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Photonics-Based Sensors for Environment and Pollution Monitoring

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Optical Sensors".

Deadline for manuscript submissions: closed (30 June 2020) | Viewed by 17545

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Special Issue Editor

Prof. Dr. Kamal Alameh

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Guest Editor

Electron Science Research Institute, Edith Cowan University, Joondalup, WA 6027, Australia
Interests: microphotonics; opto-VLSI; nanophotonics; plasmonics; photonics-based sensors; nano-bio; renewable energy; security and defense
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent advances in photonics-based sensors and machine learning techniques have enabled the development of smart photonics-based sensors for environment and pollution monitoring. While photonics technology and machine-learning techniques have matured, fusing them to realize high-accuracy environmental monitoring sensors has not been fully explored.

The goal of this Special Issue is to invite high-quality, state-of-the-art research papers that deal with challenging issues in photonics-based sensors for environment and pollution monitoring. We solicit original papers of unpublished and completed research that are not currently under review by any other conference/magazine/journal. Topics of interest include but are not limited to the following:

Photonics-based sensor structures, modules, and systems employing spectral and spatial sensing mechanisms;
Photonics-based sensing techniques relating to all aspects of environment and pollution monitoring;
Small-, medium-, and large- scale photonics-based sensor networks;
Photonic techniques for (i) air and water quality monitoring, (ii) trace gas detection, (iii) aerosol measurements, (iv) fossil fuels, (v) nature gas, and (vi) mining and nuclear emissions;
Machine-learning-based spectral and spatial photonic sensors operating in the UV, visible, NIR, and FIR ranges;
Sensing approaches that combine photonic technologies and machine learning techniques, such as image recognition;
Remote-, satellite-, and aircraft-based sensing techniques for environmental monitoring, including Fourier-transform infrared spectroscopy (FTIR), light detection and ranging (LIDAR), and dedicated outdoor air systems (DOAS);
Novel disruptive photonic sensing technologies, such as photonic sensors on a chip and the Internet of Things;
Optics and photonics for improved air and water quality, including transformative optical low-cost sensors;
Photonics-based weed/crop sensors for increased efficiency and sustainability in agriculture;
Photonics-based sensors for extreme environments, such as combustion, flames, plasmas, and explosions.

Prof. Dr. Kamal Alameh
Guest Editor

Manuscript Submission Information

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Keywords

Photonics-based sensors
Environmental monitoring
Optical data processing, understanding, and recognition
Optical data processing based on machine learning
Fusion of machine learning techniques and photonics technology

Published Papers (5 papers)

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Research

18 pages, 8471 KiB

Open AccessArticle

A Comparison of Different Counting Methods for a Holographic Particle Counter: Designs, Validations and Results

by Georg Brunnhofer, Isabella Hinterleitner, Alexander Bergmann and Martin Kraft

Sensors 2020, 20(10), 3006; https://doi.org/10.3390/s20103006 - 25 May 2020

Cited by 1 | Viewed by 3044

Abstract

Digital Inline Holography (DIH) is used in many fields of Three-Dimensional (3D) imaging to locate micro or nano-particles in a volume and determine their size, shape or trajectories. A variety of different wavefront reconstruction approaches have been developed for 3D profiling and tracking to study particles’ morphology or visualize flow fields. The novel application of Holographic Particle Counters (HPCs) requires observing particle densities in a given sampling volume which does not strictly necessitate the reconstruction of particles. Such typically spherical objects yield circular intereference patterns—also referred to as fringe patterns—at the hologram plane which can be detected by simpler Two-Dimensional (2D) image processing means. The determination of particle number concentrations (number of particles/unit volume [#/cm

^{3}

]) may therefore be based on the counting of fringe patterns at the hologram plane. In this work, we explain the nature of fringe patterns and extract the most relevant features provided at the hologram plane. The features aid the identification and selection of suitable pattern recognition techniques and its parameterization. We then present three different techniques which are customized for the detection and counting of fringe patterns and compare them in terms of detection performance and computational speed. Full article

(This article belongs to the Special Issue Photonics-Based Sensors for Environment and Pollution Monitoring)

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19 pages, 3275 KiB

Open AccessEditor’s ChoiceArticle

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

by Krzysztof M. Markowicz and Michał T. Chiliński

Sensors 2020, 20(9), 2617; https://doi.org/10.3390/s20092617 - 04 May 2020

Cited by 19 | Viewed by 4301

Abstract

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

(This article belongs to the Special Issue Photonics-Based Sensors for Environment and Pollution Monitoring)

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19 pages, 22284 KiB

Open AccessArticle

Design and Validation of a Holographic Particle Counter

by Georg Brunnhofer, Alexander Bergmann, Andreas Klug and Martin Kraft

Sensors 2019, 19(22), 4899; https://doi.org/10.3390/s19224899 - 09 Nov 2019

Cited by 9 | Viewed by 4397

Abstract

An in-line holographic particle counter concept is presented and validated where multiple micrometer sized particles are detected in a three dimensional sampling volume, all at once. The proposed Particle Imaging Unit is capable of detecting holograms of particles which sizes are in the [...] Read more.

