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1
Huang, Ching-Hsuan, Jiayang He, Elena Austin, Edmund Seto, and Igor Novosselov. "Assessing the value of complex refractive index and particle density for calibration of low-cost particle matter sensor for size-resolved particle count and PM2.5 measurements." PLOS ONE 16, no. 11 (November 11, 2021): e0259745. http://dx.doi.org/10.1371/journal.pone.0259745.
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Low-cost optical scattering particulate matter (PM) sensors report total or size-specific particle counts and mass concentrations. The PM concentration and size are estimated by the original equipment manufacturer (OEM) proprietary algorithms, which have inherent limitations since particle scattering depends on particles’ properties such as size, shape, and complex index of refraction (CRI) as well as environmental parameters such as temperature and relative humidity (RH). As low-cost PM sensors are not able to resolve individual particles, there is a need to characterize and calibrate sensors’ performance under a controlled environment. Here, we present improved calibration algorithms for Plantower PMS A003 sensor for mass indices and size-resolved number concentration. An aerosol chamber experimental protocol was used to evaluate sensor-to-sensor data reproducibility. The calibration was performed using four polydisperse test aerosols. The particle size distribution OEM calibration for PMS A003 sensor did not agree with the reference single particle sizer measurements. For the number concentration calibration, the linear model without adjusting for the aerosol properties and environmental conditions yields an absolute error (NMAE) of ~ 4.0% compared to the reference instrument. The calibration models adjusted for particle CRI and density account for non-linearity in the OEM’s mass concentrations estimates with NMAE within 5.0%. The calibration algorithms developed in this study can be used in indoor air quality monitoring, occupational/industrial exposure assessments, or near-source monitoring scenarios where field calibration might be challenging.
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Hagan, David H., and Jesse H. Kroll. "Assessing the accuracy of low-cost optical particle sensors using a physics-based approach." Atmospheric Measurement Techniques 13, no. 11 (November 26, 2020): 6343–55. http://dx.doi.org/10.5194/amt-13-6343-2020.
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Abstract. Low-cost sensors for measuring particulate matter (PM) offer the ability to understand human exposure to air pollution at spatiotemporal scales that have previously been impractical. However, such low-cost PM sensors tend to be poorly characterized, and their measurements of mass concentration can be subject to considerable error. Recent studies have investigated how individual factors can contribute to this error, but these studies are largely based on empirical comparisons and generally do not examine the role of multiple factors simultaneously. Here, we present a new physics-based framework and open-source software package (opcsim) for evaluating the ability of low-cost optical particle sensors (optical particle counters and nephelometers) to accurately characterize the size distribution and/or mass loading of aerosol particles. This framework, which uses Mie theory to calculate the response of a given sensor to a given particle population, is used to estimate the fractional error in mass loading for different sensor types given variations in relative humidity, aerosol optical properties, and the underlying particle size distribution. Results indicate that such error, which can be substantial, is dependent on the sensor technology (nephelometer vs. optical particle counter), the specific parameters of the individual sensor, and differences between the aerosol used to calibrate the sensor and the aerosol being measured. We conclude with a summary of likely sources of error for different sensor types, environmental conditions, and particle classes and offer general recommendations for the choice of calibrant under different measurement scenarios.
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Kuo, Yu-Mei, Shin-Yu Weng, Sheng-Hsiu Huang, Chih-Wei Lin, and Chih-Chieh Chen. "2 Low-Cost Pm Sensor Performance Testing." Annals of Work Exposures and Health 67, Supplement_1 (May 1, 2023): i3. http://dx.doi.org/10.1093/annweh/wxac087.008.
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Abstract Low-cost particulate matters sensors (LCPMS) are widely used in indoor and outdoor air quality monitoring. Previous studies have explored the accuracy and precision of sensors, showing that LCPMS were most accurate for PM with diameters below 1 μm, and they poorly measured PM in the 2.5–5 μm range. The study aimed to investigate the aspiration and transmission efficiency of LCPMS, to set up a standard aerosol mass concentration generation system, to further comprehend the performance characteristics of LCPMS, and to examine the effect of aerosol loading. Three models of Plantower sensors were tested in this work. An ultrasonic atomizing nozzle was used to generate micrometer-sized NaCl aerosol particles. A TSI aerodynamic particle sizer was used to measure the aerosol concentrations and size distributions upstream and downstream of the LCPMS, to determine the aspiration and transmission efficiency. The mass concentration, from 20 µg/m3 to 200 mg/m3, could be varied by controlling the solution feeding rate and the solution concentration. The high mass concentration was mainly designed for aerosol loading study. The results showed that the aspiration efficiencies of the tested LCPMS were all almost 100% for particle smaller than 5 µm. The transmission efficiency of these sensors was function of particle size and strongly dependent on sampling flow. All sensors tested showed significant performance degrading when challenged with high mass concentration. The orientation of the sensor also played a role affecting the aerosol loading. These sensors performed better when mass concentration is below 100 µg/m3.
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Bulot, Florentin Michel Jacques, Hugo Savill Russell, Mohsen Rezaei, Matthew Stanley Johnson, Steven James Ossont, Andrew Kevin Richard Morris, Philip James Basford, et al. "Laboratory Comparison of Low-Cost Particulate Matter Sensors to Measure Transient Events of Pollution—Part B—Particle Number Concentrations." Sensors 23, no. 17 (September 4, 2023): 7657. http://dx.doi.org/10.3390/s23177657.
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Low-cost Particulate Matter (PM) sensors offer an excellent opportunity to improve our knowledge about this type of pollution. Their size and cost, which support multi-node network deployment, along with their temporal resolution, enable them to report fine spatio-temporal resolution for a given area. These sensors have known issues across performance metrics. Generally, the literature focuses on the PM mass concentration reported by these sensors, but some models of sensors also report Particle Number Concentrations (PNCs) segregated into different PM size ranges. In this study, eight units each of Alphasense OPC-R1, Plantower PMS5003 and Sensirion SPS30 have been exposed, under controlled conditions, to short-lived peaks of PM generated using two different combustion sources of PM, exposing the sensors’ to different particle size distributions to quantify and better understand the low-cost sensors performance across a range of relevant environmental ranges. The PNCs reported by the sensors were analysed to characterise sensor-reported particle size distribution, to determine whether sensor-reported PNCs can follow the transient variations of PM observed by the reference instruments and to determine the relative impact of different variables on the performances of the sensors. This study shows that the Alphasense OPC-R1 reported at least five size ranges independently from each other, that the Sensirion SPS30 reported two size ranges independently from each other and that all the size ranges reported by the Plantower PMS5003 were not independent of each other. It demonstrates that all sensors tested here could track the fine temporal variation of PNCs, that the Alphasense OPC-R1 could closely follow the variations of size distribution between the two sources of PM, and it shows that particle size distribution and composition are more impactful on sensor measurements than relative humidity.
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Reynaud, Adrien, Mickael Leblanc, Stéphane Zinola, Philippe Breuil, and Jean-Paul Viricelle. "Soot Particle Classifications in the Context of a Resistive Sensor Study." Proceedings 2, no. 13 (December 7, 2018): 987. http://dx.doi.org/10.3390/proceedings2130987.
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Since 2011, Euro 5b European standard limits the particle number (PN) emissions in addition to the particulate matter (PM) emissions. New thermal engines equipped vehicles have to auto-diagnose their own Diesel particulate filter (DPF) using on-board diagnostic (OBD) sensors. Accumulative resistive soot sensors seem to be good candidates for PM measurements. The aim of this study is to bring more comprehension about soot micro-structures construction in order to link the response of such a sensor to particle size and PN concentration. The sensor sensitivity to the particle size has been studied using successively an electrostatic and an aerodynamic classification, showing the same trend.
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Reynaud, Adrien, Mickaël Leblanc, Stéphane Zinola, Philippe Breuil, and Jean-Paul Viricelle. "Responses of a Resistive Soot Sensor to Different Mono-Disperse Soot Aerosols." Sensors 19, no. 3 (February 9, 2019): 705. http://dx.doi.org/10.3390/s19030705.
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Since 2011, the Euro 5b European standard limits the particle number (PN) emissions in addition to the particulate mass (PM) emissions. New thermal engine equipped vehicles also have to auto-diagnose their own particulate filter (Diesel particulate filter or gasoil particulate filter) using on-board diagnostic (OBD) sensors. Accumulative resistive soot sensors seem to be good candidates for PM measurements. The aim of this study is to bring more comprehension about soot microstructures construction in order to link the response of such a sensor to particle size and concentration. The sensor sensitivity to the particle size has been studied using successively an electrostatic and an aerodynamic classification, showing the same trend.
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Bächler, P., J. Meyer, and A. Dittler. "Characterization of the emission behavior of pulse-jet cleaned filters using a low-cost particulate matter sensor/Charakterisierung der Emission von druckstoßgereinigten Oberflächenfiltern mit einem Low-Cost-Feinstaubsensor." Gefahrstoffe 79, no. 11-12 (2019): 443–50. http://dx.doi.org/10.37544/0949-8036-2019-11-12-49.
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The reduction of fine dust emissions with pulse-jet cleaned filters plays an important role in industrial gas cleaning to meet emission standards and protect the environment. The dust emission of technical facilities is typically measured “end of pipe”, so that no information about the local emission contribution of individual filter elements exists. Cheap and compact low-cost sensors for the detection of particulate matter (PM) concentrations, which have been prominently applied for immission monitoring in recent years have the potential for emission measurement of filters to improve process monitoring. This publication discusses the suitability of a low-cost PM-sensor, the model SPS30 from the manufacturer Sensirion, in terms of the potential for particle emission measurement of surface filters in a filter test rig based on DIN ISO 11057. A Promo® 2000 in combination with a Welas® 2100 sensor serves as the optical reference device for the evaluation of the detected PM2.5 concentration and particle size distribution of the emission measured by the low-cost sensor. The Sensirion sensor shows qualitatively similar results of the detected PM2.5 emission as the low-cost sensor SDS011 from the manufacturer Nova Fitness, which was investigated by Schwarz et al. in a former study. The typical emission peak after jet-pulse cleaning of the filter, due to the penetration of particles through the filter medium, is detected during Δp-controlled operation. The particle size distribution calculated from the size resolved number concentrations of the low-cost sensor yields a distinct distribution for three different employed filter media and qualitatively fits the size distribution detected by the Palas® reference. The emission of these three different types of filter media can be distinguished clearly by the measured PM2.5 concentration and the emitted mass per cycle and filter area, demonstrating the potential for PM emission monitoring by the low-cost PM-sensor. During the period of Δt-controlled filter aging, a decreasing emission, caused by an increasing amount of stored particles in the filter medium, is detected. Due to the reduced particle emission after filter aging, the specified maximum concentration of the low-cost sensor is not exceeded so that coincidence is unlikely to affect the measurement results of the sensor for all but the very first stage of filter life.
