A new method for the rapid identification of external water types in rainwater pipeline networks using UV-Vis absorption spectroscopy.

Ultraviolet-visible (UV-Vis) absorption spectroscopy, due to its high sensitivity and capability for real-time online monitoring, is one of the most promising tools for the rapid identification of external water in rainwater pipe networks. However, difficulties in obtaining actual samples lead to insufficient real samples, and the complex composition of wastewater can affect the accurate traceability analysis of external water in rainwater pipe networks. In this study, a new method for identifying external water in rainwater pipe networks with a small number of samples is proposed. In this method, the Generative Adversarial Network (GAN) algorithm was initially used to generate spectral data from the absorption spectra of water samples; subsequently, the multiplicative scatter correction (MSC) algorithm was applied to process the UV-Vis absorption spectra of different types of water samples; following this, the Variational Mode Decomposition (VMD) algorithm was employed to decompose and recombine the spectra after MSC; and finally, the long short-term memory (LSTM) algorithm was used to establish the identification model between the recombined spectra and the water source types, and to determine the optimal number of decomposed spectra K. The research results show that when the number of decomposed spectra K is 5, the identification accuracy for different sources of domestic sewage, surface water, and industrial wastewater is the highest, with an overall accuracy of 98.81%. Additionally, the performance of this method was validated by mixed water samples (combinations of rainwater and domestic sewage, rainwater and surface water, and rainwater and industrial wastewater). The results indicate that the accuracy of the proposed method in identifying the source of external water in rainwater reaches 98.99%, with detection time within 10 s. Therefore, the proposed method can become a potential approach for rapid identification and traceability analysis of external water in rainwater pipe networks.

Rapid Response of Daphnia magna Motor Behavior to Mercury Chloride Toxicity Based on Target Tracking.

A rapid and timely response to the impacts of mercury chloride, which is indispensable to the chemical industry, on aquatic organisms is of great significance. Here, we investigated whether the YOLOX (improvements to the YOLO series, forming a new high-performance detector) observation system can be used for the rapid detection of the response of Daphnia magna targets to mercury chloride stress. Thus, we used this system for the real-time tracking and observation of the multidimensional motional behavior of D. magna. The results obtained showed that the average velocity (v¯), average acceleration (a¯), and cumulative travel (L) values of D. magna exposed to mercury chloride stress changed significantly under different exposure times and concentrations. Further, we observed that v¯, a¯ and L values of D. magna could be used as indexes of toxicity response. Analysis also showed evident D. magna inhibition at exposure concentrations of 0.08 and 0.02 mg/L after exposure for 10 and 25 min, respectively. However, under 0.06 and 0.04 mg/L toxic stress, v¯ and L showed faster toxic response than a¯, and overall, v¯ was identified as the most sensitive index for the rapid detection of D. magna response to toxicity stress. Therefore, we provide a strategy for tracking the motile behavior of D. magna in response to toxic stress and lay the foundations for the comprehensive screening of toxicity in water based on motile behavior.

Rapid Detection of Total Petroleum Hydrocarbons in Soil Using Advanced Fluorescence Imaging Techniques.

Chemical methods for measuring soil organic content are often slow and yield inaccurate results due to significant errors. Simple summation of components may not accurately determine total organic content. In contrast, fluorescence imaging techniques offer rapid, in situ monitoring without complex pretreatment and demonstrate rapid and accurate assessment of soil organic content. Utilizing a soil organic pollutant fluorescence imaging in situ monitoring system that we independently developed, we conducted laboratory experiments to explore methods for acquiring fluorescence signals of petroleum hydrocarbons in soil and extracting image features. We used this monitoring system to obtain fluorescence images of crude oil in standard soil (soil properties are shown in Table S1) samples at concentrations ranging from 0 to 100 g/kg, and the coefficient of determination of the total amount inversion model reached 0.999. Simultaneously, we applied the system to a deserted petroleum storage area, and the relative standard deviation values of 16 of the 18 groups of tests were less than 1%, indicating that the monitoring system is highly stable when applied in the field. This study provides both theoretical foundation and technical support for the rapid and nondestructive detection of total petroleum hydrocarbons in soil at field sites.

