Recently, microplastics (MP) have emerged as global contaminants that seriously affect human and ecological health. However, rapid identification of MP is still a challenge, whether from oceans, wastewater, sediment or soil. A system based on laser-Raman spectroscopy analysis for qualitative testing of MP was established. The monitoring system can realize in-situ real-time detection and nondestructive testing, which provide a large amount of Raman spectroscopy of MP for Marine environmental analysis. A database suitable for microplastics analysis was presented based on the characteristic of Raman spectroscopy. Extra Trees algorithm was presented for the automatic identification of MP in this paper. The algorithm network is trained to detect random MP based on the established database, which including pure MP and mixed MP. The experiment result shows that several MP samples, including pure polystyrene (PS), Polymethyl methacrylate (PMMA), polyethylene terephthalate (PET), polyethylene (PE), Polyamide (PA), polyvinyl chloride (PVC) and polypropylene (PP) could be individually and automatically identified. The experiment result demonstrated that over 98.82% mixed particles could be correctly identified. The results were consistent with Extra Trees model built for identifying six types of MP, indicating Extra Trees model was highly robust for more than six of MP detection. The spectroscopy analysis method in this paper provides data support for systematically understanding the microplastic contamination.
A monitoring system for nitrate concentration in seawater based on ultraviolet absorption spectrum is discussed. The monitoring system can realize in-situ real-time monitoring and provide a large amount of monitoring data for Marine environmental analysis, which has fast measurement speed, simple operation, long time continuous monitoring and reagent-free. Savitzky-golay (SG) convolution smoothing pretreatment is used in the calculation, which can remove noise through spectral pretreatment of the acquired absorption spectral data. According to the relationship between the concentration of nitrate and the absorbance in ultraviolet, Partial least square method (PLS) is used in the model established to measure the nitrate concentration. Based on the interval Partial Least Square (iPLS), the ultraviolet absorption spectrum of 219-244 nm is selected in the model to represent the whole band for modeling, which can reduce calculation time and increase model accuracy. The optimal number of principal component was determined to be 3 on the basis of cross-validation Qh2. By laboratory system evaluation, for the artificial seawater with NO3--N concentration of 30-750μg/l, the nitrate concentration using PLS model is higher in linear correlation to its actual concentration (R2=0.999) in which the Root Mean Squared Error(RMSE) is 10.27, mean absolute error is 8.02 μg/l, average relative error is 2.4%, indicating that the system has high detection accuracy and good stability, and is suitable for the continuous monitoring of nitrate concentration in low-nutrient seawater.
In order to achieve rapid detection of chemical oxygen demand (COD) in seawater, in-situ monitoring technology and instrument for seawater COD based on spectrum analysis were studied. The influence of chloride ion (Cl-), bromine ion (Br-) and turbidity on COD measurement was studied using quantitative method. The results show that the absorption peak of Cl- and Br- is mainly between 190nm and 225nm. The absorption spectral intensity almost unchanged adding Cl- and Br- with different concentrations. The influence of Cl- and Br- on ecological parameters measurement was fixed. The absorbance at 300-720nm is caused by turbidity. Turbidity compensation can be carried out by the absorbance at 300-720nm, the effect of turbidity is eliminated on COD calculation. An absorption spectrum model based on least square method was established using artificial seawater in the laboratory. The model was validated using blind samples, and comparison was done between model calculation and actual value.
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