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Li, Liangbo, Ang Chen, Tian Deng, Jin Zeng, Feifan Xu, Shu Yan, Shu Wang, Wenqing Cheng, Ming Zhu, and Wenbo Xu. "A Simple Optical Aerosol Sensing Method of Sauter Mean Diameter for Particulate Matter Monitoring." Biosensors 12, no. 7 (June 21, 2022): 436. http://dx.doi.org/10.3390/bios12070436.
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Mass concentration is a commonly used but insufficient metric to evaluate the particulate matter (PM) exposure hazard. Recent studies have declared that small particles have more serious impacts on human health than big particles given the same mass concentration. However, state-of-the-art PM sensors cannot provide explicit information of the particle size for further analysis. In this work, we adopt Sauter mean diameter (SMD) as a key metric to reflect the particle size besides the mass concentration. To measure SMD, an effective optical sensing method and a proof-of-concept prototype sensor are proposed by using dual wavelengths technology. In the proposed method, a non-linear conversion model is developed to improve the SMD measurement accuracy for aerosol samples of different particle size distributions and reflective indices based on multiple scattering channels. In the experiment of Di-Ethyl-Hexyl-Sebacate (DEHS) aerosols, the outputs of our prototype sensor demonstrated a good agreement with existing laboratory reference instruments with maximum SMD measurement error down to 7.04%. Furthermore, the simplicity, feasibility and low-cost features of this new method present great potential for distributed PM monitoring, to support sophisticated human exposure hazard assessment.
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Di Antonio, Andrea, Olalekan Popoola, Bin Ouyang, John Saffell, and Roderic Jones. "Developing a Relative Humidity Correction for Low-Cost Sensors Measuring Ambient Particulate Matter." Sensors 18, no. 9 (August 24, 2018): 2790. http://dx.doi.org/10.3390/s18092790.
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There is increasing concern about the health impacts of ambient Particulate Matter (PM) exposure. Traditional monitoring networks, because of their sparseness, cannot provide sufficient spatial-temporal measurements characteristic of ambient PM. Recent studies have shown portable low-cost devices (e.g., optical particle counters, OPCs) can help address this issue; however, their application under ambient conditions can be affected by high relative humidity (RH) conditions. Here, we show how, by exploiting the measured particle size distribution information rather than PM as has been suggested elsewhere, a correction can be derived which not only significantly improves sensor performance but which also retains fundamental information on particle composition. A particle size distribution–based correction algorithm, founded on κ -Köhler theory, was developed to account for the influence of RH on sensor measurements. The application of the correction algorithm, which assumed physically reasonable κ values, resulted in a significant improvement, with the overestimation of PM measurements reduced from a factor of ~5 before correction to 1.05 after correction. We conclude that a correction based on particle size distribution, rather than PM mass, is required to properly account for RH effects and enable low cost optical PM sensors to provide reliable ambient PM measurements.
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Oh, Kwang Chul, Kyoung Bok Lee, and Byeong Gyu Jeong. "Characteristics of Resistive PM Sensors for Onboard Diagnostics of Diesel Particulate Filter Failure." Sensors 22, no. 10 (May 16, 2022): 3767. http://dx.doi.org/10.3390/s22103767.
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In accordance with the recently reinforced exhaust regulations and onboard diagnostics regulations, it is essential to adopt diesel particulate filter systems in diesel vehicles; a sensor that directly measures particulate matter (PM) in exhaust gas is installed to precisely monitor diesel particulate filter (DPF) failure. Because the reduction of particulate matter in the diesel particulate filter system is greatly influenced by the physical wall structure of the substrate, the presence or absence of damage to the substrate wall (cracks or local melting, etc.) determines the reliability of normal DPF operation. Therefore, an onboard diagnostics sensor for particle matter is being developed with a focus on monitoring damage to the DPF wall. In this study, as a sensor for determining damage to the substrate wall, an accumulation-type sensor whose resistance changes as soot particles are deposited between two electrodes was fabricated. The sensor characteristics were investigated by changing the gap between the sensor electrodes, sensor cap shape, and electrode bias voltage to improve resistive soot sensor sensitivity and response. From the signal characteristics of various sensor configurations, a combination sensor with improved signal stability and response time is manufactured, and they were compared with the characteristics of commercially available sensors in the engine-simulated NEDC mode in terms of the degree of DPF crack. As a result of transient mode, PM monitoring cycle was improved by 1.2~1.5 times during the same vehicle driving time compared to the existing commercial sensor.
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Sahu, Ravi, Kuldeep Kumar Dixit, Suneeti Mishra, Purushottam Kumar, Ashutosh Kumar Shukla, Ronak Sutaria, Shashi Tiwari, and Sachchida Nand Tripathi. "Validation of Low-Cost Sensors in Measuring Real-Time PM10 Concentrations at Two Sites in Delhi National Capital Region." Sensors 20, no. 5 (February 29, 2020): 1347. http://dx.doi.org/10.3390/s20051347.
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In the present study, we assessed for the first time the performance of our custom-designed low-cost Particulate Matter (PM) monitoring devices (Atmos) in measuring PM10 concentrations. We examined the ambient PM10 levels during an intense measurement campaign at two sites in the Delhi National Capital Region (NCR), India. In this study, we validated the un-calibrated Atmos for measuring ambient PM10 concentrations at highly polluted monitoring sites. PM10 concentration from Atmos, containing laser scattering-based Plantower PM sensor, was comparable with that measured from research-grade scanning mobility particle sizers (SMPS) in combination with optical particle sizers (OPS) and aerodynamic particle sizers (APS). The un-calibrated sensors often provided accurate PM10 measurements, particularly in capturing real-time hourly concentrations variations. Quantile–Quantile plots (QQ-plots) for data collected during the selected deployment period showed positively skewed PM10 datasets. Strong Spearman’s rank-order correlations (rs = 0.64–0.83) between the studied instruments indicated the utility of low-cost Plantower PM sensors in measuring PM10 in the real-world context. Additionally, the heat map for weekly datasets demonstrated high R2 values, establishing the efficacy of PM sensor in PM10 measurement in highly polluted environmental conditions.
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Brattich, Erika, Alessandro Bracci, Alessandro Zappi, Pietro Morozzi, Silvana Di Sabatino, Federico Porcù, Francesca Di Nicola, and Laura Tositti. "How to Get the Best from Low-Cost Particulate Matter Sensors: Guidelines and Practical Recommendations." Sensors 20, no. 11 (May 29, 2020): 3073. http://dx.doi.org/10.3390/s20113073.
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Low-cost sensors based on the optical particle counter (OPC) are increasingly being used to collect particulate matter (PM) data at high space and time resolution. In spite of their huge explorative potential, practical guidelines and recommendations for their use are still limited. In this work, we outline a few best practices for the optimal use of PM low-cost sensors based on the results of an intensive field campaign performed in Bologna (44°30′ N, 11°21′ E; Italy) under different weather conditions. Briefly, the performances of a series of sensors were evaluated against a calibrated mainstream OPC with a heated inlet, using a robust approach based on a suite of statistical indexes capable of evaluating both correlations and biases in respect to the reference sensor. Our results show that the sensor performance is sensibly affected by both time resolution and weather with biases maximized at high time resolution and high relative humidity. Optimization of PM data obtained is therefore achievable by lowering time resolution and applying suitable correction factors for hygroscopic growth based on the inherent particle size distribution.
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Chacón-Mateos, Miriam, Bernd Laquai, Ulrich Vogt, and Cosima Stubenrauch. "Evaluation of a low-cost dryer for a low-cost optical particle counter." Atmospheric Measurement Techniques 15, no. 24 (December 22, 2022): 7395–410. http://dx.doi.org/10.5194/amt-15-7395-2022.
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Abstract. 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.
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Bučar, Klemen, Jeanne Malet, Luca Stabile, Jure Pražnikar, Stefan Seeger, and Matjaž Žitnik. "Statistics of a Sharp GP2Y Low-Cost Aerosol PM Sensor Output Signals." Sensors 20, no. 23 (November 24, 2020): 6707. http://dx.doi.org/10.3390/s20236707.
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In this work, we characterise the performance of a Sharp optical aerosol sensor model GP2Y1010AU0F. The sensor was exposed to different environments: to a clean room, to a controlled atmosphere with known aerosol size distribution and to the ambient atmosphere on a busy city street. During the exposure, the output waveforms of the sensor pulses were digitised, saved and a following offline analysis enabled us to study the behaviour of the sensor pulse-by-pulse. A linear response of the sensor on number concentration of the monosized dispersed PSL particles was shown together with an almost linear dependence on particle diameters in the 0.4 to 4 micrometer range. The gathered data about the sensor were used to predict its response to an ambient atmosphere, which was observed simultaneously with a calibrated optical particle counter.
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Jobert, Gabriel, Pierre Barritault, Maryse Fournier, Cyrielle Monpeurt, Salim Boutami, Cécile Jamois, Pietro Bernasconi, Andrea Lovera, Daniele Braga, and Christian Seassal. "Miniature Optical Particle Counter and Analyzer Involving a Fluidic-Optronic CMOS Chip Coupled with a Millimeter-Sized Glass Optical System." Sensors 21, no. 9 (May 3, 2021): 3181. http://dx.doi.org/10.3390/s21093181.
