Water Toxicity Risk Assessment Method

Abstract

The present invention aims to solve the problems of conventional water toxicity risk assessment methods, which have long testing periods, complicated experimental procedures, and the inability to perform stepwise assessments of water toxicity risk levels corresponding to water quality samples. [Solution] A water quality toxicity risk assessment method comprising: (1) filtering a wastewater sample; (2) exposing algal cells to the filtered wastewater sample; (3) obtaining a multi-parameter phenotypic profile of the algal cells; and (4) evaluating the water quality risk level of the wastewater sample based on the multi-parameter phenotypic profile obtained in step (3) and a risk index calculation formula.

Description

[Technical Field]

[0001] This invention relates to the field of water quality risk management technology, and more particularly to methods for evaluating water quality toxicity risks. [Background technology]

[0002] According to the "Global Chemicals Outlook" published by the United Nations Environment Assembly, global chemical production is expected to double by 2030. This rapid growth in chemical production is leading to a massive influx of highly toxic substances and low concentrations of toxic and harmful pollutants into the natural environment through wastewater, increasing the potential water toxicity risk of wastewater and making the protection of aquatic ecosystems and water health a critical issue. Therefore, wastewater water toxicity risk assessment is receiving increasing attention.

[0003] However, the composition of pollutants in water is complex and our knowledge is incomplete. New pollutants that are not yet registered as chemical substances are constantly emerging. Therefore, even using conventional targeted quantitative analysis methods, we can only assess the concentration levels and potential risks of certain types / classes of pollutants in wastewater, and cannot adequately reflect the overall potential risk of the wastewater. More importantly, current water pollution problems are shifting from single-pollution to complex-pollution. Additive, synergistic, antagonistic, and other co-toxic effects exist among the excess low-concentration complex pollutants in wastewater, making it impossible to accurately assess the risk of water toxicity due to the combined effects of complex pollutants through physicochemical analysis alone. Therefore, accurately and effectively assessing the water toxicity risk of sewage is an urgent issue in the field of water quality risk management.

[0004] Biotoxicity testing is the best way to visualize the combined toxic effects of all pollutants in wastewater. This method uses model aquatic organisms (luminescent bacteria, Daphnia, Pseudokirchneriella subcapitataThis method involves exposing water samples to organisms such as (etc.) and evaluating the water quality toxicity risk of the target water sample based on changes in the characteristics of the test organisms (growth, development, behavior, etc.). For example, the international standard ISO 8692-2012 "Freshwater Algal Growth Inhibition Tests with Unicellular Green Algae for Water Quality" uses Chlamydomonas reinhardtii or Pseudokirchneriella subcapitata In this method, algae are inoculated as test organisms, cultured in gradient dilutions of water samples, and the inhibition rates of algal cell growth at different concentration groups are applied to the water samples. The concentration of the water sample that elicits a certain biological response (typically 10% or 50% inhibition of algal cell growth) is calculated, and the characteristics of the biological response are evaluated. This method is used to evaluate the combined toxicity of water samples. However, this method is not suitable for rapidly and easily evaluating water quality risk because the test period is long and the experimental procedure is complicated. Furthermore, since the biological response is calculated based on a single biological characteristic, it is difficult to accurately reflect the risk of water quality toxicity. In addition, existing methods can only determine the concentration level of water samples that cause biotoxic effects, and it is not yet possible to hierarchically evaluate the water quality toxicity risk level of the corresponding water samples. Therefore, there is an urgent need to develop a water quality toxicity risk assessment method that can rapidly and comprehensively evaluate characteristics and reflect the water quality toxicity risk level. [Overview of the project] [Problems that the invention aims to solve]

[0005] The present invention aims to solve the problems of conventional water toxicity risk assessment methods, which have long testing periods, complicated experimental procedures, and the inability to perform stepwise assessments of water toxicity risk levels corresponding to water quality samples. Therefore, the present invention provides a novel water toxicity risk assessment method. [Means for solving the problem]

[0006] This invention employs the following technical solution: A method for assessing water toxicity risk, comprising the following steps: (1) Filtration treatment of wastewater samples; (2) Exposure of algal cells to toxic substances; (3) Obtaining multi-parameter phenotypic profiles of algal cells; (4) A step to evaluate the water quality risk level of a wastewater sample based on the risk index calculation formula and the multi-parameter phenotypic profile obtained in step (3).

