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Comparison of a Computational Method for Correcting the Humidity Influence with the Use of a Low-Cost Aerosol Dryer on a SDS011 Low-Cost PM-Sensor
Abstract and Figures
The first topic in the course of this work was to analyse data of a sensor node in the OK Lab air quality network that uses a SDS011 low-cost PM sensor and a low-cost temperature/humidity sensor with respect to the humidity influence. This sensor node was located close to a governmental measurement station for air quality in Munich Lothstrasse, which we used as reference. Based on the PM and humidity data collected, we tried to correct the humidity influence on the low-cost sensor. The correction was based on a growth model fit to the ratio between low-cost sensor and reference data. The second topic was to compare the computationally corrected results to results obtained from a Twin-SDS measurement box containing one SDS011 equipped with a low-cost thermal dryer and another SDS011 without thermal dryer. The measurement box was operated during various field measurements and during a co-location measurement in Munich. Particularly, hygroscopic growth events and fog events were analysed and compared to the computational correction method. By accident also two Sahara dust events were captured and analysed. The results gave additional insight into error mechanisms related to the SDS011 low-cost sensor. For the co-location experiment, the constructed Twin-SDS measurement box was operated near the governmental measurement station in Munich, Lothstrasse. For the co-location measurement period, a comparison of the dryer efficiency to the professional equipment was done. The results revealed that the SDS011 low-cost sensor data can be corrected for hygroscopic growth of PM2.5 with a growth model obtained by the help of reference equipment and using humidity data of an accurate humidity sensor only under certain conditions. The computational correction is limited to one location and a short timeframe under the condition that the hygroscopic properties of the aerosol remain stable. Additionally, a highly accurate humidity sensor suitable for outdoor use is required. A low-cost indoor humidity sensor as used in the sensor network is not sufficient. In contrast to the computational correction, the use of a thermal dryer showed a high efficiency to correct the influence of hygroscopic growth and even fog events, independent of location or time, without the need of reference equipment once being calibrated. However, the control of the dryer requires the modification of the SDS011 sensor by adding a humidity sensor into the exhaust chamber. It also would make a potential network sensor node more bulky and more costly. Nevertheless, it could be shown that the use of a thermal dryer significantly improves the data quality of the SDS011 low-cost PM sensor and achieves an excellent matching to the reference equipment of a governmental measurement station.
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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 PM 10 and PM 2.5 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 PM 2.5 concentration compared with a sensor without the low-cost dryer during the experiments with fog and with hygroscopic particles, respectively. The outcomes of the PM 2.5 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 PM 10 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 PM 2.5 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.
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