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Our latest advances in the field of miniaturized optical PM sensors are presented. This sensor combines a hybrid fluidic-optronic CMOS (holed retina) that is able to record a specific irradiance pattern scattered by an illuminated particle (scattering signature), while enabling the circulation of particles toward the sensing area. The holed retina is optically coupled with a monolithic, millimeter-sized, refracto-reflective optical system. The latter notably performs an optical pre-processing of signatures, with a very wide field of view of scattering angles. This improves the sensitivity of the sensors, and simplifies image processing. We report the precise design methodology for such a sensor, as well as its fabrication and characterization using calibrated polystyrene beads. Finally, we discuss its ability to characterize particles and its potential for further miniaturization and integration.
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Bruchkouski, Ilya, Artur Szkop, Jakub Wink, Justyna Szymkowska, and Aleksander Pietruczuk. "Multi-Sensor Instrument for Aerosol In Situ Measurements." Atmosphere 16, no. 1 (January 2, 2025): 42. https://doi.org/10.3390/atmos16010042.
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A short comparison campaign took place at the Racibórz measurement site in May 2024 to assess the consistency of the Integrated Aerosol Monitoring Unit (IAMU), which houses three PM aerosol sensors (SPS30, OPC-N3, and OPS 3330) within a single enclosure. This assessment was supported by simultaneous measurements from two reference instruments (APS 3321 and SMP S3082), along with auxiliary observations from a ceilometer and meteorological station. To enhance particle transmission efficiency to the IAMU sensors, aerodynamic modeling of the inlet pipes was performed, accounting for particle density and diameter. The primary objective of this study was to evaluate the feasibility of using the IAMU, in conjunction with optimized inlet designs, for PM monitoring under varying ambient relative humidity and sensor temperature conditions. IAMU measurements have shown large absolute differences in PM values (exceeding one order of magnitude) with moderate (>0.54 for PM10) to high (>0.82 for PM2.5 and PM1) temporal correlations. A calibration method was proposed, using reference instrument data and incorporating sensor temperature and air sample humidity information. The IAMU, combined with the developed calibration methodology, enabled the estimation of the aerosol growth factor, deliquescence point (RH ≈ 65%), and PM1 hygroscopic parameter κ (0.27–0.56) for an industrial region in Poland.
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Aguado, Alicia, Sandra Rodríguez-Sufuentes, Francisco Verdugo, Alberto Rodríguez-López, María Figols, Johannes Dalheimer, Alba Gómez-López, Rubèn González-Colom, Artur Badyda, and Jose Fermoso. "Verification and Usability of Indoor Air Quality Monitoring Tools in the Framework of Health-Related Studies." Air 3, no. 1 (January 14, 2025): 3. https://doi.org/10.3390/air3010003.
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Indoor air quality (IAQ) significantly impacts human health, particularly in enclosed spaces where people spend most of their time. This study evaluates the performance of low-cost IAQ sensors, focusing on their ability to measure carbon dioxide (CO2) and particulate matter (PM) under real-world conditions. Measurements provided by these sensors were verified against calibrated reference equipment. The study utilized two commercial devices from inBiot and Kaiterra, comparing their outputs to a reference sensor across a range of CO2 concentrations (500–1200 ppm) and environmental conditions (21–25 °C, 27–92% RH). Data were analyzed for relative error, temporal stability, and reproducibility. Results indicate strong correlation between low-cost sensors (LCSs) and the reference sensor at lower CO2 concentrations, with minor deviations at higher levels. Environmental conditions had minimal impact on sensor performance, highlighting robustness to temperature and humidity within the tested ranges. For PM measurements, low-cost sensors effectively tracked trends, but inaccuracies increased with particle concentration. Overall, these findings support the feasibility of using low-cost sensors for non-critical IAQ monitoring, offering an affordable alternative for tracking CO2 and PM trends. Additionally, LCSs can assess long-term exposure to contaminants, providing insights into potential health risks and useful information for non-expert users.
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Vogt, Matthias, Philipp Schneider, Nuria Castell, and Paul Hamer. "Assessment of Low-Cost Particulate Matter Sensor Systems against Optical and Gravimetric Methods in a Field Co-Location in Norway." Atmosphere 12, no. 8 (July 27, 2021): 961. http://dx.doi.org/10.3390/atmos12080961.
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The increased availability of commercially-available low-cost air quality sensors combined with increased interest in their use by citizen scientists, community groups, and professionals is resulting in rapid adoption, despite data quality concerns. We have characterized three out-the-box PM sensor systems under different environmental conditions, using field colocation against reference equipment. The sensor systems integrate Plantower 5003, Sensirion SPS30 and Alphasense OCP-N3 PM sensors. The first two use photometry as a measuring technique, while the third one is an optical particle counter. For the performance evaluation, we co-located 3 units of each manufacturer and compared the results against optical (FIDAS) and gravimetric (KFG) methods for a period of 7 weeks (28 August to 19 October 2020). During the period from 2nd and 5th October, unusually high PM concentrations were observed due to a long-range transport episode. The results show that the highest correlations between the sensor systems and the optical reference are observed for PM1, with coefficients of determination above 0.9, followed by PM2.5. All the sensor units struggle to correctly measure PM10, and the coefficients of determination vary between 0.45 and 0.64. This behavior is also corroborated when using the gravimetric method, where correlations are significantly higher for PM2.5 than for PM10, especially for the sensor systems based on photometry. During the long range transport event the performance of the photometric sensors was heavily affected, and PM10 was largely underestimated. The sensor systems evaluated in this study had good agreement with the reference instrumentation for PM1 and PM2.5; however, they struggled to correctly measure PM10. The sensors also showed a decrease in accuracy when the ambient size distribution was different from the one for which the manufacturer had calibrated the sensor, and during weather conditions with high relative humidity. When interpreting and communicating air quality data measured using low-cost sensor systems, it is important to consider such limitations in order not to risk misinterpretation of the resulting data.
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Si, Minxing, Ying Xiong, Shan Du, and Ke Du. "Evaluation and calibration of a low-cost particle sensor in ambient conditions using machine-learning methods." Atmospheric Measurement Techniques 13, no. 4 (April 7, 2020): 1693–707. http://dx.doi.org/10.5194/amt-13-1693-2020.
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Abstract. Particle sensing technology has shown great potential for monitoring particulate matter (PM) with very few temporal and spatial restrictions because of its low cost, compact size, and easy operation. However, the performance of low-cost sensors for PM monitoring in ambient conditions has not been thoroughly evaluated. Monitoring results by low-cost sensors are often questionable. In this study, a low-cost fine particle monitor (Plantower PMS 5003) was colocated with a reference instrument, the Synchronized Hybrid Ambient Real-time Particulate (SHARP) monitor, at the Calgary Varsity air monitoring station from December 2018 to April 2019. The study evaluated the performance of this low-cost PM sensor in ambient conditions and calibrated its readings using simple linear regression (SLR), multiple linear regression (MLR), and two more powerful machine-learning algorithms using random search techniques for the best model architectures. The two machine-learning algorithms are XGBoost and a feedforward neural network (NN). Field evaluation showed that the Pearson correlation (r) between the low-cost sensor and the SHARP instrument was 0.78. The Fligner and Killeen (F–K) test indicated a statistically significant difference between the variances of the PM2.5 values by the low-cost sensor and the SHARP instrument. Large overestimations by the low-cost sensor before calibration were observed in the field and were believed to be caused by the variation of ambient relative humidity. The root mean square error (RMSE) was 9.93 when comparing the low-cost sensor with the SHARP instrument. The calibration by the feedforward NN had the smallest RMSE of 3.91 in the test dataset compared to the calibrations by SLR (4.91), MLR (4.65), and XGBoost (4.19). After calibrations, the F–K test using the test dataset showed that the variances of the PM2.5 values by the NN, XGBoost, and the reference method were not statistically significantly different. From this study, we conclude that a feedforward NN is a promising method to address the poor performance of low-cost sensors for PM2.5 monitoring. In addition, the random search method for hyperparameters was demonstrated to be an efficient approach for selecting the best model structure.
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Nevrlý, Václav, Michal Dostál, Petr Bitala, Vít Klečka, Jiří Sléžka, Pavel Polách, Katarína Nevrlá, et al. "Varying Performance of Low-Cost Sensors During Seasonal Smog Events in Moravian-Silesian Region." Atmosphere 15, no. 11 (November 3, 2024): 1326. http://dx.doi.org/10.3390/atmos15111326.
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Air pollution monitoring in industrial regions like Moravia-Silesia faces challenges due to complex environmental conditions. Low-cost sensors offer a promising, cost-effective alternative for supplementing data from regulatory-grade air quality monitoring stations. This study evaluates the accuracy and reliability of a prototype node containing low-cost sensors for carbon monoxide (CO) and particulate matter (PM), specifically tailored for the local conditions of the Moravian-Silesian Region during winter and spring periods. An analysis of the reference data observed during the winter evaluation period showed a strong positive correlation between PM, CO, and NO2 concentrations, attributable to common pollution sources under low ambient temperature conditions and increased local heating activity. The Sensirion SPS30 sensor exhibited high linearity during the winter period but showed a systematic positive bias in PM10 readings during Polish smog episodes, likely due to fine particles from domestic heating. Conversely, during Saharan dust storm episodes, the sensor showed a negative bias, underestimating PM10 levels due to the prevalence of coarse particles. Calibration adjustments, based on the PM1/PM10 ratio derived from Alphasense OPC-N3 data, were initially explored to reduce these biases. For the first time, this study quantifies the influence of particle size distribution on the SPS30 sensor’s response during smog episodes of varying origin, under the given local and seasonal conditions. In addition to sensor evaluation, we analyzed the potential use of data from the Copernicus Atmospheric Monitoring Service (CAMS) as an alternative to increasing sensor complexity. Our findings suggest that, with appropriate calibration, selected low-cost sensors can provide reliable data for monitoring air pollution episodes in the Moravian-Silesian Region and may also be used for future adjustments of CAMS model predictions.
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Frederickson, Louise Bøge, Shanon Lim, Hugo Savill Russell, Szymon Kwiatkowski, James Bonomaully, Johan Albrecht Schmidt, Ole Hertel, Ian Mudway, Benjamin Barratt, and Matthew Stanley Johnson. "Monitoring Excess Exposure to Air Pollution for Professional Drivers in London Using Low-Cost Sensors." Atmosphere 11, no. 7 (July 15, 2020): 749. http://dx.doi.org/10.3390/atmos11070749.
