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 involves exposing model aquatic organisms (such as bioluminescent bacteria, Daphnia, and Leptosphaeria lamblia) to a water sample and evaluating the water quality toxicity risk of the 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" inoculates Chlamydomonas reinhardtii or Leptosphaeria lambliae as test organisms, cultures them in a gradient dilution of the water sample, and applies the inhibition rate of algal cell growth at different concentration groups to the water sample. The concentration of the water sample that elicits a certain biological response (usually a 10% or 50% inhibition rate 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 unsuitable for rapidly and easily assessing water quality risk due to its long testing period and cumbersome experimental procedures. Furthermore, since biological responses are calculated based on a single biological characteristic, it is difficult to accurately reflect the risk of water toxicity. In addition, existing methods can only determine the concentration levels of water samples that cause biotoxic effects, and cannot yet hierarchically assess the water toxicity risk level of the corresponding water samples. Therefore, there is an urgent need to develop a water toxicity risk assessment method that can rapidly and comprehensively evaluate characteristics and reflect the water toxicity risk level. [Overview of the Initiative] [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) involves exposing Leptosphaeria lamblia 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: 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 stepwise dilution of the water sample, thus significantly improving the speed of the evaluation test.

[0018] (3) By utilizing the biological reaction of sewage at the subcellular structure level of algal cells, the present invention obtains a large 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 subcellular structure of algal cells in Example 1. [Figure 3] Microscopic image of the subcellular structure of algal cells in Example 2. [Figure 4] Microscopic image of the subcellular 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: Expose Leptosphaeria lamblia cells 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 2 shows microscopic images after algal cell exposure tests 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 multi-parameter phenotypic profiles were obtained. Using a risk index calculation formula, the water quality risk levels for 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. 3 The 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: Expose Leptosphaeria lamblia cells to a filtered wastewater sample 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 sewage sample based on the following risk index calculation formula and the multi-parameter phenotypic profile of the cells obtained in Step 2. Risk index ≤ 0: Low risk 0 < Risk index ≤ 0.2: Medium risk Risk index > 0.2:

[0038] High risk Risk index calculation formula: Risk index = -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 value terms 1, 2 D1, D2, D3: DNA texture differential dispersion term 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 terms 1, 2 R1, R2: RNA texture average value terms 1, 2

[0040] Figure 3 shows the microscopic images after the algal cell exposure test using the secondary treated effluent of a municipal sewage treatment plant in Fujian Province obtained by this method. Cell biological parameters were extracted from this image to obtain a multi-parameter phenotypic profile. As a result of the evaluation using the risk index calculation formula, the water quality risk index value was 0.07, and it was determined to be a low risk.

Example

[0041] This example targets the influent water of municipal sewage treatment plants in Chongqing City and Sichuan Province. The treatment capacity of the municipal sewage treatment plant in Chongqing City is 100,000 m per day 3The COD, total nitrogen, and total phosphorus concentrations in 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 m³ 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: The wastewater sample is filtered using a 0.22 μm cellulose nitrate membrane to obtain the filtered wastewater sample.

[0043] Step 2: Expose the filtered wastewater sample to Leptosphaeria lamblia cells for 36 hours (algal cell exposure test).

[0044] Step 3: The following mixed fluorescent stains were prepared. 5 μg / mL Hoechst33342 9μM SYTO14 10μL / 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: Mean RNA texture 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. A water quality toxicity risk assessment method characterized by (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.

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 Leptosphaeria lamblia cells to a filtered wastewater sample for 24 to 48 hours.

4. The water quality risk assessment method according to claim 1 is characterized in that 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.

5. The water quality risk assessment method according to claim 4 is characterized in that the mixed fluorescent dye consists of 3 to 10 μg / mL of Hoechst 33342, 6 to 12 μM of SYTO 14, 5 to 20 μL / mL of Concanavalin A / Alexa Fluor 488, 1 to 3 μg / mL of Wheat germ agglutinin Alexa Fluor 555 conjugate, and 3 to 12 μL / mL of Phalloidin Alexa Fluor 568 conjugate.

6. In the water quality risk assessment method described in claim 4, the imaging conditions for the high-volume imaging system are as follows: a 63x immersion objective lens is used to simultaneously image five fluorescence channels. The excitation wavelength / emission wavelength for each fluorescence 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), and Chl (excitation wavelength 628 nm / emission wavelength 692 / 40 nm). Imaging is performed using 4 to 16 fields per well, Z-stack, maximum intensity projection, and confocal imaging to obtain images of stained and labeled cells.

7. In the water quality risk assessment method according to claim 4, the step of extracting a multi-parameter phenotypic profile uses bright-field images for image correction. Image segmentation is performed using one of the following methods: thresholding, edge detection, watershed, or region segmentation to identify ROIs and obtain cell objects separated from the background. Thereafter, for each cell, biological parameters including fluorescence intensity, cellular morphological parameters, and cellular texture parameters are extracted.

8. In the water quality risk assessment method according to claim 1, the risk index in step (4) is calculated by 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[[ID=第27]] 1 -0.025 × D 2 -0.068 × D 3 -0.05 × R 2 +0.051 × E 2 -0.024 × C 1 +0.012 It should be noted that there seems to be a misspelling in "第27" which should probably be "". This has been left as is in the translation to maintain consistency with the original text. Here, 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 RNA texture mean terms 1 and 2, respectively.

9. The water quality risk assessment method according to claim 1 is characterized in that the water quality risk level assessment in step (4) is characterized in that a risk index of 0 or less is considered low risk, a risk index greater than 0 and 0.2 or less is considered medium risk, and a risk index greater than 0.2 is considered high risk.

Images