Determination of phenolic compounds in water using a multivariate statistical analysis method combined with three-dimensional fluorescence spectroscopy.

Phenolic compounds are toxic chemical pollutants present in water. Three-dimensional fluorescence spectroscopy analysis is an effective and rapid method for real-time phenol monitoring in aquatic environments. However, similar chemical structures of phenols result in highly overlapping three-dimensional fluorescence spectra. Therefore, it is extremely difficult to analyze and quantify the concentration of components in a mixture system that includes two or more phenolic compounds. In this article, we study the mixed phenol system containing phenol, o-cresol, p-cresol, m-cresol, catechol, and resorcinol combined with excitation-emission matrix (EEM) fluorescence data. A multivariate statistical method called best linear unbiased prediction (BLUP) is proposed to analyze the spectra with the aim to achieve quantitative results and a trilinear decomposition algorithm called parallel factor analysis (PARAFAC) was used for comparison. Two experiments with different calibration samples were set to validate the effectiveness of BLUP through recovery, ARecovery (Average Recovery), AREP (Average Relative Error of Prediction), and RMSE (Root Mean Square Error). Overall, the average recovery of each component in experiment 1 and experiment 2 ranged from 95.91% to 111.62% and 82.91% to 129.02%, respectively. Based on the results of the experiments, the concentration of phenolic compounds in water can be quantitatively determined by combining three-dimensional fluorescence spectroscopy with the BLUP method.

Phytoplankton Image Segmentation and Annotation Method Based on Microscopic Fluorescence.

Microscopic phytoplankton segmentation is an important part of water quality assessment. The segmentation of microscopic phytoplankton still faces challenges for computer vision, such as being affected by background impurities and requiring a large number of manual annotation. In this paper, the characteristics of phytoplankton emitting fluorescence under excitation light were utilized to segment and annotate phytoplankton contours by fusing fluorescence images and bright field images. Morphological operations were used to process microscopic fluorescence images to obtain the initial contours of phytoplankton. Then, microscopic bright field images were processed by Active Contour to fine tune the contours. Seven algae species were selected as the experimental objects. Compared with manually labeling the contour in LabelMe, the recall, precision, FI score and IOU of the proposed segmentation method are 85.3%, 84.5%, 84.7%, and 74.6%, respectively. Mask-RCNN was used to verify the correctness of labels annotated by the proposed method. The average recall, precision, F1 score and IOU are 97.0%, 86.5%, 91.1%, and 84.2%, respectively, when the Mask-RCNN is trained with the proposed automatic labeling method. And the results corresponding to manual labeling are 95.3%, 86.1%, 90.3%, and 82.8% respectively. The experimental results show that the proposed method can segment the phytoplankton microscopic image accurately, and the automatically annotated contour data has the same effect as the manually annotated contour data in Mask-RCNN, which greatly reduces the manual annotation workload.

Study on Calibration Method for the Count of Living Algal Cells Detection Based on Variable Fluorescence in Ballast Water.