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In this pilot study, low-cost air pollution sensor nodes were fitted in waste removal trucks, hospital vans and taxis to record drivers’ exposure to air pollution in Central London. Particulate matter (PM 2.5 and PM 10 ), CO 2 , NO 2 , temperature and humidity were recorded in real-time with nodes containing low-cost sensors, an electrochemical gas sensor for NO 2 , an optical particle counter for PM 2.5 and PM 10 and a non-dispersive infrared (NDIR) sensor for CO 2 , temperature and relative humidity. An intervention using a pollution filter to trap PM and NO 2 was also evaluated. The measurements were compared with urban background and roadside monitoring stations at Honor Oak Park and Marylebone Road, respectively. The vehicle records show PM and NO 2 concentrations similar to Marylebone Road and a higher NO 2 -to-PM ratio than at Honor Oak Park. Drivers are exposed to elevated pollution levels relative to Honor Oak Park: 1.72 μ g m − 3 , 1.92 μ g m − 3 and 58.38 ppb for PM 2.5 , PM 10 , and NO 2 , respectively. The CO 2 levels ranged from 410 to over 4000 ppm. There is a significant difference in average concentrations of PM 2.5 and PM 10 between the vehicle types and a non-significant difference in the average concentrations measured with and without the pollution filter within the sectors. In conclusion, drivers face elevated air pollution exposure as part of their jobs.
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Weissert, Lena Francesca, Geoff Steven Henshaw, David Edward Williams, Brandon Feenstra, Randy Lam, Ashley Collier-Oxandale, Vasileios Papapostolou, and Andrea Polidori. "Performance evaluation of MOMA (MOment MAtching) – a remote network calibration technique for PM2.5 and PM10 sensors." Atmospheric Measurement Techniques 16, no. 20 (October 18, 2023): 4709–22. http://dx.doi.org/10.5194/amt-16-4709-2023.
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Abstract. We evaluate the potential of using a previously developed remote calibration framework we name MOMA (MOment MAtching) to improve the data quality in particulate matter (PM) sensors deployed in hierarchical networks. MOMA assumes that a network of reference instruments can be used as “proxies” to calibrate the sensors given that the probability distribution over time of the data at the proxy site is similar to that at a sensor site. We use the reference network to test the suitability of proxies selected based on distance versus proxies selected based on land use similarity. The performance of MOMA for PM sensors is tested with sensors co-located with reference instruments across three Southern Californian regions, representing a range of land uses, topography and meteorology, and calibrated against a distant proxy reference. We compare two calibration approaches: one where calibration parameters get calculated and applied at monthly intervals and one which uses a drift detection framework for calibration. We demonstrate that MOMA improves the accuracy of the data when compared against the co-located reference data. The improvement was more visible for PM10 and when using the drift detection approach. We also highlight that sensor drift was associated with variations in particle composition rather than instrumental factors, explaining the better performance of the drift detection approach if wind conditions and associated PM sources varied within a month.
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Cui, Wuquan, Simona Dossi, and Guillermo Rein. "Laboratory benchmark of low-cost portable gas and particle analysers at the source of smouldering wildfires." International Journal of Wildland Fire 32, no. 11 (November 21, 2023): 1542–57. http://dx.doi.org/10.1071/wf22150.
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Background Smouldering wildfires emit large amounts of carbon, toxic gases and particulate matter (PM), posing health and environmental hazards. It is challenging to conduct field measurements on wildfire emissions, and available instruments are limited by high cost and low mobility. Aim Here, we contribute to solving this challenge by studying three commercial low-cost and portable air quality analysers (KANE101, SDS011 and FLOW) and comparing them with research-grade instruments (FTIR, PM Cascade Impactor and DustTrak). Methods A series of laboratory experiments on peat smouldering were conducted including the stages of ignition, spread and burnout to provide conditions of emission measurements near the source. Key results The gas analyser KANE101 accurately measured CO2 and allowed calculation of modified combustion efficiency (MCE). The FLOW air pollution sensor was found unsuitable for PM measurements near fire sources because of its narrow range. FLOW captured the variation of volatile organic compounds (VOCs), but measurements did not correlate well with NO2 measurements. The SDS011 PM sensor responded well in measuring PM10 in this study. Conclusions KANE101 and SDS011 can be used in the field after calibration to measure CO2/CO and PM. Implications This work provides a better understanding of how low-cost and portable emission sensors can be of use for wildfire measurements in the field.
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Kuula, Joel, Timo Mäkelä, Minna Aurela, Kimmo Teinilä, Samu Varjonen, Óscar González, and Hilkka Timonen. "Laboratory evaluation of particle-size selectivity of optical low-cost particulate matter sensors." Atmospheric Measurement Techniques 13, no. 5 (May 15, 2020): 2413–23. http://dx.doi.org/10.5194/amt-13-2413-2020.
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Abstract. Low-cost particulate matter (PM) sensors have been under investigation as it has been hypothesized that the use of low-cost and easy-to-use sensors could allow cost-efficient extension of the currently sparse measurement coverage. While the majority of the existing literature highlights that low-cost sensors can indeed be a valuable addition to the list of commonly used measurement tools, it often reiterates that the risk of sensor misuse is still high and that the data obtained from the sensors are only representative of the specific site and its ambient conditions. This implies that there are underlying reasons for inaccuracies in sensor measurements that have yet to be characterized. The objective of this study is to investigate the particle-size selectivity of low-cost sensors. Evaluated sensors were Plantower PMS5003, Nova SDS011, Sensirion SPS30, Sharp GP2Y1010AU0F, Shinyei PPD42NS, and Omron B5W-LD0101. The investigation of size selectivity was carried out in the laboratory using a novel reference aerosol generation system capable of steadily producing monodisperse particles of different sizes (from ∼0.55 to 8.4 µm) on-line. The results of the study show that none of the low-cost sensors adhered to the detection ranges declared by the manufacturers; moreover, cursory comparison to a mid-cost aerosol size spectrometer (Grimm 1.108, 2020) indicates that the sensors can only achieve independent responses for one or two size bins, whereas the spectrometer can sufficiently characterize particles with 15 different size bins. These observations provide insight into and evidence of the notion that particle-size selectivity has an essential role in the analysis of the sources of errors in sensors.
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Dbibih, Fatima-Ezzahraa, Meddy Vanotti, Valerie Soumann, Jean-Marc Cote, Lyes Djoumi, and Virginie Blondeau-Patissier. "Measurement of PM10 and PM2.5 Using SAW Sensors-Based Rayleigh Wave and Love Wave." Engineering Proceedings 6, no. 1 (May 17, 2021): 81. http://dx.doi.org/10.3390/i3s2021dresden-10129.
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Particulate matter (PM) is reported to be dangerous and can cause respiratory and health issues. Regulations, based on PM concentration, have been implemented to limit human exposition to air pollution. An innovative system with surface acoustic wave (SAW) sensors combined with a 3 Lpm cascade impactor was developed by our team for real time mass concentration measurements. In this study, we compare the PM sensitivity of two types of SAW sensors. The first one consists of delay lines based on Rayleigh waves propagating on a Lithium Niobate Y-X 128° substrate. The second one is a based-on Love waves on AT-Quartz. Aerosols were generated from NaCl for PM2.5 and from Silicon carbide for PM10. The sensors’ responses was compared to a reference sensor based on optical measurements. The sensitivity of the Rayleigh wave-based sensor is clearly lower than the Love wave sensor for both PMs. Although less sensitive, Rayleigh wave sensors remain very promising for the development of self-cleaning sensors using RF power due to their high electromechanical factor. To check the performance of our system in real conditions, we tested the sensitivity to PM from cigarette smoke using Rayleigh SAW. The PM2.5 stage showed a phase shift while the PM10 did not respond. This result agrees with previous studies which reported that the size of particles from cigarette smoke varies between 0.1 to 1.5 µm. A good correlation between the reference sensor’s response and the phase variation of SAW sensors was obtained.
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Yang, Jian, Jianan Lu, Shanmeng Zhang, and Dong Guan. "Sensitivity Analysis of the Surface Acoustic Wave Sensor towards Size-Distributed Particulate Matter." Shock and Vibration 2020 (December 23, 2020): 1–10. http://dx.doi.org/10.1155/2020/6665508.
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To study the sensitivity of the surface acoustic wave (SAW) sensor towards particulate matter (PM), an analytic model has been built based on single particle perturbation theory of full size range and the lognormal size distribution of the PM. The sensitivity of the frequency shift to 1 nanogram of PM has been calculated. The model shows that the frequency shift is a result of the competition between the negative perturbation by mass loading and the positive perturbation by elastic coupling, determined by particle size distribution parameters, material, and SAW frequency. To verify the model, the relationship of the frequency shift of a 315 MHz SAW to the concentration of aerosols generated by two kinds of powders of different sizes was measured. The experiment is in agreement with the model: the sensor has shown negative sensitivity towards aerosols generated by the finer particles of 1 μm, 3 μm polytetrafluoroethylene (PTFE), and A1 Arizona dust and positive sensitivity towards aerosols generated by the coarser particles of 10 μm PTFE and A4 Arizona dust; and the negative sensitivity is about 1 order higher than the positive.
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Makhsous, Sepehr, Joelle M. Segovia, Jiayang He, Daniel Chan, Larry Lee, Igor V. Novosselov, and Alexander V. Mamishev. "Methodology for Addressing Infectious Aerosol Persistence in Real-Time Using Sensor Network." Sensors 21, no. 11 (June 7, 2021): 3928. http://dx.doi.org/10.3390/s21113928.
