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In Particulate Matter (PM) monitoring, a paradigm shift towards incorporating distributed sensing approaches using low-cost sensors has begun [1]. In past research, early generations of low-cost particle sensors based on IR light scattering have been compared with official measurement stations, showing that these sensors can in principle capture the dynamics of ambient PM levels [2,3], but may suffer from low calibration stability [2], are unable to differentiate size classes [3], and may be susceptible to other sources of error [4]. Current low-cost sensor generations that rely on laser scattering claim to exhibit a better level of stability and feature internal digital processing in order to achieve more accurate results. While they are mostly designated as PM2.5 sensors, some also output values for PM10 and/or PM1. As a representative of this class of sensors, we examine the SDS011 laser-scattering PM sensor [5]. It is already widely used in deployments around the world, e.g. in the German grassroots citizen science project “luftdaten.info” (http://www.luftdaten.info), in which volunteers have deployed hundreds of these sensors in urban areas. In previous work, co-location measurements between the SDS011 have already been performed [6], the results of which indicate that the sensor delivers adequate correlation under typical conditions (relative humidity of 20-50% and PM10 mass concentrations < 20 μg/m3) but performs less well under other ambient conditions, especially high humidity. To further explore the sensor's data quality in-depth, we present the key influencing factors on measurement uncertainty of the low-cost sensor, along with a series of experiments to appropriately assess its potential and limitations: • Investigation of the humidity influence and possibilities for its compensation. • Comparison of the SDS011 sensor and a Welas2100 monitor using monodisperse aerosol of different sizes. • Characterization of the mass distributions the SDS011 can capture, based on experiments with different generated particle spectra and using the Grimm 1.108 aerosol spectrometer as reference. • Longer-term comparison (days) of 13 SDS011 and a Scanning Mobility Particle Sizer (SMPS) exposed to (1) ambient air, (2) artificial aerosol (ammonium sulfate) levels, and (3) black carbon/soot. From the results of these experiments, we present the causes of the sensor's measurement uncertainty in our talk. We show that the sensor generally does not capture PM10 satisfactorily and discuss under which conditions PM2.5 readings reflect the ambient air quality adequately.
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... Several sensors are available for the detection of PM 2.5 [10][11][12][13][14]. All of these low-cost sensors use optical light scattering. ...
... Particle size is also known to affect the particulates measurements [11,13,23,49]. Here, we noticed a larger standard error (an average 20%) for PM concentrations between 50 and 200 μg/m 3 compared to lower mass concentrations. ...
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... In order to overcome this problem and provide more spatially frequent measurements, low-cost sensors, integrated with the Internet of Things (IoT) systems became very popular. There are a number of publications that analyze the performances of low-cost sensor-based PM measuring solutions [4,[20][21][22][23][24][25][26][27][28][29][30][31][32]. It was shown that low-cost PM sensors could not replace professional stations due to their limitations in precision but could be complementarily used to enhance data availability and spatial resolution. ...
... Additionally, according to the EU report [21], the SDS011 has sufficient reliability between 0.7 and 1. However, studies showed that the precision of the SDS011 sensor is very dependent on general meteorological data (air temperature and humidity) at deployment sites [4,[20][21][22][28][29][30][33][34][35][36]. In most of the studies, it was reported that the SDS011 sensor has quite good measurement capabilities in the case of favourable meteorological conditions (moderate temperature and humidity), while in high humidity environments its precision decreases [36]. ...
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... "Low-cost" refers to the purchase price of LCS [9] compared to the purchase and operating cost of reference analyzers [46] for the monitoring of regulated inorganic pollutants and PM, which can easily be an order of magnitude more costly. More recently, ultra-affordable OEMs have started to appear on the market for PM monitoring [47][48][49]. Many of them are designed to be integrated into Internet of Things (IoT) networks of interconnected devices. ...
... The majority of records refer to commercially available OEMs and SSys, even though a few references regarding non-commercial LCS were also picked up. [84], Budde [47], Crunaire [33] For the detection of PM, the largest number of LCS tests were carried out for optical particle counters (OPC) with 752 records, followed by nephelometers with 181 records (see Table 1). Both systems detect PM by measuring the light scattered by particles, with the OPC being able to directly count particles according to their size. ...
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... In order to address these challenges, several handheld sensor system for measuring Particulate Matter (PM) have been designed and/or characterized to investigate the capabilities of low-cost commodity dust sensors [4,3,5,8]. ...
... Today, small scale exposure is either calculated indirectly, e.g. by extrapolating pollution levels from traffic data or remote sensing approaches or spatially interpolated, e.g. by fusing sparse but high quality data from official measurement grids with land use models. Emerging low-cost sensor networks have their problems too, as such sensors typically have stability issues, especially under "atypical" conditions [8]. To deal with a large number of diverse sources, networks need to be large-scale, which make the gathering, processing, assimilation and analysis of data from such distributed sensor networks challenging. ...
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... The scattered transformed into electrical signals and these amplified and processed. The number particles can be obtained by analysis because waveform has certain relations with the particles [9]. This sensor is used to measure 2.5 air pollutants and shown in Figure 5. ...
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