What is the use and limitations of low-cost PM sensors in air quality measurements?

Technical Note No. 065

Use and Limitations of Low-Cost PM Sensors in Air Quality Measurements

Date: 12 December 2024

Author: Andrew Turnipseed

Summary:
This Technical Note provides guidance for using low-cost particulate matter (PM) Sensors in air 
quality measurements and recognizing their limitations. This Tech Note applies to the following 
2B Tech instruments that use these sensors: AQLite (standard) Air Monitoring Packages and 
Personal Air Monitors (PAM).

Tools/Materials Needed:
none

Introduction
Recently there has been an enormous surge in interest in the application of low-cost sensors to 
conduct measurements of air pollutants by educators, citizen scientists and groups interested in 
air pollution levels within their own communities. Indeed, it is now recognized that sensors can 
fill important gaps that are virtually impossible to fill with conventional or even miniaturized 
instrumentation because of their low cost, small size, and ease of deployment. However, the 
limitations of sensors must be recognized and care must be taken to obtain reliable results. 2B 
Tech incorporates several low-cost sensors in our AQLite measurement platform and our
Personal Air Monitor (PAM, previously offered by 2B Tech). In this Tech Note, we will focus on 
low-cost PM (Particulate Matter) sensors that are based on the technique of optical particle 
counting (OPC). Our Tech Note 066 focuses on electrochemical sensors.

Practical Recommendations for the Use of Low-Cost PM sensors
The low-cost PM sensor that 2B Tech is currently using in our AQLites and our more recent PAM 
is the Plantower PMS7003. Older models of our PAMs used the Plantower PMS5003, which is 
the sensor used in the popular Purple Air PA-II PM monitor. Since the PA-II PM monitor has 
such widespread use, many of the recommendations presented here were originally derived from 
studies involving the PMS5003 sensor; however, we have found that they equally apply to the 
newer PMS7003. This PM sensor outputs values for PM1, PM2.5, and PM10, which are mass 
concentrations (in µg/m3) for particles having diameters less than 1, 2.5, or 10 µm, respectively.
 (1) Plantower PM2.5 values have been shown to correlate well with established reference PM 
 techniques; however, the raw PM2.5 output of the Plantower is typically a factor of 1.2 to  2.0 larger than PM2.5 measured by reference methods (see Barkjohn et al., 2021a for example). 
As the sensor response depends upon local aerosol composition, one should “calibrate” the 
Plantower PM2.5 response to the average local aerosol mix by co-locating it with an
established reference PM method for a few days to weeks. A correction factor can then be 
derived from this intercomparison to be applied to future sensor PM2.5 data. Make sure to
include PM2.5 concentrations ≥ 5 µg/m3. Ideally, this comparison should be done a few times 
per year to account for possible seasonal changes in the aerosol mix. 
(2) Although Plantower PM2.5 measurements show good correlation with reference methods, the same cannot be said of the PM10 output. This appears to be due to a combination of issues that impact their measurement of larger particles (see Ouimette et al., 2024). Therefore, 
these sensors currently have not been shown to provide reliable measurements of PM10. 
(3) At high relative humidity (RH > 70%), water can coalesce on particulates and increase their 
size and mass. At 2B Tech, we do not automatically apply humidity corrections to data from 
our low-cost PM sensors. However, accompanying humidity measurements can be used to 
correct PM measurements during post-processing of data (see next section for more 
details).

Further Understanding of Low-Cost Particulate Matter (PM) Sensors

The Plantower PM sensors operate on the principle of Optical Particle Counting (OPC). Low-cost optical particle counters (OPCs) were originally developed for monitoring particulates in 
indoor HVAC systems. Only recently have air quality scientists realized that they often correlate well with ambient PM2.5. All OPCs (low-cost or otherwise) operate by illuminating a
flow of air typically with a small laser diode. As 
a particle flows through the laser light path, it 
can scatter light, producing a pulse of light that
is detected by a photo diode (see Figure to the right). The intensity of the light pulse increases with the size of the particle. The recorded light 
pulses are counted and binned by intensity (i.e., 
particle size) over some time period (typically a 
few seconds). From these binned counts, the 
sampling air flow rate, and an assumed particle density and shape, the mass density (µg/m3) of the particulates can be calculated. 

The light intensity vs. particle size calibration in an OPC is typically determined using particles 
that have well-defined chemical composition, size, and shape (usually spherical). However, 
ambient aerosols are not well-defined – they are highly variable in both chemical composition 
and shape, which leads to several complicating issues:

(1) A particle’s chemical composition affects its refractive index, which, in turn, determines the 
way a particulate scatters or absorbs light. For example, smoke from a fire can either
appear black (actively flaming) or white (smoldering) depending upon how hot the fire is 
burning. You see this difference because of changes in the chemical composition (and 
therefore the refractive indices) of the smoke particulates. Differing refractive indices alter 
how the light is scattered or absorbed, which then determines what light reaches one’s eyes.
In a similar fashion, particulates of varying composition will also scatter light differently in an 
optical particle counter. 
(2) To compute PM mass density (µg/m3) from particle counts it is necessary to assume both a
particulate shape and density. The shape is required to determine the volume of a particle 
whereas the density (units of mass/volume) is then used to convert that particulate volume 
to particulate mass. Both of these assumptions are complicated by the facts that ambient 
aerosols do not have uniform shapes and nor chemical composition (as discussed above) 
which determines aerosol density.