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Human exposure to infectious aerosols results in the transmission of diseases such as influenza, tuberculosis, and COVID-19. Most dental procedures generate a significant number of aerosolized particles, increasing transmission risk in dental settings. Since the generation of aerosols in dentistry is unavoidable, many clinics have started using intervention strategies such as area-filtration units and extraoral evacuation equipment, especially under the relatively recent constraints of the pandemic. However, the effectiveness of these devices in dental operatories has not been studied. Therefore, the ability of dental personnel to efficiently position and operate such instruments is also limited. To address these challenges, we utilized a real-time sensor network for assessment of aerosol dynamics during dental restoration and cleaning producers with and without intervention. The strategies tested during the procedures were (i) local area High-Efficiency Particle Air (HEPA) filters and (ii) Extra-Oral Suction Device (EOSD). The study was conducted at the University of Washington School of Dentistry using a network of 13 fixed sensors positioned within the operatory and one wearable sensor worn by the dental operator. The sensor network provides time and space-resolved particulate matter (PM) data. Three-dimensional (3D) visualization informed aerosol persistence in the operatory. It was found that area filters did not improve the overall aerosol concentration in dental offices in a significant way. A decrease in PM concentration by an average of 16% was observed when EOSD equipment was used during the procedures. The combination of real-time sensors and 3D visualization can provide dental personnel and facility managers with actionable feedback to effectively assess aerosol transmission in medical settings and develop evidence-based intervention strategies.
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Casari, Martina, Laura Po, and Leonardo Zini. "AirMLP: A Multilayer Perceptron Neural Network for Temporal Correction of PM2.5 Values in Turin." Sensors 23, no. 23 (November 27, 2023): 9446. http://dx.doi.org/10.3390/s23239446.
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In recent times, pollution has emerged as a significant global concern, with European regulations stipulating limits on PM 2.5 particle levels. Addressing this challenge necessitates innovative approaches. Smart low-cost sensors suffer from imprecision, and can not replace legal stations in terms of accuracy, however, their potential to amplify the capillarity of air quality evaluation on the territory is not under discussion. In this paper, we propose an AI system to correct PM 2.5 levels in low-cost sensor data. Our research focuses on data from Turin, Italy, emphasizing the impact of humidity on low-cost sensor accuracy. In this study, different Neural Network architectures that vary the number of neurons per layer, consecutive records and batch sizes were used and compared to gain a deeper understanding of the network’s performance under various conditions. The AirMLP7-1500 model, with an impressive R-squared score of 0.932, stands out for its ability to correct PM 2.5 measurements. While our approach is tailored to the city of Turin, it offers a systematic methodology for the definition of those models and holds the promise to significantly improve the accuracy of air quality data collected from low-cost sensors, increasing the awareness of citizens and municipalities about this critical environmental information.
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Gómez-Suárez, Jaime, Patricia Arroyo, Raimundo Alfonso, José Ignacio Suárez, Eduardo Pinilla-Gil, and Jesús Lozano. "A Novel Bike-Mounted Sensing Device with Cloud Connectivity for Dynamic Air-Quality Monitoring by Urban Cyclists." Sensors 22, no. 3 (February 8, 2022): 1272. http://dx.doi.org/10.3390/s22031272.
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We present a device based on low-cost electrochemical and optical sensors, designed to be attached to bicycle handlebars, with the aim of monitoring the air quality in urban environments. The system has three electrochemical sensors for measuring NO2 and O3 and an optical particle-matter (PM) sensor for PM2.5 and PM10 concentrations. The electronic instrumentation was home-developed for this application. To ensure a constant air flow, the input fan of the particle sensor is used as an air supply pump to the rest of the sensors. Eight identical devices were built; two were collocated in parallel with a reference urban-air-quality-monitoring station and calibrated using a neural network (R2 > 0.83). Several bicycle routes were carried out throughout the city of Badajoz (Spain) to allow the device to be tested in real field conditions. An air-quality index was calculated to facilitate the user’s understanding. The results show that this index provides data on the spatiotemporal variability of pollutants between the central and peripheral areas, including changes between weekdays and weekends and between different times of the day, thus providing valuable information for citizens through a dedicated cloud-based data platform.
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Nramat, Wichai, Wasakorn Traiphat, Phuachat Sukruan, Prachum Utaprom, Saranyaras Tongsawai, Suriya Namgaew, and Suvinai Sodajaroen. "Developing a prototype centre using agricultural smart sensors to promote agrarian production with technology." EUREKA: Physics and Engineering, no. 1 (January 19, 2023): 54–66. http://dx.doi.org/10.21303/2461-4262.2023.002604.
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This article presents the development of a model center using agricultural intelligent center technology. The goal of this research is 1. To develop a wireless sensor network. 2. To be a source of learning on the use of sensor technology in agriculture. For local and nearby farmers Using the Sufficiency Economy Learning Center, according to King's Science. The Rajamangala University of Technology Suvarnabhumi is a research area. With the problems faced in farming today. It found that the world's climate change whether it's drought. Rains leave ranges and toxic airborne particulate matter caused by farming to match current problem conditions. The researchers then designed a two-part system: 1. Node Moisture Sensor that measures soil moisture and commands the opening – It also controls on-off with a manual switch. Wind speed and wind direction sensors, light intensity sensors, temperature, and humidity sensors, and Particulate Matters Sensor 1.0, 2.5, 10 with environmental reports within the growing area via Wi-F signals to (Sever) Raspberry Pi record real-time data. Every 30 seconds According to research, node moisture sensors can measure soil moisture and record results, and the station measures the environment within the growing area via a Wi-F signal to (Sever) Raspberry Pi. Rainfall values measured by local rainfall sensors measuring up to 35.3 mm are within the threshold of heavy rain. The maximum wind speed measured is 8.5 km/h, the maximum temperature of 35.8 degrees Celsius, and the maximum humidity of 99.9 percent, the light intensity is up to 58,002 Lux, and the Final Particles, with pm 1.0 up to 40.1 microns, PM 2.5 up to 51.3 microns and PM 10 up to 63.5 microns. Apply agriculture to 50 interested farmers after receiving knowledge transfer of smart sensor technology. The expansion has resulted in 3 farmers and will continue to expand in the future. Promote the use of agricultural technology. Intensifying communities and supporting global climate change
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Jiao, Wan, Gayle Hagler, Ronald Williams, Robert Sharpe, Ryan Brown, Daniel Garver, Robert Judge, et al. "Community Air Sensor Network (CAIRSENSE) project: evaluation of low-cost sensor performance in a suburban environment in the southeastern United States." Atmospheric Measurement Techniques 9, no. 11 (November 1, 2016): 5281–92. http://dx.doi.org/10.5194/amt-9-5281-2016.
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Abstract. Advances in air pollution sensor technology have enabled the development of small and low-cost systems to measure outdoor air pollution. The deployment of a large number of sensors across a small geographic area would have potential benefits to supplement traditional monitoring networks with additional geographic and temporal measurement resolution, if the data quality were sufficient. To understand the capability of emerging air sensor technology, the Community Air Sensor Network (CAIRSENSE) project deployed low-cost, continuous, and commercially available air pollution sensors at a regulatory air monitoring site and as a local sensor network over a surrounding ∼ 2 km area in the southeastern United States. Collocation of sensors measuring oxides of nitrogen, ozone, carbon monoxide, sulfur dioxide, and particles revealed highly variable performance, both in terms of comparison to a reference monitor as well as the degree to which multiple identical sensors produced the same signal. Multiple ozone, nitrogen dioxide, and carbon monoxide sensors revealed low to very high correlation with a reference monitor, with Pearson sample correlation coefficient (r) ranging from 0.39 to 0.97, −0.25 to 0.76, and −0.40 to 0.82, respectively. The only sulfur dioxide sensor tested revealed no correlation (r < 0.5) with a reference monitor and erroneously high concentration values. A wide variety of particulate matter (PM) sensors were tested with variable results – some sensors had very high agreement (e.g., r = 0.99) between identical sensors but moderate agreement with a reference PM2.5 monitor (e.g., r = 0.65). For select sensors that had moderate to strong correlation with reference monitors (r > 0.5), step-wise multiple linear regression was performed to determine if ambient temperature, relative humidity (RH), or age of the sensor in number of sampling days could be used in a correction algorithm to improve the agreement. Maximum improvement in agreement with a reference, incorporating all factors, was observed for an NO2 sensor (multiple correlation coefficient R2adj-orig = 0.57, R2adj-final = 0.81); however, other sensors showed no apparent improvement in agreement. A four-node sensor network was successfully able to capture ozone (two nodes) and PM (four nodes) data for an 8-month period of time and show expected diurnal concentration patterns, as well as potential ozone titration due to nearby traffic emissions. Overall, this study demonstrates the performance of emerging air quality sensor technologies in a real-world setting; the variable agreement between sensors and reference monitors indicates that in situ testing of sensors against benchmark monitors should be a critical aspect of all field studies.
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Wagner, Jeff, Rosemary Castorina, Kazukiyo Kumagai, McKenna Thompson, Rebecca Sugrue, Elizabeth M. Noth, Asa Bradman, and Susan Hurley. "Identification of Airborne Particle Types and Sources at a California School Using Electron Microscopy." Atmosphere 14, no. 11 (November 20, 2023): 1702. http://dx.doi.org/10.3390/atmos14111702.
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We conducted a pilot study to investigate air quality indoors in two classrooms and outdoors on the school grounds in a California community with historically high PM2.5 (fine particulate matter, diameter < 2.5 μm). We used computer-controlled scanning electron microscopy of passive samples to identify major PM types, which were used to help interpret continuous PM2.5 and black carbon sensor data. The five major PM types were sodium salt particles with sulfur, calcium, or chlorine; aluminosilicate dusts; carbonaceous combustion agglomerates; biogenic particles; and metal-rich particles. Based on morphological evidence of water droplets, the salt particles are hypothesized to be secondary aerosols formed via the reaction of sodium chloride fog droplets with sulfur from regional sources. The carbonaceous agglomerates had unusual morphologies consistent with low-temperature combustion and smoke from open-burning activities observed nearby. The passive PM sampler and continuous sensor results indicated lower concentrations in the classroom equipped with an air cleaner. Passive samples collected in one classroom exhibited enhanced PM10–2.5 crustal particles and PM2.5 metal particles, suggesting a potential local PM source in that room. Future study designs that enable longer passive sampling times would reduce detection limits and sample contamination concerns. The determination of major airborne particle types in a given environment makes this technique a useful and unique community exposure assessment tool, even in these limited-duration (48 h) deployments.
