Browsing by Author "Laquai, Bernd"

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Open Access
Calibration method for particulate matter low-cost sensors used in ambient air quality monitoring and research
(2021) Venkatraman Jagatha, Janani; Klausnitzer, André; Chacón-Mateos, Miriam; Laquai, Bernd; Nieuwkoop, Evert; Mark, Peter van der; Vogt, Ulrich; Schneider, Christoph
Over the last decade, manufacturers have come forth with cost-effective sensors for measuring ambient and indoor particulate matter concentration. What these sensors make up for in cost efficiency, they lack in reliability of the measured data due to their sensitivities to temperature and relative humidity. These weaknesses are especially evident when it comes to portable or mobile measurement setups. In recent years many studies have been conducted to assess the possibilities and limitations of these sensors, however mostly restricted to stationary measurements. This study reviews the published literature until 2020 on cost-effective sensors, summarizes the recommendations of experts in the field based on their experiences, and outlines the quantile-mapping methodology to calibrate low-cost sensors in mobile applications. Compared to the commonly used linear regression method, quantile mapping retains the spatial characteristics of the measurements, although a common correction factor cannot be determined. We conclude that quantile mapping can be a useful calibration methodology for mobile measurements given a well-elaborated measurement plan assures providing the necessary data.
Open Access
Evaluation of a low-cost dryer for a low-cost optical particle counter
(2022) Chacón-Mateos, Miriam; Laquai, Bernd; Vogt, Ulrich; Stubenrauch, Cosima
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.
Open Access
Investigating a low-cost dryer designed for low-cost PM sensors measuring ambient air quality
(2021) Samad, Abdul; Melchor Mimiaga, Freddy Ernesto; Laquai, Bernd; Vogt, Ulrich
Air pollution in urban areas is a huge concern that demands an efficient air quality control to ensure health quality standards. The hotspots can be located by increasing spatial distribution of ambient air quality monitoring for which the low-cost sensors can be used. However, it is well-known that many factors influence their results. For low-cost Particulate Matter (PM) sensors, high relative humidity can have a significant impact on data quality. In order to eliminate or reduce the impact of high relative humidity on the results obtained from low-cost PM sensors, a low-cost dryer was developed and its effectiveness was investigated. For this purpose, a test chamber was designed, and low-cost PM sensors as well as professional reference devices were installed. A vaporizer regulated the humid conditions in the test chamber. The low-cost dryer heated the sample air with a manually adjustable intensity depending on the voltage. Different voltages were tested to find the optimum one with least energy consumption and maximum drying efficiency. The low-cost PM sensors with and without the low-cost dryer were compared. The experimental results verified that using the low-cost dryer reduced the influence of relative humidity on the low-cost PM sensor results.
Open Access
Particulate matter profiles along the rack railway route using low-cost sensor
(2021) Samad, Abdul; Maali, Anas; Laquai, Bernd; Vogt, Ulrich
Air pollution due to Particulate Matter (PM) is an increasing concern of global extent. It has been the focus of many research projects worldwide and the latest low-cost technology is offering an ease and cheap way to monitor PM concentration. In this research, a low-cost PM monitoring platform was built with the objectives of evaluating its feasibility and its performance in mobile measurements, as well as characterizing the concentration profiles of PM along the measurement route. The rack railway in Stuttgart was utilized as means of transportation for this low-cost monitoring system with which the temporal and spatial distribution of the PM10, PM2.5 and PM1 concentration along the route was attained. The measurements were conducted for around two months from mid of January until mid of March 2019, during the operation hours of the rack railway. The results showed that the PM concentrations were dominated by fine particulate matter (PM2.5 and PM1) along the route of the rack railway. Higher PM concentrations were measured near the federal highway and high traffic area as compared to the residential area. An overestimation of PM concentration using low-cost sensor platform was observed during high relative humidity conditions as compared to the professional aerosol spectrometers.