Accurate identification and monitoring of particulate matter is an important means to solve the problem of atmospheric particulate pollution. Therefore, we propose a method of feature extraction and attribute recognition based on wavelet scattering and long and short term memory neural network. In this paper, the light scattering signals of particles with different attributes are collected by the experimental platform. Firstly, the EMD-ICA noise reduction model is employed to complete the noise reduction preprocessing of the particle light scattering signals. Then, the feature of scattering coefficient is extracted by wavelet scattering network, and the scattering feature matrix is constructed and input into the long and short term memory neural network for training. Finally, the probability classification of softmax layer is utilized recognize the attributes of different particles. Meanwhile, the proposed new method of particle property identification has a 98.83% correct classification rate, which shows that it has good performance to achieve particle property identification. Therefore, it provides a feasible basis for preventing air pollution and rapid particle identification.
With the development of industrialization, the problem of aerosol pollution has become increasingly prominent, which has a significant impact on public health. In this paper, the variation of Stokes vector of scattered light of aerosol particles under 45° incident polarized light is analyzed by T-matrix method, and the effects of complex refractive index, shape and particle size on Stokes vector are discussed. The results show that the variation trend and numerical difference of normalized S1, S2 and S3 can realize the morphological recognition of spherical and ellipsoidal particles. These parameters are sensitive to real and imaginary parts of different complex refractive indices in different detection ranges. This shows that the scattering Stokes vector obtained by 45° linearly polarized light shows great potential in identifying the morphology, complex refractive index and particle size of aerosol particles, and provides a new method and idea for the accurate identification of aerosol particle properties.
Atmospheric particulate matter has complex components and a wide range of sources. It not only frequently occurs in bad weather such as haze, but also aggravates environmental pollution as a carrier of pollutants, posing a potential threat to human health. In order to identify its composition characteristics and sort particles with different particle sizes, this study designed a particle sorting structure based on microfluidic technology, in order to provide scientific guidance for solving the problem of atmospheric particulate matter pollution. Based on the principle of microfluidic particle optical sorting, a particle sorting structure of microfluidic technology was designed to achieve continuous sorting of particles with different particle sizes. The results show that the use of optical radiation force and fluid resistance can effectively achieve the separation of particles with different particle sizes, so as to effectively sort the atmospheric particles according to the particle size.
Feature extraction and attribute recognition of particulate light scattering signals are important in the fields of environmental monitoring, atmospheric science and industrial process control. However, due to the complexity of particles, it remains a challenge to accurately extract features from light scattering signals and perform attribute recognition. In this study, a deep learning-based feature extraction and attribute classification model for particulate light scattering signals is proposed. Firstly, the acquisition of particulate light scattering signal is accomplished by the multi-angle detection of light scattering signal experimental flat, secondly, a total of 20 statistical features in time domain, frequency domain and information entropy features of the signal are extracted to describe the local details of the signal at different frequencies and the feature weights of the signal are obtained by the ReliefF algorithm, so as to find the optimal feature vectors of the signal. Finally, the GRNN and PNN neural network algorithms are used to construct the particle attribute classification model, and the acquired optimal feature vectors are input into the model for attribute classification and recognition. The results show that the recognition accuracy of GRNN reaches 86.7%, while that of PNN reaches 91.67%. It is verified that the GRNN and PNN methods are able to effectively distinguish the above six particles with different attributes.
A T-type microfluidic chip for particulate matter sorting and collection is theoretically established based on optical methods, and an analytical solution for the offset distance of particles is also derived. On this basis, a dimensionless parameter S is proposed in combination with the characteristic parameters of the microfluidic chip. The trajectories of the particles under different conditions are calculated by means of numerical simulations, and the numerical simulations are compared with the theoretical calculations to verify the validity of the calculations and models. At the same time, an experimental platform was constructed, and the final experimental results show that the errors of the particle offset distances are 4.2 μm and 1.4 μm compared with the results of theoretical calculations and numerical simulations, respectively. Meanwhile, the particle sorting efficiency was counted according to the experimental results, and the experimental results and the numerical simulations are sufficient to attain satisfactory agreements.
The light scattering method has the advantages of simple structure and good real-time performance in measuring the mass concentration of particulate matter. It is usually used to calibrate the laboratory standard particles to ensure the accuracy of measurement. However, the actual measured particle size of pollutant particles is complex and changeable, it is necessary to explore the scattering light intensity distribution law in multi-detection angles under different particle sizes. The numerical simulation results obtain scattering light intensity distribution of silica particles under different polarization state light sources, and get the appropriate range of detection angle. This paper mainly designs a set of device to measure the scattering light intensity and mass concentration of particulate matter. Meanwhile, the standard instrument TSI was used to measure the real-time change of the mass concentration of particulate matter. The results show that the scattering light intensity signal measured by photodetector is highly correlated with the mass concentration measured by the TSI standard instrument, and the mass concentration of the particles measured by the system is in good agreement with the standard instrument TSI. The scattering light intensity can effectively invert the real-time mass concentration of particulate matter. The experimental device is suitable for real-time measurement of particulate matter mass concentration.