[0007] Furthermore, the filtration treatment of the wastewater sample in step (1) is a step in which the wastewater sample is filtered using either a 0.22 μm cellulose acetate membrane, a cellulose nitrate membrane, or a mixed cellulose ester membrane to obtain a filtered wastewater sample.

[0008] Furthermore, the exposure of algal cells to toxic substances in step (2) is Pseudokirchneriella subcapitata This process involves exposing cells to filtered wastewater samples for 24 to 48 hours.

[0009] Furthermore, the acquisition of a multi-parameter phenotypic profile in step (3) involves labeling the cellular structure of algal cells using a mixed fluorescent stain, performing high-throughput imaging of the stained and labeled cells using a high-volume imaging system, and acquiring a multi-parameter phenotypic profile of the algal cells.

[0010] Furthermore, the mixed fluorescent stain consists of 3-10 μg / mL of Hoechst 33342, 6-12 μM of SYTO 14, 5-20 μL / mL of Concanavalin A / Alexa Fluor 488, 1-3 μg / mL of Wheat germ agglutinin Alexa Fluor 555 conjugate, and 3-12 μL / mL of Phalloidin Alexa Fluor 568 conjugate.

[0011] Furthermore, the imaging conditions for the high-volume imaging system involve simultaneously imaging five fluorescence channels using a 63x water immersion objective lens. The excitation / emission wavelengths for each fluorescence channel are DNA (405nm / 445 / 45nm), RNA (488nm / 600 / 37nm), ER (488nm / 525 / 50nm), AGP (561nm / 600 / 37nm), and Chl (628nm / 692 / 40nm). Images of stained and labeled cells are acquired using 4 to 16 fields per well, Z-stack, maximum intensity projection, and confocal imaging.

[0012] Furthermore, the multi-parameter phenotypic profile extraction process uses bright-field images for image correction. Image segmentation is performed using either thresholding, edge detection, watershed, or region segmentation to identify ROIs and obtain cell objects separated from the background. Then, for each cell, biological parameters including fluorescence intensity, cellular morphological parameters, and cellular texture parameters are extracted.

[0013] Furthermore, the risk indicator in process (4) is calculated using the following formula: Risk indicator = -0.0042 × R1 2 -0.0042 × R2 2 +0.073×C1×D1-0.00055×R1×C2-0.00081×R2×E1-0.00062×N1×A2-0.00067×N2×A1-0.043×D1-0.025×D2-0.068×D3-0.05×R2+0.051×E2-0.024×C1+0.012

[0014] Here, A1, A2: AGP texture contrast moment terms 1 and 2 respectively. C1, C2: Chloroplast texture measurement terms 1 and 2, respectively. D1, D2, D3: DNA texture difference variance term 1, 2, DNA texture angular moment term 1, respectively. E1, E2: ER texture angular second moment terms 1 and 2 respectively N1, N2: Cell nuclear shape area terms 1 and 2 respectively. R1, R2: RNA texture average value terms 1 and 2 respectively Furthermore, for the evaluation of the water quality risk level in step (4), when the risk index is 0 or less, it is defined as low risk; when it is greater than 0 and 0.2 or less, it is defined as medium risk; and when it exceeds 0.2, it is defined as high risk.

[0015] The advantageous effects of the present invention in comparison with the prior art are as follows.

[0016] (1) The present invention provides a water quality toxicity risk assessment method that solves the problems of the conventional method, such as the length of the test period, the complexity of the experimental operation, and the inability to perform a step - by - step evaluation of the water quality toxicity risk level corresponding to the water quality sample.

[0017] (2) Based on the high - content imaging system, the present invention can automatically obtain the biotoxicity effect characteristics of algal cells after exposure to toxic substances with high throughput without performing step - by - step dilution of the water sample, thus greatly improving the speed of the evaluation test.