In the monitoring the discharge of ballast water, the count of living algal cells is of utmost significant. Variable fluorescence, denoted as Fv, stands as an optimal parameter for photosynthetic fluorescence, efficiently charactering the living algal cells count, unaffected by the ballast waters' complex background fluorescence environment. This study deeply investigates the quantitative relationship between Fv and the count of living algal cells. Observations indicate that single cell fluorescence yield (abbreviated as SCF) varies significantly across different algae species, leading to considerable errors in quantifying living algal cell count in ballast water with unknown components using the calibration relationship between Fv and the cell count. Thus, correcting SCF prior to calibration becomes necessary. The paper proposes an innovative SCF correction method based on cell cross-sectional area and an eµ factor (where µ is the expected value of the functional absorption cross-section of PSII) This method mitigates the influence of cell size and species differences on quantifying the living algal cell count. Correction operation trials revealed that dividing the SCF measurement by cell cross-sectional area and multiplying by eµ enhanced the correction effect. Comparative experiments demonstrated marked improvement: Relative errors (REs) for Chlorella pyrenoidosa and Chlorella marine, both belonging to the Chlorophyta group, fell from 92.1% and 90.6% to 37.2% and 9.5% respectively post-correction. Similarly, REs for Thalassiosira weissflogii and Nitzschia closterium minutissima, from the Bacillariophyta group, decreased from 74.7% and 68.1% to 14.3% and 19.1% respectively. The RE of Peridinium from the Pyrrophyta group dropped from 28.4% to 12.1%. The results underscore the effectiveness of cell cross-sectional area and eµ in correcting SCF, thus offering a novel correction method for swift and precise measurement of living algal cell count in ballast water, based on variable fluorescence.

Correction method of the fluorescence intensity of petroleum hydrocarbons in different soil types based on resonance scattering spectroscopy.

Soil types has an obvious impact influence on the fluorescence intensity of soil petroleum hydrocarbons. To reduce the interference caused by soil types, this paper proposes a calibration method using resonance scattering spectroscopy. To establish the correction method, 100 g/kg crude oil samples from six soil types were prepared. The mission and resonance scattering spectrum under 280 nm excitation were measured for all samples. The results showed that the fluorescence spectra and resonance scattering spectra of soil crude oil vary with different soil types. And the fluorescence peak intensity and the resonance scattering peak intensity at 360 nm of soil petroleum hydrocarbons are highly correlated in different soil types. The fluorescence peak intensity at 360 nm was divided by the resonance scattering light intensity at 360 nm to obtain the corrected fluorescence intensity, which can effectively reduce the influence of soil type on fluorescence intensity. The feasibility of correction method was further verified for different types and concentrations of soil crude oil. The results showed better linearity between the fluorescence intensity and the concentration of Petroleum Hydrocarbons after the correction (with a correlation coefficient R2 of 0.96) than before the correction (with R2 of 0.72), the mean relative prediction error of all samples decreased from 31.92 % to 4.71 % after correction. The research can provide theoretical basis and technical support for the accurate and rapid detection of Petroleum Hydrocarbons in soil by fluorescence spectroscopy.

Quantitative Representation of Water Quality Biotoxicity by Algal Photosynthetic Inhibition.

The method based on the photosynthetic inhibition effect of algae offers the advantages of swift response and straightforward measurement. Nonetheless, this effect is influenced by both the environment and the state of the algae themselves. Additionally, a single parameter is vulnerable to uncertainties, rendering the measurement accuracy and stability inadequate. This paper employed currently utilized photosynthetic fluorescence parameters, including Fv/Fm(maximum photochemical quantum yield), Performance Indicator (PIabs), Comprehensive Parameter Index (CPI) and Performance Index of Comprehensive Toxicity Effect (PIcte), as quantitative toxicity characteristic parameters. The paper compared the univariate curve fitting results with the multivariate data-driven model results and investigated the effectiveness of Back Propagation(BP) Neural Network and Support Vector Machine for Regression (SVR) models to enhance the accuracy and stability of toxicity detection. Using Dichlorophenyl Dimethylurea (DCMU) samples as an example, the mean Relative Root Mean Square Error (RRMSE) corresponding to the optimal parameter PIcte for the dose-effect curve fitting was 1.246 in the concentration range of 1.25-200 µg/L. On the other hand, the mean RRMSEs corresponding to the results of the BP neural network and SVR models were 0.506 and 0.474, respectively. Notably, BP neural network exhibited excellent prediction accuracy in the medium-high concentration range of 7.5-200 µg/L, with a mean RRSME of only 0.056. Regarding the stability of the results, the mean Relative Standard Deviation (RSD) of the univariate dose-effect curve results was 15.1% within the concentration range of 50-200 µg/L. In contrast, the mean RSDs for both BP neural network and SVR results were less than 5%. In the concentration range of 1.25-200 µg/L, the mean RSDs were 6.1% and 16.5%, with the BP neural network performing well. The experimental results of Atrazine were analyzed to further validate the effectiveness of the BP neural network in improving the accuracy and stability of results. These findings provided valuable insights for the development of biotoxicity detection by using the algae photosynthetic inhibition method.