In recent years, studies on fine particulate matter have shown that high concentrations of particulate matter seriously affect the quality of weather, creating a series of severe weather such as haze and posing a great risk to human health. The results of epidemiological studies suggest that particulate matter is associated with a higher risk of cardiopulmonary mortality and morbidity. Therefore, there is an urgent need to conduct research on particulate matter to solve the human health problems caused by particulate matter pollution. The identification of the compositional characteristics of particulate matter presupposes the separation of particulate matter with different aerodynamic diameters and provides scientific guidance for solving the problem of atmospheric particulate matter pollution. To address this problem, a virtual impactor with a cutting particle size of 1.2 μm is designed in this paper. The influence of key parameters on the performance of the virtual impactor is also discussed. The results show that the proposed virtual impactor has a cutting particle size of 1.2um and a good steepness of the collection efficiency curve. It shows that it can effectively separate atmospheric particulate matter according to particle size and provides a design basis for realizing a low-cost atmospheric particulate matter mass concentration detection instrument. Meanwhile, we design a microfluidic chip for particulate matter detection based on this virtual impactor. The hardware circuit of this microfluidic chip is also designed.
Inhalable particulate matter has been widely concerned due to its serious harm to human health in China. Real time, rapid and high-resolution monitoring of particle concentration change is the first step to prevent and control inhalable particle pollution. In this paper we present a method for detecting fine particle mass concentration based on forward and lateral light scattering measurement. Based on Mie light scattering theory, we design and establish the experimental platform of multi-angle light scattering measurement. Moreover, a portable multi-angle light scattering detection particle mass concentration prototype is developed by using computer modeling, 3D printing and weak photoelectric signal detection technology. Through theoretical numerical simulation and experimental analysis of optical platform, 20° forward and 45° lateral are selected as the optimal detection angles with the advantages of simple structure and high efficiency. Finally, we obtain the pulse reference voltage of different particle size segments is to realize the particle size segment detection. A specific case of our nonlinear regression algorithm is used to calibrate parameters of the detection system. The feasibility of the proposed detection method is verified by the comparative detection experiments in the laboratory and the outdoor atmospheric environment.
The level of fine particulate air pollution exposure is positively correlated with the death rate of individuals infected with COVID-19. Monitoring is the first step to prevent fine particulate pollution. The instrument based on light scattering method to detect particle concentration has unparalleled advantages over other instruments due to its rapidity, real-time and low cost. Traditional light scattering instruments are limited by the light absorption and particle properties of particles, and their ability to monitor some particles with strong light absorption is greatly reduced. Moreover, when the measured environment is greatly different from the calibration environment, the measurement results often have large errors. In this research, an instrument is designed to detect the forward scattering of light from small angles of particles. It can monitor the number concentration of particles in the environment in real time in four particle size ranges (PM1, PM2.5, PM4 and PM10) and convert it into the mass concentration of particles. By using the simulated atmospheric smoke box and the standard instrument to conduct a field comparison experiment, the reliability and stability of the measurement results are verified.
Small angle light scattering measurement is more relevant for determining the size of solid aerosols, but small scattering angle measurement results will be interfered by larger stray light. The key technology is to suppress the background noise caused by the Fraunhofer diffraction of the laser light source and the Rayleigh scattering of atmospheric molecules, so as to improve the resolution of weak scattered light signal of strong light absorption small particle aerosol. An adaptive filtering method of forward small angle aerosol scattering signal is proposed based on recursive least-square (RLS) algorithm. By analyzing the characteristics of small-angle aerosol detection signals, the forgetting factor in traditional RLS is optimized, so that it can not only distinguish aerosol scattered light signals from stray light signals, but also dynamically adjust according to the amplitude change under different particle size and absorbance. In order to verify the filtering effect, small angle scattering light pulse extraction experiments of aerosols with different absorbance and different particle sizes were conducted in a simulated smoke box. Experiments show that the proposed variable forgetting factor RLS algorithm can effectively suppress stray light signals caused by laser light sources and atmospheric molecules. When the aerosol detection signal appears, the algorithm has fast convergence speed and tracking speed, which highlights the aerosol pulse signal well. Compared with the traditional method, the resolution of the processed aerosol scattering pulse signal increases dramatically and has a great advantage in the extraction of weak scattering pulse signal.
Light emitting diodes (LEDs) have recently gained much interest as projection light sources. In this work, a compound parabolic concentrator (CPC) coupled to a biologically inspired compound-eye array is designed and fabricated as a light collection engine of a pico-projector. The results indicate that more than 90% light emitted by a monolithic LED array can be collected by the CPC coupled to a compound-eye array and transmitted within the designed angle. This method is advantageous in many respects compared with those available, such as compact volume, high collection efficiency, rectangular radiation pattern and controllable output divergence angle. The result validates that the system reaches a collection efficiency of 87% of micro-LED emitted light. Moreover, the beam collimation quality has been analyzed obtaining a residual divergence of less than 2º. Thus, the results achieved by the proposed optical system improve those obtained with several commercially available devices.
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