[0018] (3) By utilizing the biological reaction of sewage at the sub - cellular structure level of algal cells, the present invention obtains a number of cell - biological parameters and establishes a numerical relationship between the water quality toxicity risk level and these parameters, thereby providing technical support for the water quality toxicity risk assessment in sewage treatment plants.

Brief Description of the Drawings

[0019] [Figure 1] Flowchart of the water quality toxicity risk assessment method of the present invention. [Figure 2] Microscopic image of the sub - cellular structure of algal cells in Example 1. [Figure 3] Microscopic image of the sub - cellular structure of algal cells in Example 2. [Figure 4] Microscopic image of the sub - cellular structure of algal cells in Example 3.

Modes for Carrying Out the Invention

[0020] The present invention will be further described below based on specific examples, but the scope of protection of the present invention is not limited to these examples.

[0021] As shown in Figure 1, the water quality toxicity risk assessment method of the present invention includes the following steps. (1) Filtration treatment of wastewater samples; (2) Exposure of algal cells to toxic substances; (3) Acquisition of multi-parameter phenotypic profiles of algal cells (4) A step to evaluate the water quality risk level of a wastewater sample based on the risk index calculation formula and the multi-parameter phenotypic profile obtained in step (3). Water quality risk levels are assessed as follows: a risk index of 0 or less is considered low risk; a value greater than 0 and 0.2 or less is considered medium risk; and a value greater than 0.2 is considered high risk. [Examples]

[0022] This example focuses on wastewater samples from all stages (influent, anaerobic tank, hypoxic tank, aerobic tank, secondary sedimentation tank, sand filtration tank, disinfection tank, and effluent) at a municipal wastewater treatment plant in Heilongjiang Province. The treatment plant has a processing capacity of 9 m³ per day. 3 The COD, total nitrogen, and total phosphorus concentrations of the influent water are 282.41 mg / L, 32.55 mg / L, and 4.44 mg / L, respectively, and the COD, total nitrogen, and total phosphorus concentrations of the effluent water are 48.83 mg / L, 11.91 mg / L, and 1.02 mg / L, respectively. The water quality toxicity risk assessment method of the present invention includes the following steps.

[0023] Step 1: The wastewater sample is filtered using a 0.22 μm cellulose acetate membrane to obtain the filtered wastewater sample.

[0024] Step 2: Pseudokirchneriella subcapitata The cells were exposed to a filtered wastewater sample for 24 hours (algal cell exposure test).

[0025] Step 3: Prepare the following mixed fluorescent stain. 3 μg / mL Hoechst33342 6μM SYTO14 5μL / mL Concanavalin A / Alexa Fluor488 1 μg / mL Wheat germ agglutinin Alexa Fluor555 conjugate 3 μL / mL Phalloidin Alexa Fluor568 Conjugate

[0026] This mixed fluorescent stain is used to label the cellular structure of algal cells. Five fluorescence channels are simultaneously imaged using a high-volume imaging system with a 63x immersion objective lens. The excitation / emission wavelengths for each channel are DNA (405nm / 445 / 45nm), RNA (488nm / 600 / 37nm), ER (488nm / 525 / 50nm), AGP (561nm / 600 / 37nm), and Chl (628nm / 692 / 40nm). Images are acquired using four fields per well, Z-stack, maximum intensity projection, and confocal imaging. Brightfield images are used for image correction, and cell images are segmented using the threshold method to identify ROIs. Subsequently, cell objects separated from the background are acquired, and biological parameters including fluorescence intensity, cellular morphological parameters, and cellular texture parameters are extracted to obtain a multi-parameter phenotypic profile.

[0027] Step 4: Evaluate the water quality risk level of the wastewater sample based on the multi-parameter phenotypic profile obtained in Step 3 and the risk index calculated by the following formula.

