The in situ detection of smoking in public area by laser-induced breakdown spectroscopy

Real-time in situ detection of petroleum hydrocarbon pollution in soils via a novel optical methodology

Petroleum hydrocarbon (PHC) contamination in soils is considered one of the most serious problems currently, of which the detection and identification is a fairly significant but challenging work. Conventional methods to do such work usually need complex sample pretreatment, consume much time and fail to do the in-situ detection. This paper set out to create a novel systematic methodology to realize the goals accurately and efficiently. Based on laser-induced breakdown spectroscopy (LIBS) and self-improved machine learning methods, the innovative methodology only uses extremely simple devices to do the real-time in situ detection and identification work and even realize the quantitative analysis of pollution level accurately. In the study, clean soils mixed with petroleum were served as polluted samples, clean soils to be the blank group for comparison. Based on the elemental information from the spectra obtained by LIBS, machine learning methods were improved and helped optimized the algorithm to identify the PHC polluted soil samples for the first time. Furthermore, a novel model was designed to perform the quantitative analysis of the concentration of PHC pollution in soils, which can be applied to detect the degree of PHC contamination in soils accurately. Finally, the harmful volatile component of the PHC polluted soils was also successfully and identified despite its extremely minimal content in the air. The newly-designed methodology is novel and efficient, which has extensive application prospect in the real-time in situ detection of petroleum hydrocarbon pollution.

Individual Micron-Sized Aerosol Qualitative Analysis-Combined Raman Spectroscopy and Laser-Induced Breakdown Spectroscopy by Optical Trapping in Air

The attribution of single particle sources of atmospheric aerosols is an essential problem in the study of air pollution. However, it is still difficult to qualitatively analyze the source of a single aerosol particle using noncontact in situ techniques. Hence, we proposed using optical trapping to combine gated Raman spectroscopy with laser-induced breakdown spectroscopy (LIBS) in a single levitated micron aerosol. The findings of the spectroscopic imaging indicated that the particle plasma formed by a single particle ablation with a pulsed laser within 7 ns deviates from the trapped particle location. The LIBS acquisition field of view was expanded using the 19-bundle fiber, which also reduces the fluctuation of a single particle signal. In addition, gated Raman was utilized to suppress the fluorescence and increase the Raman signal-to-noise ratio. Based on this, Raman can measure hard-to-ionize substances with LIBS, such as sulfates. The LIBS radical can overcome the restriction that Raman cannot detect ionic chemicals like fluoride and chloride in halogens. To test the capability of directly identifying distinctive feature compounds utilizing spectra, we detected anions using Raman spectroscopy and cations using LIBS. Four typical mineral aerosols are subjected to precise qualitative evaluations (marble, gypsum, baking soda, and activated carbon adsorbed potassium bicarbonate). To further validate the application potential for substances with indistinctive feature discrimination, we employed machine learning algorithms to conduct a qualitative analysis of the coal aerosol from ten different origin regions. Three data fusion methodologies (early fusion, intermediate fusion, and late fusion) for Raman and LIBS are implemented, respectively. The accuracy of the late fusion model prediction using StackingClassifier is higher than that of the LIBS data (66.7%) and Raman data (86.1%) models, with an average accuracy of 90.6%. This research has the potential to provide online single aerosol analysis as well as technical assistance for aerosol monitoring and early warning.

Online in situ detection of local air conditions in hazardous operation scenarios

Although the risk of some hazardous operations has been greatly reduced, the changes in air conditions caused by them cannot be ignored. A lot of smoke is produced in some hazardous workplaces, which is very easy to cause air pollution. Exposed to this condition for a long time, human health is also greatly endangered. Therefore, the online detection of air conditions in hazardous workplaces is of great significance. In this work, electric welding operation is taken as an example to study the air conditions in hazardous workplaces. The real-time detection of smoke generated from electric welding operation is carried out by Laser induced breakdown spectroscopy (LIBS). The spectral line of lead is found in the smoke spectrum of lead tin wire. Some other metal elements are also detected in the smoke of the tin wire, such as Sn, Cu, Ca and K. Consequently, it is recommended to use lead-free tin wire. In addition, two welding scenarios are simulated. The results show that the generated smoke contains different element information with different welding objects. Then, the quantitative analysis of lead in smoke is carried out. It shows that the concentration of Pb can be calculated by the spectral line intensity in the spectrum. Finally, combined with the error back propagation artificial neural network (BP-ANN), different kinds of smoke are classified, and the recognition accuracy is more than 99%, which proves that the combination of LIBS and BP-ANN is developed into an important method in the monitoring of different air pollutants.