Comment on amt-2022-119

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.