[0028] If the risk indicator is ≤ 0: Low risk If the risk indicator is < 0.2: Medium risk If the risk indicator is > 0.2:

[0029] High-risk risk indicator calculation formula: Risk indicator = -0.0042 × R1 2 -0.0042 × R2 2+0.073×C1×D1-0.00055×R1×C2-0.00081×R2×E1-0.00062×N1×A2-0.00067×N2×A1-0.043×D1-0.025×D2-0.068×D3-0.05×R2+0.051×E2-0.024×C1+0.012

[0030] Here, A1, A2: AGP texture contrast moment terms 1 and 2 respectively. C1, C2: Chloroplast texture measurement terms 1 and 2, respectively. D1, D2, D3: DNA texture difference variance term 1, 2, DNA texture angular moment term 1, respectively. E1, E2: ER texture angular second moment terms 1 and 2 respectively N1, N2: Cell nuclear shape area terms 1 and 2 respectively. R1, R2: RNA texture mean terms 1 and 2 respectively.

[0031] figure 3 The image shows microscopic images after an algal cell exposure test using wastewater samples from all stages of a municipal sewage treatment plant in Heilongjiang Province, obtained using this method. Cell biological parameters were extracted from these images, and a multi-parameter phenotypic profile was obtained. Using a risk index calculation formula, the water quality risk levels of each stage (influent, anaerobic tank, hypoxic tank, aerobic tank, secondary sedimentation tank, sand filtration tank, disinfection tank, and effluent) were evaluated, and determined to be high risk (0.41), high risk (0.39), medium risk (0.10), medium risk (0.12), low risk (-0.02), low risk (-0.31), medium risk (0.13), and medium risk (0.13), respectively. [Examples]

[0032] The difference from Example 1 is that this example targets secondary treated effluent from a municipal sewage treatment plant in Fujian Province. The treatment capacity of this plant is 1.3 million m³ per day. 3The COD, total nitrogen, and total phosphorus concentrations of the secondary treated effluent are 46.84 mg / L, 10.13 mg / L, and 1.21 mg / L, respectively. The water quality toxicity risk assessment method of the present invention includes the following steps.

[0033] Step 1: The wastewater sample is filtered using a 0.22 μm mixed cellulose ester membrane to obtain the filtered wastewater sample.

[0034] Step 2: Pseudokirchneriella subcapitata The cells were exposed to filtered wastewater samples for 48 hours (algal cell exposure test).

[0035] Step 3: Prepare the following mixed fluorescent stain. 10 μg / mL Hoechst33342 12μM SYTO14 20μL / mL Concanavalin A / Alexa Fluor488 3 μg / mL Wheat germ agglutinin Alexa Fluor555 conjugate 12 μL / mL Phalloidin Alexa Fluor568 Conjugate

[0036] After labeling the cellular structure of algal cells with this mixed fluorescent stain, five fluorescence channels are simultaneously imaged using a high-volume imaging system with a 63x water immersion objective lens. The excitation / emission wavelengths for each channel are DNA (405nm / 445 / 45nm), RNA (488nm / 600 / 37nm), ER (488nm / 525 / 50nm), AGP (561nm / 600 / 37nm), and Chl (628nm / 692 / 40nm). Images are acquired using 9 fields per well, Z-stack, maximum intensity projection, and confocal imaging. Brightfield images are used for image correction, and cell images are segmented using region segmentation to identify ROIs. Subsequently, cell objects separated from the background are acquired, and biological parameters including fluorescence intensity, cellular morphological parameters, and cellular texture parameters are extracted to obtain a multi-parameter phenotypic profile.

[0037] Step 4: Evaluate the water quality risk level of the wastewater sample based on the risk index calculation formula below and the multi-parameter phenotypic profile of the cells obtained in Step 2. Risk indicator ≤ 0: Low risk 0 < Risk indicator ≤ 0.2: Medium risk Risk indicator > 0.2:

[0038] High-risk risk indicator calculation formula: Risk indicator = -0.0042 × R1 2 -0.0042 × R2 2 +0.073×C1×D1-0.00055×R1×C2-0.00081×R2×E1-0.00062×N1×A2-0.00067×N2×A1-0.043×D1-0.025×D2-0.068×D3-0.05×R2+0.051×E2-0.024×C1+0.012

[0039] Here, A1, A2: AGP Texture Contrast Moment Terms 1, 2 C1, C2: Chloroplast texture measurement terms 1, 2 D1, D2, D3: DNA texture difference variance terms 1, 2, DNA texture angular second moment term 1 E1, E2: ER texture angular second moment terms 1, 2 N1, N2: Cell nucleus shape area term 1, 2 R1, R2: RNA texture mean terms 1, 2