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Stavroulas, Iasonas, Georgios Grivas, Panagiotis Michalopoulos, Eleni Liakakou, Aikaterini Bougiatioti, Panayiotis Kalkavouras, Kyriaki Fameli, Nikolaos Hatzianastassiou, Nikolaos Mihalopoulos, and Evangelos Gerasopoulos. "Field Evaluation of Low-Cost PM Sensors (Purple Air PA-II) Under Variable Urban Air Quality Conditions, in Greece." Atmosphere 11, no. 9 (August 29, 2020): 926. http://dx.doi.org/10.3390/atmos11090926.
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Recent advances in particle sensor technologies have led to an increased development and utilization of low-cost, compact, particulate matter (PM) monitors. These devices can be deployed in dense monitoring networks, enabling an improved characterization of the spatiotemporal variability in ambient levels and exposure. However, the reliability of their measurements is an important prerequisite, necessitating rigorous performance evaluation and calibration in comparison to reference-grade instrumentation. In this study, field evaluation of Purple Air PA-II devices (low-cost PM sensors) is performed in two urban environments and across three seasons in Greece, in comparison to different types of reference instruments. Measurements were conducted in Athens (the largest city in Greece with nearly four-million inhabitants) for five months spanning over the summer of 2019 and winter/spring of 2020 and in Ioannina, a medium-sized city in northwestern Greece (100,000 inhabitants) during winter/spring 2019–2020. The PM2.5 sensor output correlates strongly with reference measurements (R2 = 0.87 against a beta attenuation monitor and R2 = 0.98 against an optical reference-grade monitor). Deviations in the sensor-reference agreement are identified as mainly related to elevated coarse particle concentrations and high ambient relative humidity. Simple and multiple regression models are tested to compensate for these biases, drastically improving the sensor’s response. Large decreases in sensor error are observed after implementation of models, leading to mean absolute percentage errors of 0.18 and 0.12 for the Athens and Ioannina datasets, respectively. Overall, a quality-controlled and robustly evaluated low-cost network can be an integral component for air quality monitoring in a smart city. Case studies are presented along this line, where a network of PA-II devices is used to monitor the air quality deterioration during a peri-urban forest fire event affecting the area of Athens and during extreme wintertime smog events in Ioannina, related to wood burning for residential heating.
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Sousan, Sinan, Swastika Regmi, and Yoo Min Park. "Laboratory Evaluation of Low-Cost Optical Particle Counters for Environmental and Occupational Exposures." Sensors 21, no. 12 (June 17, 2021): 4146. http://dx.doi.org/10.3390/s21124146.
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Low-cost optical particle counters effectively measure particulate matter (PM) mass concentrations once calibrated. Sensor calibration can be established by deriving a linear regression model by performing side-by-side measurements with a reference instrument. However, calibration differences between environmental and occupational settings have not been demonstrated. This study evaluated four commercially available, low-cost PM sensors (OPC-N3, SPS30, AirBeam2, and PMS A003) in both settings. The mass concentrations of three aerosols (salt, Arizona road dust, and Poly-alpha-olefin-4 oil) were measured and compared with a reference instrument. OPC-N3 and SPS30 were highly correlated (r = 0.99) with the reference instrument for all aerosol types in environmental settings. In occupational settings, SPS30, AirBeam2, and PMS A003 exhibited high correlation (>0.96), but the OPC-N3 correlation varied (r = 0.88–1.00). Response significantly (p < 0.001) varied between environmental and occupational settings for most particle sizes and aerosol types. Biases varied by particle size and aerosol type. SPS30 and OPC-N3 exhibited low bias for environmental settings, but all of the sensors showed a high bias for occupational settings. For intra-instrumental precision, SPS30 exhibited high precision for salt for both settings compared to the other low-cost sensors and aerosol types. These findings suggest that SPS30 and OPC-N3 can provide a reasonable estimate of PM mass concentrations if calibrated differently for environmental and occupational settings using site-specific calibration factors.
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Bulot, Florentin Michel Jacques, Hugo Savill Russell, Mohsen Rezaei, Matthew Stanley Johnson, Steven James Johnston Ossont, Andrew Kevin Richard Morris, Philip James Basford, et al. "Laboratory Comparison of Low-Cost Particulate Matter Sensors to Measure Transient Events of Pollution." Sensors 20, no. 8 (April 15, 2020): 2219. http://dx.doi.org/10.3390/s20082219.
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Airborne particulate matter (PM) exposure has been identified as a key environmental risk factor, associated especially with diseases of the respiratory and cardiovascular system and with almost 9 million premature deaths per year. Low-cost optical sensors for PM measurement are desirable for monitoring exposure closer to the personal level and particularly suited for developing spatiotemporally dense city sensor networks. However, questions remain over the accuracy and reliability of the data they produce, particularly regarding the influence of environmental parameters such as humidity and temperature, and with varying PM sources and concentration profiles. In this study, eight units each of five different models of commercially available low-cost optical PM sensors (40 individual sensors in total) were tested under controlled laboratory conditions, against higher-grade instruments for: lower limit of detection, response time, responses to sharp pollution spikes lasting <1 min , and the impact of differing humidity and PM source. All sensors detected the spikes generated with a varied range of performances depending on the model and presenting different sensitivity mainly to sources of pollution and to size distributions with a lesser impact of humidity. The sensitivity to particle size distribution indicates that the sensors may provide additional information to PM mass concentrations. It is concluded that improved performance in field monitoring campaigns, including tracking sources of pollution, could be achieved by using a combination of some of the different models to take advantage of the additional information made available by their differential response.
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Kaliszewski, Miron, Maksymilian Włodarski, Jarosław Młyńczak, and Krzysztof Kopczyński. "Comparison of Low-Cost Particulate Matter Sensors for Indoor Air Monitoring during COVID-19 Lockdown." Sensors 20, no. 24 (December 18, 2020): 7290. http://dx.doi.org/10.3390/s20247290.
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This study shows the results of air monitoring in high- and low-occupancy rooms using two combinations of sensors, AeroTrak8220(TSI)/OPC-N3 (AlphaSense, Great Notley, UK) and OPC-N3/PMS5003 (Plantower, Beijing, China), respectively. The tests were conducted in a flat in Warsaw during the restrictions imposed due to the COVID-19 lockdown. The results showed that OPC-N3 underestimates the PN (particle number concentration) by about 2–3 times compared to the AeroTrak8220. Subsequently, the OPC-N3 was compared with another low-cost sensor, the PMS5003. Both devices showed similar efficiency in PN estimation, whereas PM (particulate matter) concentration estimation differed significantly. Moreover, the relationship among the PM1–PM2.5–PM10 readings obtained with the PMS5003 appeared improbably linear regarding the natural indoor conditions. The correlation of PM concentrations obtained with the PMS5003 suggests an oversimplified calculation method of PM. The studies also demonstrated that PM1, PM2.5, and PM10 concentrations in the high- to low-occupancy rooms were about 3, 2, and 1.5 times, respectively. On the other hand, the use of an air purifier considerably reduced the PM concentrations to similar levels in both rooms. All the sensors showed that frying and toast-making were the major sources of particulate matter, about 10 times higher compared to average levels. Considerably lower particle levels were measured in the low-occupancy room.
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Jiang, Hao, and Keith Kolaczyk. "Quantification of Size-Binned Particulate Matter in Electronic Cigarette Aerosols Using Multi-Spectral Optical Sensing and Machine Learning." Sensors 24, no. 21 (November 3, 2024): 7082. http://dx.doi.org/10.3390/s24217082.
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To monitor health risks associated with vaping, we introduce a multi-spectral optical sensor powered by machine learning for real-time characterization of electronic cigarette aerosols. The sensor can accurately measure the mass of particulate matter (PM) in specific particle size channels, providing essential information for estimating lung deposition of vaping aerosols. For the sensor’s input, wavelength-specific optical attenuation signals are acquired for three separate wavelengths in the ultraviolet, red, and near-infrared range, and the inhalation pressure is collected from a pressure sensor. The sensor’s outputs are PM mass in three size bins, specified as 100–300 nm, 300–600 nm, and 600–1000 nm. Reference measurements of electronic cigarette aerosols, obtained using a custom vaping machine and a scanning mobility particle sizer, provided the ground truth for size-binned PM mass. A lightweight two-layer feedforward neural network was trained using datasets acquired from a wide range of puffing conditions. The performance of the neural network was tested using unseen data collected using new combinations of puffing conditions. The model-predicted values matched closely with the ground truth, and the accuracy reached 81–87% for PM mass in three size bins. Given the sensor’s straightforward optical configuration and the direct collection of signals from undiluted vaping aerosols, the achieved accuracy is notably significant and sufficiently reliable for point-of-interest sensing of vaping aerosols. To the best of our knowledge, this work represents the first instance where machine learning has been applied to directly characterize high-concentration undiluted electronic cigarette aerosols. Our sensor holds great promise in tracking electronic cigarette users’ puff topography with quantification of size-binned PM mass, to support long-term personalized health and wellness.
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Xiao, Xiao, Ming Zhu, Qiuyu Wang, Xiaodong Yuan, and Mengxue Lin. "A Three-Wavelength Optical Sensor for Measuring the Multi-Particle-Size Channel Mass Concentration of Thermal Power Plant Emissions." Sensors 24, no. 5 (February 22, 2024): 1424. http://dx.doi.org/10.3390/s24051424.
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Emissions from thermal power plants have always been the central consideration for environmental protection. Existing optical sensors in thermal power plants usually measure the total mass concentration of the particulate matter (PM) by a single-wavelength laser, bearing intrinsic errors owing to the variation in particle size distribution (PSD). However, the total mass concentration alone cannot characterize all the harmful effects of the air pollution caused by the power plant. Therefore, it is necessary to measure the mass concentration and PSD simultaneously, based on which we can obtain multi-particle-size channel mass concentration. To achieve this, we designed an optical sensor based on the three-wavelength technique and tested its performance in a practical environment. Results showed that the prototype cannot only correctly measure the mass concentration of the emitted PM but also determine the mean diameter and standard deviation of the PSDs. Hence, the mass concentrations of PM10, PM2.5, and PM1 are calculated, and the air pollutants emission by a thermal power plant can be estimated comprehensively.
