In this paper, we proposed a novel method to determine the oil film thickness on water with a wide range, which is based on the intensity ratio between the laser-induced oil film fluorescence and Raman scattering of the water without oil films. To verify the feasibility of this method, we presented a portable detection system which employs a laser diode at 405 nm and an intensified charged-coupled device (ICCD) detector. Using this system, the diesel oil films with variable thicknesses on water were measured at different distances. The measured thickness was in the range from 0.07 mm to 36.62 mm with an average relative error of 5.98% and 4.07% by parameter calibration method (PCM) and spectral fitting method (SFM), respectively. The experimental results showed that the effective thickness range measured using the proposed ratio method is 40 times greater than that measured by the traditional Raman suppression method (less than 0.82 mm). In addition, different thicknesses of crude oil films were also measured, and the effective thickness range was from 0.002 mm to 0.493 mm, which was 70 times greater than the traditional Raman method (less than 0.007 mm). In the range of 0.007 mm to 0.493 mm, the average relative error is less than 10% using the proposed ratio method. Furthermore, this method can greatly eliminate the influences from the detection distances, instrument factors and external environments compared with the fluorometric method.

Laser-induced fluorescence lidar (LIF-LiDAR) is an effective technology for the stand-off detection, identification, and quantification of oil spills on the water surface. In this work, by using a self-developed LIF-LiDAR system, the laser-induced spectra of different oils with different thicknesses on the water surface were remotely measured at a distance of 100 m. The traditional algorithms based on the similarity degree of matching spectra are adopted to identify the oil types, which results in the 65% (min = 33.68%) and 85% (max = 98.66%) average matching accuracy when the pure oil spectra (infinite thickness) and the average oil spectra (different thickness) are introduced as the database, respectively. Furthermore, to avoid the influence of the pretreatment process in the traditional matching method, the machine learning model is applied to classify the oil types and an identifying accuracy of nearly 100% is successfully achieved. Our results presented in this work not only demonstrate the good performance of the classification models of Fine Tree, Linear Discriminant, Support Vector Machine, k-Nearest Neighbor, and Neural Network in remote oil type identification, but also the capability of the combination of LIF-LiDAR and machine learning algorithms on the high accuracy identification in the types of oil films on water.

Chemical Oxygen Demand (COD) serves as a crucial metric for assessing the extent of water pollution attributable to organic substances. This study introduces an innovative approach for the detection of low-concentration COD in aqueous environments through the application of Laser-Induced Fluorescence (LIF) image processing. The technique employs an image sensor to capture fluorescence image data generated by organic compounds in water when excited by ultraviolet laser radiation. Subsequently, the COD value, indicative of the concentration of organic matter in the water, is derived via image processing techniques. Utilizing this methodology, an LIF image processing COD detection system has been developed. The system is primarily composed of a CMOS image sensor, an STM32 microprocessor, a laser emission module, and a display module. In this study, the system was employed to detect mixed solutions of sodium humate and glucose at varying concentrations, resulting in the acquisition of corresponding fluorescence images. By isolating color channels and processing the image data features, variations in RGB color characteristics were analyzed. The Partial Least Squares Regression (PLSR) analysis method was utilized to develop a predictive model for COD concentration values based on the average RGB color feature values from the characteristic regions of the fluorescence images. Within the COD concentration range of 0–12 mg/L, the system demonstrated a detection relative error of less than 10%. In summary, the system designed in this research, utilizing the LIF image processing method, exhibits high sensitivity, robust stability, miniaturization, and non-contact detection capabilities for low-concentration COD measurement. It is well-suited for rapid, real-time online water quality monitoring.

Laser induced breakdown spectroscopy (LIBS) is a kind of laser ablation spectrum analysis technology, which has the advantages of small sample damage, fast detection speed and simultaneous detection of multiple elements. In this paper, a LIBS system based on passive Q-switched laser is designed, which is used to carry out marine heavy metal detection research. Aiming at the two or more laser pulses that the laser may generate in a single pumping cycle, a laser pulse control module is designed in this paper to analyze and judge the number of laser pulses, and obtain good experimental results. Using this LIBS system to output single-pulse and double-pulse lasers to detect samples of seawater and shells in different regions, we can detect the presence of characteristic spectral lines of elements such as Na, Mg, Ga and other elements in seawater. At the same time, the characteristic spectra of Cu, Al and some other elements are found in the shell samples, which suggest that there are heavy metal pollution components in the shells.