Evaluation of Two Low-Cost Optical Particle Counters for the Measurement of Ambient Aerosol Scattering Coefficient and Ångström Exponent

Abstract

The aerosol scattering coefficient and Ångström exponent (AE) are important parameters in the understanding of aerosol optical properties and aerosol direct effect. These parameters are usually measured by a nephelometer network which is under-represented geographically; however, a rapid growth of air-pollution monitoring, using low-cost particle sensors, may extend observation networks. This paper presents the results of co-located measurements of aerosol optical properties, such as the aerosol scattering coefficient and the scattering AE, using low-cost sensors and using a scientific-grade polar Aurora 4000 nephelometer. A high Pearson correlation coefficient (0.94–0.96) between the low-cost particulate matter (PM) mass concentration and the aerosol scattering coefficient was found. For the PM10 mass concentration, the aerosol scattering coefficient relation is linear for the Dfrobot SEN0177 sensor and non-linear for the Alphasense OPC-N2 device. After regression analyses, both low-cost instruments provided the aerosol scattering coefficient with a similar mean square error difference (RMSE) of about 20 Mm−1, which corresponds to about 27% of the mean aerosol scattering coefficient. The relative uncertainty is independent of the pollution level. In addition, the ratio of aerosol number concentration between different bins showed a significant statistical (95% of confidence level) correlation with the scattering AE. For the SEN0177, the ratio of the particle number in bin 1 (radius of 0.15–0.25 µm) to bin 4 (radius of 1.25–2.5 µm) was a linear function of the scattering AE, with a Pearson correlation coefficient of 0.74. In the case of OPC-N2, the best correlation (r = 0.66) was found for the ratio between bin 1 (radius of 0.19–0.27 µm) and bin 2 (radius of 0.27–0.39 µm). Comparisons of an estimated scattering AE from a low-cost sensor with Aurora 4000 are given with the RMSE of 0.23–0.24, which corresponds to 16–19%. In addition, a three-year (2016–2019) observation by SEN0177 indicates that this sensor can be used to determine an annual cycle as well as a short-term variability.

Low-cost air pollution monitoring system—an opportunity for reducing the health risk associated with physical activity in polluted air

“…The average power consumption of the station is equal to 5.4 W (450mA@12V). The quality of measurements made with the DFRobot sensor has been confirmed in former studies ( Rogulski, 2017 ; Markowicz & Chiliński, 2020 ).The block diagram of the station is presented in Fig. 1B , and the view of the arrangement of the components in the housing of the measuring station is shown in Fig.…”

“…In recent years, the possibility of constructing low-cost PM sensors attracted the attention of many researchers around the world ( Johnston et al, 2019 ; Chojer et al, 2020 ). Most of the research has been focused on the following issues: (i) evaluation and calibration of various PM sensor systems ( Wang et al, 2015 ; Bulot et al, 2019 ; Markowicz & Chiliński, 2020 ); (ii) detection of local sources of air pollution ( Kumar et al, 2015 ; Morawska et al, 2018 ; Rogulski, 2018 ); (iii) influence of meteorological and topographic parameters on air pollution ( Gao, Cao & Seto, 2015 ; Rogulski, 2017 ); and (iv) studies aimed at evaluating mortality risk associated with exposure to air pollution ( Castell et al, 2017 ; Chen et al, 2017 ). To the best of our knowledge, the present paper is the first attempt to draw the attention of a potential readers to potential applications of low cost PM systems that could be considered as a part of the sports infrastructure and allow the benefits of physical exercise practiced in the open air to be enhanced.…”

Comparison of Low-Cost Particulate Matter Sensors for Indoor Air Monitoring during COVID-19 Lockdown

“…Contrary to this, the PMS5003 showed about 470% and 280% higher average PM 1 and PM 2.5 , respectively, than the OPC-N3 ( Table 6 ). According to literature evaluation studies, AlphaSense shows a good correlation with reference devices even though the PM readings were found to be underestimated [ 57 , 58 , 59 ]. On the other hand, a machine learning algorithm can effectively improve the calibration and performance of low-cost optical sensors that could be used in the future [ 60 ].…”

Ice-nucleating particles in precipitation samples from the Texas Panhandle

“…Although our knowledge regarding INPs remains insufficient, there have been advances in understanding the different modes of heterogeneous ice nucleation (IN) in the atmosphere in the last few decades. For example, deposition nucleation is induced by the direct deposition of water vapor onto an INP's surface and ice embryo formation on the surface under ice supersaturation conditions (Kanji and Abbatt, 2006;Möhler et al, 2008). Recently, some studies have argued that deposition nucleation could be interpreted as pore condensation and freezing (Marcolli, 2014).…”