[0040] figure 2 The image shows a microscopic image after an algal cell exposure test using secondary treated effluent from a municipal wastewater treatment plant in Fujian Province, obtained using this method. Cell biological parameters were extracted from this image, and a multi-parameter phenotypic profile was obtained. Based on the evaluation using the risk index calculation formula, the water quality risk index value was 0.07, and it was determined to be low risk. [Examples]

[0041] This example focuses on the inflow water from municipal sewage treatment plants in Chongqing and Sichuan Province. The treatment capacity of the municipal sewage treatment plant in Chongqing is 100,000 m³ per day.3 The COD, total nitrogen, and total phosphorus concentrations of the influent water are 236.05 mg / L, 27.83 mg / L, and 2.99 mg / L respectively. The treatment capacity of the municipal sewage treatment plant in Sichuan Province is 120,000 m per day. 3 The COD, total nitrogen, and total phosphorus concentrations of the influent water are 242.00 mg / L, 25.64 mg / L, and 2.51 mg / L respectively. The water quality toxicity risk assessment method of the present invention includes the following steps.

[0042] Step 1: Filter the sewage sample using a 0.22 μm nitrate cellulose membrane to obtain a filtered sewage sample.

[0043] Step 2: Expose the filtered sewage sample to Pseudokirchneriella subcapitata cells for 36 hours (algae cell exposure test).

[0044] Step 3: Prepare the following mixed fluorescent staining agent. 5 μg / mL Hoechst33342 9 μM SYTO14 10 μL / mL Concanavalin A / Alexa Fluor488 1.5 μg / mL Wheat germ agglutinin Alexa Fluor555 conjugate 9 μL / mL Phalloidin Alexa Fluor568 conjugate

[0045] The cellular structure of algal cells was labeled using this mixed fluorescent stain. Subsequently, five fluorescence channels were simultaneously imaged at high throughput using a high-volume imaging system with a 63x water immersion objective lens. The excitation / emission wavelengths for each fluorescence channel were DNA (405nm / 445 / 45nm), RNA (488nm / 600 / 37nm), ER (488nm / 525 / 50nm), AGP (561nm / 600 / 37nm), and Chl (628nm / 692 / 40nm). Images of the stained and labeled cells were acquired using 16 fields per well, Z-stack, maximum intensity projection, and confocal imaging. Brightfield images were used for image correction, and cell images were segmented using edge detection to identify ROIs. Then, cell objects separated from the background were acquired, and biological parameters including fluorescence intensity, cellular morphological parameters, and cellular texture parameters were extracted to obtain a multi-parameter phenotypic profile.

[0046] Step 4: The water quality risk level of the wastewater sample is evaluated using the multi-parameter phenotypic profile obtained in Step 3 and the following risk index calculation formula. Risk indicator ≤ 0: Low risk 0 < Risk indicator ≤ 0.2: Medium risk Risk indicator > 0.2:

[0047] High-risk risk indicator calculation formula: Risk indicator = -0.0042 × R1 2 -0.0042 × R2 2 +0.073×C1×D1-0.00055×R1×C2-0.00081×R2×E1-0.00062×N1×A2-0.00067×N2×A1-0.043×D1-0.025×D2-0.068×D3-0.05×R2+0.051×E2-0.024×C1+0.012

[0048] Here, A1, A2: AGP Texture Contrast Moment Terms 1, 2 C1, C2: Chloroplast texture measurement terms 1, 2 D1, D2, D3: DNA texture difference variance terms 1, 2, DNA texture angular second moment term 1 E1, E2: ER texture angular second moment terms 1, 2 N1, N2: Cell nucleus shape area term 1, 2 R1, R2: RNA texture mean terms 1, 2

[0049] Figure 4 shows microscopic images after algal cell exposure tests using influent water from municipal sewage treatment plants in Chongqing and Sichuan Province, obtained using this method. Cell biological parameters were extracted from these images, and multi-parameter phenotypic profiles were obtained. Based on evaluation using the risk index calculation formula, the influent water from the municipal sewage treatment plant in Chongqing was judged to be high risk with a water quality risk index value of 0.85, while the influent water from the municipal sewage treatment plant in Sichuan Province was judged to be medium risk with a water quality risk index value of 0.18.

