Laser induced fluorescence (LIF) technique has been demonstrated as a powerful technology for analyzing the contamination of petroleum due to its excellent attributes of rapid analysis speed and slight sample preparation. This study focuses on the monitoring application of LIF in petroleum hydrocarbon-contaminated soils by establishing the three-dimensional diffusion models. In this paper, to improve the analysis accuracy, the effects of soil matrix difference for fluorescence intensities were considered. In order to validate the practicability of LIF, the longitudinal penetration laws and the lateral diffusion laws of diesel oil in different humidity soils were analyzed. These laws indicate that the longitudinal penetration depth decreases and the lateral diffusion range increases with the increase of soil moisture. Then, the three-dimensional diffusion models were established, the relative standard deviation (RSD) of the predictions for diesel oil in different soil moisture are 5.09%, 9.62%, 7.92%, and the contaminated volumes of soils by diesel oil are 233.90 cm³, 332.70 cm³, and 660.05 cm³, respectively. These results express that the soil moisture extends the extent of diesel-contaminated soils. The present work shows the feasibility of LIF technique for the field monitoring of petroleum.

The particle size of submicron particles significantly affects their properties; thus, the accurate measurement of submicron particle size is essential to ensure its excellent properties. Polarized light scattering is an important tool for measuring the particle size of the ensemble of particles in suspension. However, in the existing measurement systems, the polarized scattered light is detected using a CCD detector or an array of single-point detectors. The CCD detector misses a large part of the polarized scattered light due to its narrow detection range of scattering angles, and the array of single-point detectors has the problem of low angular resolution due to the limited number of detectors. According to the above problems, this paper designs a submicron particle size measurement method based on the polarization difference in polarized scattered light with high angular resolution. The vertically and horizontally polarized scattered light was acquired with high angular resolution (angular separation = 2°) over a scattering angle range of 50°–110° using a photomultiplier coupled with a turntable. The scattering angle of the acquired vertically and horizontally polarized scattered light were corrected to eliminate the scattering angle deviations caused by obliquely incident light, and then the polarization difference in the vertically and horizontally polarized scattered light was computed, from which the submicron particle size distribution was inverted subsequently. Experiments were performed using polystyrene microsphere standard particles with particle sizes of 350 nm, 200 nm, and 100 nm. The experimental results show that (1) the Pearson correlation coefficient of the linearly fitted curve of the corrected polarization difference to the theoretical polarization difference is larger than 0.997, and the slope and intercept of the linearly fitted curve are, respectively, close to 1 and 0, indicating that the corrected polarization difference is highly consistent with the theoretical polarization difference; (2) the mean relative error and coefficient of variation of the particle size distribution parameter D50 obtained from the polarization difference with high angular resolution (angular separation = 2°) are better than those of the parameter D50 obtained from the polarization difference with low angular resolution (angular separation = 12°), indicating better accuracy and repeatability of the particle size distribution inverted from the polarization difference with high angular resolution; and (3) for the particle size distribution parameters D10, D50, and D90 obtained from the scattering angle-corrected polarization difference with high angular resolution, the deviation of the measured values from the average value are all smaller than the thresholds given in the international standard, indicating a good repeatability of the proposed method.

The quantitative analyses of pH value in soil have been performed using laser-induced breakdown spectroscopy (LIBS) technology. The aim of this work was to obtain a reliable and accurate method for rapid detection of pH value in soil. Seventy-four samples were used as a calibration set, and 24 samples were used as a prediction set. To eliminate the matrix effect, the multivariate models of partial least-squares regression (PLSR) and least-squares support vector regression (LS-SVR) were used to construct the models. The intensities of nine emission lines of C, Ca, Na, O, H, Mg, Al, and Fe elements were used to fit the models. For the PLSR model, the correlation coefficient was 0.897 and 0.906 for the calibration and prediction set, respectively. Furthermore, the analysis accuracy was improved effectively by the LS-SVR method, and the correlation coefficients for calibration and prediction set were improved to 0.991 and 0.987. The prediction mean absolute error was pH 0.1 units, and the root mean square error of the prediction was only 0.079. The results indicated that the LIBS technique coupled with LS-SVR could be a reliable and accurate method for determining pH value in soil.

Plastic recycling is the most effective way to solve plastic pollution. Color recognition of plastic is a key step in the plastic recycling process, which is very important for improving the utilization rate of waste plastic. At present, the manual recognition method is inefficient and cannot meet the requirements of large-scale production, while the existing color recognition based on machine vision has problems such as low recognition accuracy of similar colors, complicated algorithm and high system cost. According to the above problems, a color recognition method based on multi-wavelength transmission spectrum was proposed in this paper for the color recognition of colorless, light blue, light green and apple green transparent plastic. For each transparent plastic, the transmission spectra at 381 nm, 439 nm, 620 nm and white transmission light source were obtained, and the ratios of R, G and B wavebands of the transmission spectra were used to set up a color feature, which was then used for color recognition. A color recognition model was established based on the color features and naive Bayes model. The 10-fold cross-validation results showed that the recognition accuracy of the four colors of transparent plastic reached 100%.