25 Apr 2022
25 Apr 2022
Status: a revised version of this preprint is currently under review for the journal AMT.

Evaluation of a Low-Cost Dryer for a Low-Cost Optical Particle Counter

Miriam Chacón-Mateos1, Bernd Laquai1, Ulrich Vogt1, and Cosima Stubenrauch2 Miriam Chacón-Mateos et al.
  • 1Department of Flue Gas Cleaning and Air Quality Control, University of Stuttgart, Stuttgart, 70569, Germany
  • 2Institute of Physical Chemistry, University of Stuttgart, Stuttgart, 70569, Germany

Abstract. The use of low-cost sensors for air quality measurements has become very popular in the last decades. Due to the detrimental effects particulate matter (PM) has on human health, PM sensors like photometers and optical particle counters (OPC) have been widely investigated. The negative effects of high relative humidity 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. The study was conducted in the laboratory under two different scenarios. In one case, we tested the efficiency of the low-cost dryer in the presence of fog. In the second case, we studied to which extent the low-cost dryer hinders the hygroscopic growth of inorganic aerosols. The results show that the sensor with the low-cost dryer at its inlet measured an average of 64 % less PM2.5 concentration during the experiments with fog compared to a sensor without the low-cost dryer. In the experiments with hygroscopic aerosols, the sensor with the low-cost dryer measured 59 % less PM2.5 concentration compared to a sensor without it. In light of these results, we believe that a low-cost, thermal dryer is a cost-effective add-on that can improve the accuracy of low-cost sensors under high relative humidity or during fog events. With the proposed air pre-conditioning method, the typical overestimation of the mass concentration readings is avoided, i.e., the sensor data are improved without the need for complex data post-processing. We believe that these low-cost dryers are very promising for the application of sensors in citizen science, in sensor networks for supplemental monitoring, and for epidemiological studies.

Miriam Chacón-Mateos et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on amt-2022-119', Anonymous Referee #1, 22 May 2022
    • AC1: 'Reply on RC1', Miriam Chacón-Mateos, 06 Sep 2022
  • RC2: 'Comment on amt-2022-119', Anonymous Referee #2, 25 Jul 2022
    • AC2: 'Reply on RC2', Miriam Chacón-Mateos, 06 Sep 2022
  • RC3: 'Comment on amt-2022-119', Anonymous Referee #3, 26 Jul 2022
    • AC3: 'Reply on RC3', Miriam Chacón-Mateos, 06 Sep 2022

Miriam Chacón-Mateos et al.

Model code and software

A low-cost dryer for Alphasense OPC-R1 sensor Miriam Chacón-Mateos; Bernd Laquai

Miriam Chacón-Mateos et al.


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Short summary
This study presents a simple method to remove the negative effect of hygroscopic growth and fog in sensor readings for particulate matter. It consists of a low-cost, thermal dryer made with a brass tube, in which a wire has been wound. By applying a specific voltage, the tube gets hot and transfers the heat to the air that flows through. Our results show a reduction in the overestimation of the mass concentrations. Low-cost dryers are very promising for sensor applications like citizen science.