A Method of Water COD Retrieval Based on 1D CNN and 2D Gabor Transform for Absorption-Fluorescence Spectra.

Chemical Oxygen Demand (COD) is one of the indicators of organic pollution in water bodies. The rapid and accurate detection of COD is of great significance to environmental protection. To address the problem of COD retrieval errors in the absorption spectrum method for fluorescent organic matter solutions, a rapid synchronous COD retrieval method for the absorption-fluorescence spectrum is proposed. Based on a one-dimensional convolutional neural network and 2D Gabor transform, an absorption-fluorescence spectrum fusion neural network algorithm is developed to improve the accuracy of water COD retrieval. Results show that the RRMSEP of the absorption-fluorescence COD retrieval method is 0.32% in amino acid aqueous solution, which is 84% lower than that of the single absorption spectrum method. The accuracy of COD retrieval is 98%, which is 15.3% higher than that of the single absorption spectrum method. The test results on the actual sampled water spectral dataset demonstrate that the fusion network outperformed the absorption spectrum CNN network in measuring COD accuracy, with the RRMSEP improving from 5.09% to 1.15%.

A Sensitive Response Index Selection for Rapid Assessment of Heavy Metals Toxicity to the Photosynthesis of Chlorella pyrenoidosa Based on Rapid Chlorophyll Fluorescence Induction Kinetics.

Heavy metals as toxic pollutants have important impacts on the photosynthesis of microalgae, thus seriously threatening the normal material circulation and energy flow of the aquatic ecosystem. In order to rapidly and sensitively detect the toxicity of heavy metals to microalgal photosynthesis, in this study, the effects of four typical toxic heavy metals, chromium (Cr(VI)), cadmium (Cd), mercury (Hg), and copper (Cu), on nine photosynthetic fluorescence parameters (φPo, ΨEo, φEo, δRo, ΨRo, φRo, FV/FO, PIABS, and Sm) derived from the chlorophyll fluorescence rise kinetics (OJIP) curve of microalga Chlorella pyrenoidosa, were investigated based on the chlorophyll fluorescence induction kinetics technique. By analyzing the change trends of each parameter with the concentrations of the four heavy metals, we found that compared with other parameters, φPo (maximum photochemical quantum yield of photosystem II), FV/FO (photochemical parameter of photosystem II), PIABS (photosynthetic performance index), and Sm (normalized area of the OJIP curve) demonstrated the same monotonic change characteristics with an increase in concentration of each heavy metal, indicating that these four parameters could be used as response indexes to quantitatively detect the toxicity of heavy metals. By further comparing the response performances of φPo, FV/FO, PIABS, and Sm to Cr(VI), Cd, Hg, and Cu, the results indicated that whether it was analyzed from the lowest observed effect concentration (LOEC), the influence degree by equal concentration of heavy metal, the 10% effective concentration (EC10), or the median effective concentration (EC50), the response sensitivities of PIABS to each heavy metal were all significantly superior to those of φRo, FV/FO, and Sm. Thus, PIABS was the most suitable response index for sensitive detection of heavy metals toxicity. Using PIABS as a response index to compare the toxicity of Cr(VI), Cd, Hg, and Cu to C. pyrenoidosa photosynthesis within 4 h by EC50 values, the results indicated that Hg was the most toxic, while Cr(VI) toxicity was the lowest. This study provides a sensitive response index for rapidly detecting the toxicity of heavy metals to microalgae based on the chlorophyll fluorescence induction kinetics technique.