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Feng, Zikang, Lina Zheng, Lingyu Liu, and Wenli Zhang. "Real-Time PM2.5 Monitoring in a Diesel Generator Workshop Using Low-Cost Sensors." Atmosphere 13, no. 11 (October 26, 2022): 1766. http://dx.doi.org/10.3390/atmos13111766.
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Particulates from diesel generator operation are a known air pollutant with adverse health effects. In this study, we used low-cost particulate matter (PM) sensors to monitor PM2.5 in a diesel generator plant. We compared the measurement results from a PM sensor and a reference instrument (DustTrak), and we found a high correlation between them. The data overestimation or underestimation of PM sensors implied the need for data calibration. Hence, we proposed a data calibration algorithm based on a nonlinear support vector machines (SVM) model, and we investigated the effect of three calibration factors on the model: humidity, temperature, and total volatile organic compounds (TVOC). It was found that the TVOC correction coefficient has great influence on the model, which should be considered when calibrating the low-cost PM sensor in diesel generator operation sites. A monitoring network with six low-cost sensors was installed in the diesel generator plant to monitor PM2.5 concentration. It was found that normal diesel generator work, diesel generator set handling work, and human activity are the most dominant ways of producing particulate matter at the site, and dispersion is the main cause of increased PM2.5 concentrations in nonworking areas. In this study, PM2.5 emissions from two different diesel generators were tested, and PM2.5 concentrations at monitoring points reached 220 μg/m3 and 120 μg/m3, respectively. This further confirms that diesel generators produce many respirable particles when working.
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Liu, Rui-Tao, Lu-Qi Tao, Yi Yang, and Tian-Ling Ren. "Simulation on a novel micron-array inertial impactor for submicron and ultrafine particle separation." Modern Physics Letters B 30, no. 22 (August 20, 2016): 1650273. http://dx.doi.org/10.1142/s0217984916502730.
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The particulate matter (PM), which was put forward in 1997 by US, had taken more and more attention due to the influence on human health. Although the mass concentration, number concentration and chemical composition of PM were still major research directions, how to collect these PMs more efficiently becomes critical. Inertial impactor is an effective separation device, however, due to different motion states of PM[Formula: see text] and PM[Formula: see text] in the flow field, the inertial impactor which can separate PM[Formula: see text] from other PMs has not been fabricated. In this work, the motion states for both submicron and ultrafine particles were studied by using classical theory of channel aerodynamic, and a novel micron-array inertial impactor was designed and simulated for the first time. Besides, the influence of some characteristic parameters (W, T, S, Dc, etc.) on particle collection efficiency were researched and discussed through simulation results. This novel structure can be easily fabricated by MEMS technology or laser direct writing and also can be widely used in particle separation or flexible sensor fields.
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Kulikova, Tatjana, Anna Porfireva, Alexey Rogov, and Gennady Evtugyn. "Electrochemical DNA Sensor Based on Acridine Yellow Adsorbed on Glassy Carbon Electrode." Sensors 21, no. 22 (November 22, 2021): 7763. http://dx.doi.org/10.3390/s21227763.
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Electrochemical DNA sensors offer unique opportunities for the sensitive detection of specific DNA interactions. In this work, a voltametric DNA sensor is proposed on the base of glassy carbon electrode modified with carbon black, adsorbed acridine yellow and DNA for highly sensitive determination of doxorubicin antitumor drug. The signal recorded by cyclic voltammetry was attributed to irreversible oxidation of the dye. Its value was altered by aggregation of the hydrophobic dye molecules on the carbon black particles. DNA molecules promote disaggregation of the dye and increased the signal. This effect was partially suppressed by doxorubicin compensate for the charge of DNA in the intercalation. Sensitivity of the signal toward DNA and doxorubicin was additionally increased by treatment of the layer with dimethylformamide. In optimal conditions, the linear range of doxorubicin concentrations determined was 0.1 pM–1.0 nM, and the detection limit was 0.07 pM. No influence of sulfonamide medicines and plasma electrolytes on the doxorubicin determination was shown. The DNA sensor was tested on two medications (doxorubicin-TEVA and doxorubicin-LANS) and showed recoveries of 102–105%. The DNA sensor developed can find applications in the determination of drug residues in blood and for the pharmacokinetics studies.
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Sutter, Benjamin, Alexis Boivin, Raphaël Payet, Xavier Simon, Sébastien Bau, and Olivier Witschger. "118 Performances of Low-Cost Sensors Exposed to Airborne NOAA Powders." Annals of Work Exposures and Health 67, Supplement_1 (May 1, 2023): i30. http://dx.doi.org/10.1093/annweh/wxac087.080.
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Abstract Assessing occupational inhalation exposure to airborne nano-objects, their agglomerates and aggregates (NOAA) is a challenge due to the multiple possibilities to measure them in the absence of regulation. The size range of NOAA is very wide, from sub-100 nm nanoparticles to micron-sized agglomerates and aggregates. Consequently, a suite of middle to high costs direct-reading instruments are commonly used to determine airborne NOAA concentrations in workplaces. Due to the high cost of such devices, measurements with high spatial resolution are not feasible. New Low-Cost Sensors (LCS) have emerged since the last ten years, providing a high degree of compactness, a high time resolution and a reduced price (<200 €). Consequently, these sensors offer new possibilities to assess workers’ exposure during their activity. Since these sensors are typically calibrated for the measurement of ambient PM concentrations, their use in workplaces may be limited due to different particle properties or when faced with medium to high occupational mass concentrations. In order to study the applicability of LCS for NOAA dust concentration measurements in workplaces, a pre-normative research project has been initiated. Six different sensor types have been tested, namely Plantower PMS 7003, Nova Fitness SDS011, Alphasense OPC-R1, Sensirion SPS30 and Groupe Tera Next PM and Next PM-CR. 9 NOAA powders were involved to produce the test aerosols in a low-speed wind tunnel. The results show that sensors output vary strongly with the powder’s nature. In addition, the PM fractions from the LCS are correlated to the PM fractions measured by the reference method.
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Rodríguez Rama, Juan Antonio, Leticia Presa Madrigal, Jorge L. Costafreda Mustelier, Ana García Laso, Javier Maroto Lorenzo, and Domingo A. Martín Sánchez. "Monitoring and Ensuring Worker Health in Controlled Environments Using Economical Particle Sensors." Sensors 24, no. 16 (August 14, 2024): 5267. http://dx.doi.org/10.3390/s24165267.
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Nowadays, indoor air quality monitoring has become an issue of great importance, especially in industrial spaces and laboratories where materials are handled that may release particles into the air that are harmful to health. This study focuses on the monitoring of air quality and particle concentration using low-cost sensors (LCSs). To carry out this work, particulate matter (PM) monitoring sensors were used, in controlled conditions, specifically focusing on particle classifications with PM2.5 and PM10 diameters: the Nova SDS011, the Sensirion SEN54, the DFRobot SEN0460, and the Sensirion SPS30, for which an adapted environmental chamber was built, and gaged using the Temtop M2000 2nd as a reference sensor (SRef). The main objective was to preliminarily assess the performance of the sensors, to select the most suitable ones for future research and their possible use in different work environments. The monitoring of PM2.5 and PM10 particles is essential to ensure the health of workers and avoid possible illnesses. This study is based on the comparison of the selected LCS with the SRef and the results of the comparison based on statistics. The results showed variations in the precision and accuracy of the LCS as opposed to the SRef. Additionally, it was found that the Sensirion SEN54 was the most suitable and valuable tool to be used to maintain a safe working environment and would contribute significantly to the protection of the workers’ health.
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Veeramanikandasamy*, T., Gokul Raj. S, A. Balamurugan, A. P. Ramesh, and Y. A. Syed Khadar. "IoT based Real-time Air Quality Monitoring and Control System to Improve the Health and Safety of Industrial Workers." International Journal of Innovative Technology and Exploring Engineering 9, no. 4 (February 28, 2020): 1889–84. http://dx.doi.org/10.35940/ijrte.d1604.018520.
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Pollution is adding contaminants into the nature that causes an adverse change in the environment. Air pollution is one of the highest mortality risk factors globally. The sources of air pollution in the industries are power plants, chamber process, cleaning, burning of materials, etc. A variety of pollutants emitted into the air such as sulfur dioxide, carbon monoxide, carbon dioxide, ammonia and volatile organic compounds. Particulate Matter (PM) is an air pollutant that is the mixture of solid dust or pollen and liquid droplets with air. Air pollution in industrial workplaces is a major concern and monitoring and management of it to be addressed to protect the industrial workers health from the air pollution effects. The people are suffering from several respiratory and heart issues along with cancer due to increasing air pollution. This device is composed of ESP32 MCU, MQ135 gas sensor, SDS011 optical dust particle sensor, and BME280 humidity and temperature sensor for monitoring the air quality. The gas sensor MQ135 senses the harmful gases present in the environment. SDS011 optical dust sensor senses the PM levels (PM10 and PM2.5) in the atmosphere. The sensor values are evaluated for the Air Quality Index (AQI) and display it on the ThingSpeak IoT platform. Vrituino app has used for a virtual screen with widgets on the mobile phone to monitor the system using the web. In order to improve the real-time performance of the system, an IoT and a cloud computing technology are being used. The ESP32 turns on the fan units to maintain the pollutants within the safe limit when the presence of harmful gases and PM levels exceeds a certain threshold level. This system is essential for industrial work places to adopt measures and control air pollution which increase industrial workers safety.
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Ali Shah, Syed Mohsin, Diego Casado-Mansilla, and Diego López-de-Ipiña. "An Image-Based Sensor System for Low-Cost Airborne Particle Detection in Citizen Science Air Quality Monitoring." Sensors 24, no. 19 (October 4, 2024): 6425. http://dx.doi.org/10.3390/s24196425.
