Number of independent measurements required to obtain reliable mean scattering properties of irregular particles having a small size parameter, using microwave analogy measurements

Renard, Jean‐Baptiste; Geffrin, Jean-Michel; Valencia, Vanesa Tobon; Tortel, Hervé; Ménard, F.; Rannou, P.; Milli, J.; Berthet, Gwenaël

doi:10.1016/j.jqsrt.2021.107718

Cited by 13 publications

(6 citation statements)

References 29 publications

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“…Several factors determine the scattering of the incoming measured light, including composition, albedo, porosity, shape and size distribution, being relevant the size, shape and porosity of the airborne particles (Szymanski and Liu, 1986;Xu, 2002;Grimm and Eatough, 2009;Moosmüller and Arnott, 2009a;Moosmüller et al, 2009b;Morpurgo et al, 2012;Gao et al, 2013;Renard et al, 2016;Renard et al, 2016;Stavroulas et al, 2020;Renard et al, 2021). As reported, in some cases the effects of the shape and porosity of the airborne particles can be even greater than their size on the light scattering, and this deserves to be considered in further designs of low cost monitors.…”

Section: Introductionmentioning

confidence: 99%

“…Some LCMs have nonlinear response to particle size, with most of them having low performance for large particles (Feenstra et al, 2019;Kuula et al, 2020). The optical properties of particles, size and shape play an important role because they change the intensity as well as the angle of the scattered light (Wang et al, 2015;Sousan et al, 2016;Kelly et al, 2017;Liu et al, 2017;Renard et al, 2021). Direct observation of the performance of LCMs with the composition of particles has also been observed (Sayahi et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…As reported, in some cases the effects of the shape and porosity of the airborne particles can be even greater than their size on the light scattering, and this deserves to be considered in further designs of low cost monitors. However, considering the cost limitations of low cost sensors mainly associated to the microcontroller and its programming, it becomes difficult to process an ensemble of measurements for controlling these factors, as recommended by Renard et al, 2021. Most low cost sensors use laser light with wavelength between 600 and 980 nm (Hagan and Kroll, 2020). When the particle size is much smaller (d < 0.1 μm) than the wavelength (Rayleigh scattering regime, Xu, 2002;Moosmüller et al, 2009b) the pattern of the scattered light is symmetrical in the forward and backward directions and more or less independent of the shape of the particle.…”

Section: Introductionmentioning

confidence: 99%

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Influence of Particle Composition and Size on the Accuracy of Low Cost PM Sensors: Findings From Field Campaigns

Gramsch¹,

Oyola²,

Reyes³

et al. 2021

Front. Environ. Sci.

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In the last decade, many low-cost monitoring sensors and sensor-networks have been used as an alternative air quality assessment method. It is also well known that these low cost monitors have calibration, accuracy and long term variation problems which require various calibration techniques. In this work PM2.5 and PM10 low cost sensors (Plantower and Nova Fitness) have been tested in five cities under different environmental conditions and compared with collocated standard instruments. Simultaneously, particle composition (organic and black carbon, sulfate, nitrate, chloride, ammonium, and chemical elements) has been measured in the same places to study its influence on the accuracy. The results show a very large variability in the correlation between the low cost sensors and collocated standard instruments depending on the composition and size of particles present in the site. The PM10 correlation coefficient (R2) between the low cost sensor and a collocated regulatory instrument varied from to 0.95 in Temuco to 0.04 in Los Caleos. PM2.5 correlation varied from 0.97 to 0.68 in the same places. It was found that sites that had higher proportion of large particles had lower correlation between the low cost sensor and the regulatory instrument. Sites that had higher relative concentration of organic and black carbon had better correlation because these species are mostly below the 1 μm size range. Sites that had higher sulfate, nitrate or SiO2 concentrations in PM2.5 or PM10 had low correlation most likely because these particles have a scattering coefficients that depends on its size or composition, thus they can be classified incorrectly.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Influence of Particle Composition and Size on the Accuracy of Low Cost PM Sensors: Findings From Field Campaigns

Gramsch¹,

Oyola²,

Reyes³

et al. 2021

Front. Environ. Sci.