These properties of ambient aerosols are why an in situ field calibration versus reference PM 
instrumentation is necessary to derive a correction factor (sometimes referred to as a K-factor) 
to tune the OPC mass calculations to the local aerosol mix. It should be noted that this 
correction factor is needed for all OPC instruments, not just the low-cost PM sensors. Reference 
PM instrumentation that are certified by the US-EPA include gravimetric Federal reference 
methods (FRM, typically filter collection, followed by weighing of the particulate mass) or a 
gravimetrically calibrated equivalent method (FEM). Lastly, although this in situ calibration is 
reasonably robust, sudden changes in aerosol composition (for example, a wildfire smoke 
event) can alter sensor response and temporarily lead to incorrect PM mass concentrations.

Issues that relate primarily to low-cost PM sensors (and the Plantower specifically) include poor 
response to PM10 and the impact of humidity on the measured PM. PM10 is typically 
underestimated by a significant amount and often shows poor correlations with reference 
methods. A recent study (Ouimette et al., 2024) has suggested that inhomogeneities in the light 
source of Plantower sensors can lead to incorrect sizing of particles as they become larger than 
about 1 µm in diameter. Along with other known difficulties associated with larger particulates 
(such as efficient sampling into the sensor and impaction on walls within the sensor), this leads 
to underestimation of the mass contribution from larger aerosols. Although further studies on 
the response of larger particles in low-cost PM OPCs are warranted, currently PM10
measurements from these sensors (and in particular, the Plantower) should be viewed with 
skepticism.

High relative humidity (RH > 70%) causes water to coalesce on airborne particulates, increasing
their size and mass, as well as altering their refractive index. Typical PM reference methods 
and higher-cost OPCs operate by controlling the incoming RH; reducing it to ~ 25-50% RH to 
prevent this water uptake. However, humidity control is typically impractical with low-cost PM 
sensors due to the expense and larger power requirements. Therefore, many studies have 
developed algorithms to correct low-cost PM2.5 sensor data for humidity (e.g., Zheng et al., 
2018, Barkjohn et al., 2021a and 2021b). Some of these corrections are more theoretically
based, while others are strictly derived empirically from field data. As these humidity correction 
algorithms are an evolving science (Patel et al., 2024), we currently have opted not to 
automatically apply any PM humidity-based corrections. The accompanying humidity 
measurements in the AQLite or PAM can be used during data processing to derive corrections 
based on any user-preferred method. Note - when using these humidity correction algorithms
from the literature, one should be aware that there is typically a term included in these equations that accounts for the in situ “calibration” as described in Recommendation (1). 

References:
Barkjohn, K.K., Gantt, B., and Clements, A.L, “Development and Application of a United Stateswide Correction for PM2.5 Data Collected with the Purple Air Sensor,” Atmos. Meas. Tech., 14, 4617-4637, 2021a. https://doi.org/10.5194/amt-14-4617-2021

Barkjohn, K., Holder, A., Clements, A., Frederick, R., and Evans, R., “Sensor Data Cleaning and 
Correction: Application on the Air Now Fire and Smoke Map,” presented at the American 
Association for Aerosol Research Conference, Oct 18-22, 2021b, 
https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=353088&Lab=CEMM

Ouimette, J., Arnott, W.P., Laven, P., Whitwell, R., Radhakrishnan, N., Dhaniyala, S., Sandink, 
M., Tryner, J., and Volckens, J., “Fundamentals of Low-cost Aerosol Sensor Design and 
Operation,” Aerosol Sci. Tech., 58:1, 1-15, 2024, DOI: 10.1080/02786826.2023.2285935.

Patel, M.Y., Vannucci, P.F., Kim, J., Berelson, W.M, and Cohen, R.C., “Towards a Hygroscopic 
Growth Calibration for Low-cost PM2.5 Sensors,” Atmos. Meas. Tech., 17, 1051-1060, 2024. 
https://doi.org/10.5194/amt-17-1051-2024

Zheng, T., Bergin, M.H., Johnson, K.K, Tripahti, S.N., Shirodkar, S., Landis, M.S., Sutaria, R., 
and Carlson, D.E., “Field Evaluation of Low-cost Particulate Matter Sensors in High- and Lowconcentration Environments,” Atmos. Meas. Tech., 11, 4823-4846, 2018, 
https://doi.org/10.5194/amt-11-4823-2018