Claims

1. (1) filtering the wastewater sample; (2) exposing algal cells to the filtered wastewater sample; (3) obtaining a multiparameter phenotypic profile of the algal cells; and (4) evaluating the water quality risk level of the wastewater sample based on the multiparameter phenotypic profile and risk index calculation formula obtained in step (3), In the acquisition of the multi-parameter phenotypic profile in Project (3), a mixed fluorescent stain is prepared by mixing multiple fluorescent dyes, including 3 - 10 μg / mL of Hoechst 33342, 6 - 12 μM of SYTO 14, 5 - 20 μL / mL of Concanavalin A / Alexa Fluor 488, 1 - 3 μg / mL of Wheat germ agglutinin Alexa Fluor 555 conjugate, and 3 - 12 μL / mL of Phalloidin Alexa Fluor 568 conjugate. Using this mixed fluorescent stain to label the cell structure of algal cells, with a high-content imaging system, the imaging conditions are as follows: using a 63x water immersion objective lens, simultaneously imaging 5 fluorescent channels, and the excitation wavelength / emission wavelength for each fluorescent channel is DNA (excitation wavelength 405 nm / emission wavelength 445 / 45 nm), RNA (excitation wavelength 488 nm / emission wavelength 600 / 37 nm), ER (excitation wavelength 488 nm / emission wavelength 525 / 50 nm), AGP (excitation wavelength 561 nm / emission wavelength 600 / 37 nm), Chl (excitation wavelength 628 nm / emission wavelength 692 / 40 nm). The imaging is performed with 4 - 16 fields of view per well, Z-stack, maximum intensity projection, and confocal imaging to obtain images of the stained and labeled cells, thereby performing high-throughput imaging of the stained and labeled cells. The brightfield image is used for image correction. Image segmentation is performed using either the threshold method, edge detection method, watershed method, or region segmentation method to identify the ROI and obtain cell objects separated from the background. Subsequently, for each cell, biological parameters including fluorescence intensity, cell morphological parameters, and cell texture parameters are extracted to obtain the multi-parameter phenotypic profile of algal cells containing A 1 、A 2 、C 1 、C 2 、D 1 、D 2 、D 3 、E 1 、E 2 、N 1 、N 2 、R 1 、R 2 to obtain the multi-parameter phenotypic profile of algal cells including A 1 、A 2 These are AGP texture contrast moment terms 1 and 2, respectively; C 1 , C 2 These are chloroplast texture measurement terms 1 and 2, respectively; D 1 , D 2 , D 3 These are DNA texture difference variance terms 1 and 2, and DNA texture angular second moment term 1, respectively; E 1 , E 2 These are the ER texture angle second moment terms 1 and 2, respectively; N 1 , N 2 These are the cell nuclear shape area terms 1 and 2, respectively; R 1 , R 2 These are the RNA texture mean terms 1 and 2, respectively. The risk indicator for process (4) is calculated using the following formula: Risk index = -0.0042 × R 1 2 -0.0042×R 2 2 +0.073 × C 1 ×D 1 -0.00055×R 1 ×C 2 -0.00081×R 2 ×E 1 -0.00062 × N 1 ×A 2 -0.00067×N 2 ×A 1 -0.043 × D 1 -0.025 × D 2 -0.068 × D 3 -0.05×R 2 +0.051×E 2 -0.024 × C 1 +0.012 The water quality risk level assessment in process (4) is a water toxicity risk assessment method characterized by classifying a risk index of 0 or less as low risk, a value greater than 0 and 0.2 or less as medium risk, and a value greater than 0.2 as high risk.

2. The water quality risk assessment method according to claim 1 is characterized in that the filtration treatment in step (1) is performed using a 0.22 μm cellulose acetate membrane, a cellulose nitrate membrane, or a mixed cellulose ester membrane.

3. The water quality risk assessment method according to claim 1 is characterized in that step (2) involves exposing a filtered wastewater sample to Pseudokirchneriella subcapitata cells for 24 to 48 hours.

Images