View full text Add to dashboard Cite

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“…We implemented this oversampling [ 42 ] procedure to handle the problem of imbalanced data and to ensure there were enough counts in each size bin. Indeed, at least 20 particles per size bin are necessary to achieve the mean scattering properties of a given species [ 43 ]. Following that we concatenated the obtained datasets to form a unique dataset of 360 rows (30 cumulated size distributions × 12 pollen species) and 19 columns (19 size bins), on which we applied a row-normalization.…”

Section: Methodsmentioning

confidence: 99%

A Laboratory Evaluation of the New Automated Pollen Sensor Beenose: Pollen Discrimination Using Machine Learning Techniques

Azari

Renard

Lauthier³

et al. 2023

Sensors

Self Cite

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The monitoring of airborne pollen has received much attention over the last decade, as the prevalence of pollen-induced allergies is constantly increasing. Today, the most common technique to identify airborne pollen species and to monitor their concentrations is based on manual analysis. Here, we present a new, low-cost, real-time optical pollen sensor, called Beenose, that automatically counts and identifies pollen grains by performing measurements at multiple scattering angles. We describe the data pre-processing steps and discuss the various statistical and machine learning methods that have been implemented to distinguish different pollen species. The analysis is based on a set of 12 pollen species, several of which were selected for their allergic potency. Our results show that Beenose can provide a consistent clustering of the pollen species based on their size properties, and that pollen particles can be separated from non-pollen ones. More importantly, 9 out of 12 pollen species were correctly identified with a prediction score exceeding 78%. Classification errors occur for species with similar optical behaviour, suggesting that other parameters should be considered to provide even more robust pollen identification.

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“…At least several tens of images are necessary in order to retrieve the mean scattering properties of the particles at a given scattering angle. This is mandatory to minimize the effect of the scattering intensity variability produced by individual particles [26] when they cross the field view of the detectors.…”

Section: Laboratory Experimental Set-up For the Scattering Curves Of ...mentioning

confidence: 99%

Towards an Automatic Pollen Detection System in Ambient Air Using Scattering Functions in the Visible Domain

Renard

Azari

Richard³

et al. 2022

Sensors

Self Cite

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Pollen grains strongly affect human health by inducing allergies. Although the monitoring of airborne pollens particles is of major importance, the current measurement methods are manually conducted and are expensive, limiting the number of monitoring stations. Thus, there is a need for relatively low-cost instruments that can work automatically. The possible detection of pollen in urban ambient air (Paris, France) has been reported using the LOAC optical aerosol counter. These measurements indicate that the pollen grains and their nature could be determined using their scattering properties. For this purpose, the scattering functions (intensity and linear polarization) of 21 different airborne pollens were established in the laboratory using a PROGRA2 instrument. The linear polarization curves were close together, with a maximum polarization lower than 10% in the red domain and 5% in the green domain. The variability from one sample to another was partly due to the different sizes of the grains. An instrument with an absolute accuracy of about ±1% for polarization measurements should then be needed, coupled with a counting instrument to take into account the effects of size. On the other hand, the scattering curves for intensity presented with different shapes and strong differences up to a factor of 20 at some scattering angles, due to the size, shape, surface texture, and composition of the grains. Thus, we propose a proof of concept for new automated sensors that can be used in dense networks to count and identify pollen grains by analyzing the light they scatter at some specific angles.

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Number of independent measurements required to obtain reliable mean scattering properties of irregular particles having a small size parameter, using microwave analogy measurements

Cited by 13 publications

References 29 publications

Influence of Particle Composition and Size on the Accuracy of Low Cost PM Sensors: Findings From Field Campaigns

Influence of Particle Composition and Size on the Accuracy of Low Cost PM Sensors: Findings From Field Campaigns

A Laboratory Evaluation of the New Automated Pollen Sensor Beenose: Pollen Discrimination Using Machine Learning Techniques

Towards an Automatic Pollen Detection System in Ambient Air Using Scattering Functions in the Visible Domain

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