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Air pollution poses significant public health risks, necessitating accurate and efficient monitoring of particulate matter (PM). These organic compounds may be released from natural sources like trees and vegetation, as well as from anthropogenic, or human-made sources including industrial activities and motor vehicle emissions. Therefore, measuring PM concentrations is paramount to understanding people’s exposure levels to pollutants. This paper introduces a novel image processing technique utilizing photographs/pictures of Do-it-Yourself (DiY) sensors for the detection and quantification of PM10 particles, enhancing community involvement and data collection accuracy in Citizen Science (CS) projects. A synthetic data generation algorithm was developed to overcome the challenge of data scarcity commonly associated with citizen-based data collection to validate the image processing technique. This algorithm generates images by precisely defining parameters such as image resolution, image dimension, and PM airborne particle density. To ensure these synthetic images mimic real-world conditions, variations like Gaussian noise, focus blur, and white balance adjustments and combinations were introduced, simulating the environmental and technical factors affecting image quality in typical smartphone digital cameras. The detection algorithm for PM10 particles demonstrates robust performance across varying levels of noise, maintaining effectiveness in realistic mobile imaging conditions. Therefore, the methodology retains sufficient accuracy, suggesting its practical applicability for environmental monitoring in diverse real-world conditions using mobile devices.
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Bard, Delphine, Graeme Hunwin, Eelco Kuijpers, Sander Ruiter, Emanuele Cauda, Jean-Philippe Gorce, John Snawder, Anjoeka Pronk, John Saunders, and Nick Warren. "124 Laboratory Evaluation of Low-Cost Optical Particle Counters for Occupational Respirable Exposure Measurements." Annals of Work Exposures and Health 67, Supplement_1 (May 1, 2023): i31. http://dx.doi.org/10.1093/annweh/wxac087.083.
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Abstract Direct-reading, time-resolved devices, such as optical particle counters (OPCs) and photometers, offer a unique insight into the temporal and spatial distribution of airborne particles. They can provide a comprehensive picture of changes in concentration of airborne particles in occupational settings and therefore can be used to investigate failures in engineering control systems as well as identify exposures driven by working procedures and methods. In recent years, new developments have led to the commercialisation of low- cost optical-based sensors, which provide particle matter (PM) mass concentrations including PM2.5 and PM10 for environmental monitoring. TNO, NIOSH, and HSE are investigating their application to occupational settings with the aim to produce guidelines for calibration and use. This study evaluated the performance and accuracy of six commercially available low-cost sensors (Airbeam 2, Airveda, Omni Awair, OPC-N3, OPC-R1 and PATS+), in calm air test chambers, against reference devices including an Aerodynamic Particle Sizer (APS 3320), GK2.69 respirable cyclones, and pDR-1500 photometers. Several factors were considered: type of dust (particles having different size distribution, shape and refractive index), within- and between-device variations and exposure pattern (peak and constant concentrations). The devices were subjected to relatively high respirable concentrations (greater than 1 mg/m3) in addition to low concentrations. This presentation will present the results of the laboratory testing with particular focus on their accuracy, response, and calibration for quantitative exposure measurements. The low-cost sensor devices are also being deployed in the workplace for further evaluation and practicality of use.
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Amara, Selma, Abdulrahman Aljedaibi, Ali Alrashoudi, Sofiane Ben Mbarek, Danial Khan, and Yehia Massoud. "High-performance MTJ-based sensors for monitoring of atmospheric pollution." AIP Advances 13, no. 3 (March 1, 2023): 035329. http://dx.doi.org/10.1063/9.0000496.
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Solid and liquid particles in the atmosphere, referred to as airborne particulate matter (PM), have been rising significantly over the past two decades. Exposure to PM carries significant health risks such as lungs damage, heart disease, cancer, and death. PM2.5 is a subgroup of PM particles that are smaller than 2.5 µm and is a major concern as it is more harmful to health and more difficult to detect. One problematic component of PM2.5 is magnetite nanoparticles (<200 nm), which are readily absorbed into the bloodstream through the respiratory system. Eventually, magnetite nanoparticles deposit inside the brain causing neurodegenerative diseases such as Alzheimer’s or cancerous tumors by inducing oxidative stress. Additionally, Magnetite nanoparticles are often surrounded by heavy metal nanoparticles such as Cadmium and lead which are a great concern to the environment and health. Traditional PM detection methods such as laser scattering are bulky, expensive, and incapable of detecting particles smaller than 200 nm such as magnetite nanoparticles. Therefore, developing a low-cost highly sensitive sensor for monitoring magnetite nanoparticles is vital. Tunneling Magneto-Resistance (TMR) sensors are an attractive option due to their low-cost and high sensitivity toward magnetic nanoparticle detection. Moreover, developing a cheap, portable, and precise remote monitoring technique will allow for the creation of high spatial resolution highly sensitive monitoring networks for magnetic PM2.5. This work focuses on developing, modeling, and simulation of low-cost highly sensitive TMR sensor based on Magnetic Tunnel Junction (MTJ) that can detect and count magnetite nanoparticles.
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Masic, Adnan, Dzevad Bibic, Boran Pikula, Almir Blazevic, Jasna Huremovic, and Sabina Zero. "Evaluation of optical particulate matter sensors under realistic conditions of strong and mild urban pollution." Atmospheric Measurement Techniques 13, no. 12 (November 30, 2020): 6427–43. http://dx.doi.org/10.5194/amt-13-6427-2020.
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Abstract. In this paper we evaluate characteristics of three optical particulate matter sensors/sizers (OPS): high-end spectrometer 11-D (Grimm, Germany), low-cost sensor OPC-N2 (Alphasense, United Kingdom) and in-house developed MAQS (Mobile Air Quality System), which is based on another low-cost sensor – PMS5003 (Plantower, China), under realistic conditions of strong and mild urban pollution. Results were compared against a reference gravimetric system, based on a Gemini (Dadolab, Italy), 2.3 m3 h−1 air sampler, with two channels (simultaneously measuring PM2.5 and PM10 concentrations). The measurements were performed in Sarajevo, the capital of Bosnia-Herzegovina, from December 2019 until May 2020. This interval is divided into period 1 – strong pollution – and period 2 – mild pollution. The city of Sarajevo is one of the most polluted cities in Europe in terms of particulate matter: the average concentration of PM2.5 during the period 1 was 83 µg m−3, with daily average values exceeding 500 µg m−3. During period 2, the average concentration of PM2.5 was 20 µg m−3. These conditions represent a good opportunity to test optical devices against the reference instrument in a wide range of ambient particulate matter (PM) concentrations. The effect of an in-house developed diffusion dryer for 11-D is discussed as well. In order to analyse the mass distribution of particles, a scanning mobility particle sizer (SMPS), which together with the 11-D spectrometer gives the full spectrum from nanoparticles of diameter 10 nm to coarse particles of diameter 35 µm, was used. All tested devices showed excellent correlation with the reference instrument in period 1, with R2 values between 0.90 and 0.99 for daily average PM concentrations. However, in period 2, where the range of concentrations was much narrower, R2 values decreased significantly, to values from 0.28 to 0.92. We have also included results of a 13.5-month long-term comparison of our MAQS sensor with a nearby beta attenuation monitor (BAM) 1020 (Met One Instruments, USA) operated by the United States Environmental Protection Agency (US EPA), which showed similar correlation and no observable change in performance over time.
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Markowicz, Krzysztof M., and Michał T. Chiliński. "Evaluation of Two Low-Cost Optical Particle Counters for the Measurement of Ambient Aerosol Scattering Coefficient and Ångström Exponent." Sensors 20, no. 9 (May 4, 2020): 2617. http://dx.doi.org/10.3390/s20092617.
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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.
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Dewage, Prabuddha M. H., Lakitha O. H. Wijeratne, Xiaohe Yu, Mazhar Iqbal, Gokul Balagopal, John Waczak, Ashen Fernando, Matthew D. Lary, Shisir Ruwali, and David J. Lary. "Providing Fine Temporal and Spatial Resolution Analyses of Airborne Particulate Matter Utilizing Complimentary In Situ IoT Sensor Network and Remote Sensing Approaches." Remote Sensing 16, no. 13 (July 3, 2024): 2454. http://dx.doi.org/10.3390/rs16132454.
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This study aims to provide analyses of the levels of airborne particulate matter (PM) using a two-pronged approach that combines data from in situ Internet of Things (IoT) sensor networks with remotely sensed aerosol optical depth (AOD). Our approach involved setting up a network of custom-designed PM sensors that could be powered by the electrical grid or solar panels. These sensors were strategically placed throughout the densely populated areas of North Texas to collect data on PM levels, weather conditions, and other gases from September 2021 to June 2023. The collected data were then used to create models that predict PM concentrations in different size categories, demonstrating high accuracy with correlation coefficients greater than 0.9. This highlights the importance of collecting hyperlocal data with precise geographic and temporal alignment for PM analysis. Furthermore, we expanded our analysis to a national scale by developing machine learning models that estimate hourly PM 2.5 levels throughout the continental United States. These models used high-resolution data from the Geostationary Operational Environmental Satellites (GOES-16) Aerosol Optical Depth (AOD) dataset, along with meteorological data from the European Center for Medium-Range Weather Forecasting (ECMWF), AOD reanalysis, and air pollutant information from the MERRA-2 database, covering the period from January 2020 to June 2023. Our models were refined using ground truth data from our IoT sensor network, the OpenAQ network, and the National Environmental Protection Agency (EPA) network, enhancing the accuracy of our remote sensing PM estimates. The findings demonstrate that the combination of AOD data with meteorological analyses and additional datasets can effectively model PM 2.5 concentrations, achieving a significant correlation coefficient of 0.849. The reconstructed PM 2.5 surfaces created in this study are invaluable for monitoring pollution events and performing detailed PM 2.5 analyses. These results were further validated through real-world observations from two in situ MINTS sensors located in Joppa (South Dallas) and Austin, confirming the effectiveness of our comprehensive approach to PM analysis. The US Environmental Protection Agency (EPA) recently updated the national standard for PM 2.5 to 9 μg/m 3, a move aimed at significantly reducing air pollution and protecting public health by lowering the allowable concentration of harmful fine particles in the air. Using our analysis approach to reconstruct the fine-time resolution PM 2.5 distribution across the entire United States for our study period, we found that the entire nation encountered PM 2.5 levels that exceeded 9 μg/m 3 for more than 20% of the time of our analysis period, with the eastern United States and California experiencing concentrations exceeding 9 μg/m 3 for over 50% of the time, highlighting the importance of regulatory efforts to maintain annual PM 2.5 concentrations below 9 μg/m 3.