal into PM mass concentration.
Additionally, the internal airflow engineering has a high impact on the accuracy and drift of these sensors as particles can build up easily on their optical elements (photodiode, laser, beam-dump) and degrade their output over time in case they are not properly engineered.
The Sensirion SPS30 works on the principle of laser scattering. A controlled airflow is created inside the sensor using a fan. As illustrated in Figure 2, an internal feedback loop between the microprocessor and fan stabilizes the fan speed and consequently the airflow through the sensor. Environmental PM travels inside the sensor from inlet to outlet, carried by the airflow (black dots in Figure 3). Particles in the airstream traverse through a focused laser beam in line with the photodiode, as marked in red in Figure 3, causing light scattering. The scattered light is then detected by the photodiode and converted to a mass/number concentration output via Sensirion’s proprietary algorithms, which run on the SPS30 internal microcontroller.
Figure 2. Block diagram SPS30 (Source: Sensirion)
Figure 3. Working principle (Source: Sensirion)
Particle Composition Recognition
As previously discussed, the manufacturer’s algorithms, along with a proper front-end electronics design, make an elemental difference in the estimation of mass concentration from the detected scattered light. Majority of the low-cost PM sensors on the market assume a constant mass density in calibration and calculate the mass concentration by multiplying the detected particle count with this mass density. This assumption only works if the sensor measures a single particle type (for example, tobacco smoke), but in reality, you can find several different particle types with many different optical properties in day-to-day life, from “heavy” house dust to “light” combustion particles (see Figure 4). Sensirion’s proprietary algorithms use an advanced approach that enables a proper estimation of the mass concentration, without considering the particle type measured.
Moreover, such an approach allows a correct estimation of the size bins. Also, an extra bin output is provided contrary to most modern consumer PM sensors on the market — PM4.0. The higher resolution on the number of bins and the increased accuracy for different aerosols enables users to develop new use cases based on particle composition recognition. Figure 5 shows a practical demonstration of such a feature, using Sensirion’s Control Center software. The bar charts show the real-time measured mass concentration bins, measured with an SPS30.
The right chart illustrates a measurement from Arizona dust, clearly richer in bigger particles. The left chart illustrates a live measurement of match smoke, clearly richer in smaller particles. This simple but effective experiment emphasizes the value of the SPS30 advanced binning feature and the potential for the development of new applications based on particle composition detection.
Figure 4. Particle composition of smoke (Source: Sensirion)
Figure 5. Particle composition of heavy dust (Source: Sensirion)
As mentioned previously, a PM sensor is in principle very vulnerable to output drift due to the accumulation of dust on the vital optical parts of the device, which are the photodiode, the laser, and the beam-dump (used to absorb the laser light and avoid parasitic scattering).
Figure 6. Clean photodiode after stress test (Source: Sensirion)
With over two decades of experience in flow sensor design for several demanding markets and applications (for example, medical, automotive, industrial, and smart energy), Sensirion’s engineers have developed and integrated an innovative and proprietary flow path technology in the SPS30 that prevents the build-up of dirt and dust on the optical components. Figure 6 shows the result of a stress test, where a sensor is exposed to the equivalent of five years’ dust exposure in Beijing. The figure clearly reveals that the flow path protects the vital optical elements from dust exposure, and that the laser and photodiode are completely clean even after the stress test (the beam-dump, which is also protected from dust accumulation, is not visible in the photo).
In summary, Sensirion’s dust resistance and advanced binning technologies offer additional value to applications in many industries, including HVAC, air purifiers, and air quality monitoring. A sensor that works over the entire lifetime of a device ensures good air quality to the final user and maximizes energy efficiency and sustainable operation. Advanced binning and higher accuracy help to trigger specific actions depending on the detected particle composition and enhance the monitoring of filter lifetime depending on the contaminant type information gathered over the device’s operation.
- AQI levels as defined by the China Ministry of Environmental Protection (2012): http://bz.mep.gov.cn/bzwb/dqhjbh/jcgfffbz/201203/W020120410332725219541.pdf
- AQI levels as defined by the US Environmental Protection Agency (2013): https://www.gpo.gov/fdsys/pkg/FR-2013-01-15/pdf/2012-30946.pdf
- International Agency for Research on Cancer (IARC) list of classifications: https://monographs.iarc.fr/list-of-classifications-volumes/
- Ambient air pollution: A global assessment of exposure and burden of disease, WHO, 2016: http://who.int/phe/publications/air-pollution-global-assessment/en/
- Measurement of Particulate Matter, California Air Resources Board: https://www.arb.ca.gov/aaqm/qa/qa-manual/vol4/Chapter5.pdf
This information has been sourced, reviewed and adapted from materials provided by Sensirion Inc.
For more information on this source, please visit Sensirion Inc.