Evaluation of a low-cost dryer for a low-cost optical particle counter

Abstract. The use of low-cost sensors for air quality measurements
has become very popular in the last few decades. Due to the detrimental effects
of particulate matter (PM) on human health, PM sensors like photometers and
optical particle counters (OPCs) are widespread and have been widely
investigated. The negative effects of high relative humidity (RH) and fog
events in the mass concentration readings of these types of sensors are well documented. In the literature, different solutions to these problems – like correction models based on the Köhler theory or machine learning algorithms – have been applied. In this work, an air pre-conditioning method
based on a low-cost thermal dryer for a low-cost OPC is presented. This
study was done in two parts. The first part of the study was conducted in
the laboratory to test the low-cost dryer under two different scenarios. In
one scenario, the drying efficiency of the low-cost dryer was investigated
in the presence of fog. In the second scenario, experiments with hygroscopic
aerosols were done to determine to which extent the low-cost dryer reverts
the growth of hygroscopic particles. In the second part of the study, the
PM10 and PM2.5 mass concentrations of an OPC with dryer were compared with the gravimetric measurements and a continuous federal equivalent method (FEM) instrument in the field. The feasibility of using univariate linear regression (ULR) to correct the PM data of an OPC with dryer during field measurement was also evaluated. Finally, comparison measurements between an OPC with dryer, an OPC without dryer, and a FEM instrument during a real fog event are also presented. The laboratory results show that the sensor with the low-cost dryer at its inlet measured an average of 64 % and 59 % less PM2.5 concentration compared with a sensor without the low-cost dryer
during the experiments with fog and with hygroscopic particles,
respectively. The outcomes of the PM2.5 concentrations of the low-cost
sensor with dryer in laboratory conditions reveal, however, an excess of
heating compared with the FEM instrument. This excess of heating is also
demonstrated in a more in-depth study on the temperature profile inside the
dryer. The correction of the PM10 concentrations of the sensor with dryer during field measurements by using ULR showed a reduction of the maximum absolute error (MAE) from 4.3 µg m−3 (raw data) to 2.4 µg m−3 (after correction). The results for PM2.5 make evident an increase in the MAE after correction: from 1.9 µg m−3 in the raw
data to 3.2 µg m−3. In light of these results, a low-cost
thermal dryer could be a cost-effective add-on that could revert the effect
of the hygroscopic growth and the fog in the PM readings. However, special
care is needed when designing a low-cost dryer for a PM sensor to produce
FEM similar PM readings, as high temperatures may irreversibly change the
sampled air by evaporating the most volatile particulate species and thus
deliver underestimated PM readings. New versions of a low-cost dryer aiming
at FEM measurements should focus on maintaining the RH at the sensor inlet
at 50 % and avoid reaching temperatures higher than 40 ∘C in
the drying system. Finally, we believe that low-cost dryers have a very
promising future for the application of sensors in citizen science, sensor
networks for supplemental monitoring, and epidemiological studies.