μ

m- range. The detection and counting principle is based on common image processing techniques using a customized Hough Transform with a result directly relating to the particle number concentration in the recorded sampling volume. The proposed counting unit is mounted ontop of a Condensation Nucleus Magnifier for comparison with a commercial TSI-3775 Condensation Particle Counter (CPC). The concept does not only allow for a precise in-situ determination of low particle number concentrations but also enables easy upscaling to higher particle densities (e.g.,

> 30.000 \frac{#}{c c m}

) through its linear expandability and option of cascading. The impact of coincidence at higher particle densities is shown and two coincidence correction approaches are presented where, at last, its analogy to the coincidence correction methods used in state-of-the-art CPCs is identified. Full article

(This article belongs to the Special Issue Photonics-Based Sensors for Environment and Pollution Monitoring)

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15 pages, 3895 KiB

Open AccessArticle

Selection of the Optimal Spectral Resolution for the Cadmium-Lead Cross Contamination Diagnosing Based on the Hyperspectral Reflectance of Rice Canopy

by Shuangyin Zhang, Ying Zhu, Mi Wang and Teng Fei

Sensors 2019, 19(18), 3889; https://doi.org/10.3390/s19183889 - 09 Sep 2019

Cited by 6 | Viewed by 2305

Abstract

This paper proposed an optimal spectral resolution for diagnosing cadmium-lead (Cd-Pb) cross contamination with different pollution levels based on the hyperspectral reflectance of rice canopy. Feature bands were sequentially selected by two-way analysis of variance (ANOVA2) and random forests from the high-dimensional hyperspectral data after preprocessing. Then Support Vector Machine (SVM) was applied to diagnose the pollution levels using different feature bands combination with different spectral resolutions and cross validation was conducted to evaluate the distinguishing accuracies. Finally, the optimal spectral resolution could be determined by comparing the diagnosing accuracies of the optimal feature bands combination in each spectral resolution. In the experiments, the hyperspectral reflectance data of rice canopy with ten different spectral resolutions was captured, covering 16 pretreatments of Cd and Pb pollution. The experimental results showed the optimal spectral resolution was 9 nm with the highest average accuracy of 0.71 and relatively standard deviation of 0.07 for diagnosing the categories and levels of Cd-Pb cross contamination. The useful exploration provided an evidence for optimal spectral resolution selection to reduce the cost of heavy metal pollution diagnose. Full article

(This article belongs to the Special Issue Photonics-Based Sensors for Environment and Pollution Monitoring)

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15 pages, 2997 KiB

Open AccessArticle

A Fast Image Deformity Correction Algorithm for Underwater Turbulent Image Distortion

by Min Zhang, Yuzhang Chen, Yongcai Pan and Zhangfan Zeng

Sensors 2019, 19(18), 3818; https://doi.org/10.3390/s19183818 - 04 Sep 2019

Cited by 3 | Viewed by 2139

Abstract

An algorithm correcting distortion based on estimating the pixel shift is proposed for the degradation caused by underwater turbulence. The distorted image is restored and reconstructed by reference frame selection and two–dimensional pixel registration. A support vector machine-based kernel correlation filtering algorithm is proposed and applied to improve the speed and efficiency of the correction algorithm. In order to validate the algorithm, laboratory experiments on a controlled simulation system of turbulent water and field experiments in rivers and oceans are carried out, and the experimental results are compared with traditional, theoretical model-based and particle image velocimetry-based restoration and reconstruction algorithms. Using subjective visual evaluation, image distortion has been effectively suppressed; based on an objective performance statistical analysis, the measured values are better than the traditional and formerly studied restoration and reconstruction algorithms. The method proposed in this paper is also much faster than the other algorithms. It can be concluded that the proposed algorithm can effectively improve the de-distortion effect of the underwater turbulence degraded image, and provide potential techniques for the accurate operation of underwater target detection in real time. Full article

(This article belongs to the Special Issue Photonics-Based Sensors for Environment and Pollution Monitoring)

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