A method for determining the continuous variation of nanoplastics migration parameters with pH

Abstract

The present invention discloses a method for determining the continuous variation of the migration parameters of nanoplastics with pH. The method includes the following steps: obtaining a two-dimensional surface of the DLVO interaction energy between nanoplastics and each component of the mixed porous medium that varies with pH; using the double kinetic site retention model to fit and obtain the migration parameters, mass recovery rate, and standardized effluent concentration of nanoplastics under different pH conditions. Through regression analysis, the fitting relationship between the comprehensive DLVO barrier and the mass recovery rate of nanoplastics is obtained, and the quantitative variation relationship of the coefficient of determination R<supgt;2< / supgt> with the DLVO barrier proportionality coefficient between nanoplastics is searched. The DLVO barrier proportionality coefficient between nanoplastics when R<supgt;2< / supgt> is the largest is determined, and the continuous quantitative variation of the migration parameters of nanoplastics with pH is determined through the comprehensive DLVO barrier, so as to better quantitatively predict the magnitude of the migration ability of nanoplastics in the mixed porous medium.

Description

Technical Field

[0001] The present invention belongs to the technical field of pollutant migration, and more specifically, relates to a method for determining the continuous change of nanoplastics particle migration parameters with pH. Background Art

[0002] Groundwater is an important part of water resources, and more than 50% of the world's population uses groundwater as a drinking water source. With the widespread use of different plastics and unreasonable disposal methods after use, a large amount of plastic waste is released into environmental water bodies, and the environmental pollution caused by plastics has attracted more and more attention. After plastic waste is released into the environment, it decomposes under various actions, further forming microplastics with a particle diameter less than 5 mm and nanoplastics with a particle diameter less than 1 μm, which have a serious impact on aquatic and terrestrial environments.

[0003] Due to the larger specific surface area and lower surface polarity of nanoplastics particles than microplastics particles, more harmful substances such as heavy metals and persistent organic pollutants can be enriched. Under the action of runoff and the like, plastic particles will vertically migrate in the soil and cause the exchange of microplastics between the surface and groundwater. Natural soil is composed of various granular minerals, organic substances, microorganisms, etc. Therefore, it is very important to study the deposition of nanoplastics in the mixed porous medium.

[0004] The existing DLVO interaction energy theory is a classical theory used to explain colloid stability, which was proposed by four scholars, Derjaguin, Landau, Verwey, and Overbeek, in the 1940s of the last century. This theory can calculate the interaction between nanoplastics particles and the mixed porous medium, but it can only obtain the one-dimensional change relationship between the potential energy and the spacing between the nanoplastics particle-medium. However, the interaction energy between the nanoplastics particle-medium is also affected by other factors such as environmental pH. The existing one-dimensional change relationship between the potential energy and the spacing between the nanoplastics particle-medium cannot determine the continuous quantitative change of the migration ability and migration parameters of nanoplastics particles in the mixed porous medium with pH.

[0005] The double kinetic site model can better describe the migration and deposition of nanoplastics particles in the porous medium. The parameters related to the migration of nanoplastics particles obtained by model fitting are helpful to explain the migration behavior of nanoplastics in the mixed porous medium. The existing research cannot establish the relationship between the relevant migration parameters obtained by fitting the double kinetic site model and the DLVO two-dimensional surface, and cannot quantitatively predict the relevant migration parameters through the DLVO two-dimensional surface.

[0006] Therefore, there is currently a lack of a method to confirm the continuous quantitative change of the migration parameters of nanoplastics with pH in a mixed porous medium, and it is impossible to quantitatively predict the magnitude of the migration ability of nanoplastics in a mixed porous medium. Summary of the Invention

[0007] Aiming at the above-mentioned existing technical problems, the purpose of the present invention is to provide a method for determining the continuous change of nanoplastics migration parameters with pH. The method can accurately and quantitatively determine the continuous change of the migration parameters of nanoplastics with pH in a mixed porous medium.

[0008] In order to achieve the above purpose, the present invention is realized through the following technical solutions:

[0009] A method for determining the continuous change of nanoplastics migration parameters with pH, comprising the following steps:

[0010] S1. Measure the Zeta potential of nanoplastics and each component of the mixed porous medium under different pH conditions; measure the hydrodynamic diameter of nanoplastics under different pH conditions; obtain the mathematical relationship between the Zeta potential of nanoplastics and each component of the mixed porous medium and the change of the hydrodynamic diameter of nanoplastics with pH; measure and obtain the concentration C0 of nanoplastics before passing through the mixed porous medium and the concentration C of nanoplastics after passing through the mixed porous medium;

[0011] S2. Calculate the two-dimensional surface of the DLVO interaction energy between nanoplastics and each component of the mixed porous medium changing with pH according to the mathematical relationship obtained in step S1;

[0012] S3. Use the double kinetic site retention model to fit and obtain the migration parameters K1, K 1d , K2, S max2 of nanoplastics under different pH conditions; and calculate the mass recovery rate R RE of nanoplastics and the normalized effluent concentration P C / C0 under different pH conditions;

[0013] S4. Calculate the comprehensive DLVO barrier. Based on the calculated comprehensive DLVO barrier, obtain the fitting relationship between the comprehensive DLVO barrier and the mass recovery rate R RE of nanoplastics through regression analysis; search for the quantitative change relationship of its determination coefficient R 2 with the DLVO barrier proportionality coefficient between nanoplastics, and determine the DLVO barrier proportionality coefficient between nanoplastics when R 2 is the largest;

[0014] S5. According to R 2Proportionality coefficient of DLVO barrier between nanoplastics particles at maximum, calculate the comprehensive DLVO barrier, and obtain the parameter fitting equations of the comprehensive DLVO barrier with the migration parameters K1, K 1d , K2, S max2 , R RE and P C / C0 through regression analysis, and calculate the migration parameters K1, K 1d , K2, S max2 , R RE and P C / C0 with the continuous change of pH.

[0015] Preferably, in the step S1, use a Malvern nanoparticle size potentiometer to measure the Zeta potential of the nanoplastics particles and each component of the mixed porous medium under different pH conditions by laser Doppler microelectrophoresis; use dynamic light scattering method to measure the hydrodynamic diameter of the nanoplastics particles under different pH conditions.

[0016] Preferably, in the step S1, obtain the mathematical relationships Ψ p (C pH )~C pH of the Zeta potential of the nanoplastics particles varying with pH, R p (C pH )~C pH of the hydrodynamic diameter of the nanoplastics particles varying with pH, and Ψ c (C pH )~C pH of the Zeta potential of each component of the mixed porous medium varying with pH by regression analysis fitting.

[0017] Preferably, in the step S2, the two-dimensional surface of the DLVO interaction energy is:

[0018] φ tot (C pH ,h)=φ vdw (C pH ,h)+φ edl (C pH ,h)+φ Born (C pH ,h)

[0019]

[0020]

[0021]

[0022]

[0023]

[0024] Among them, h represents the distance (m) between the nanoplastics particles and the mixed porous medium; φ tot (C pH , h) is the two-dimensional distribution of the total interaction energy with respect to pH and h; φ vdw (C pH , h) is the van der Waals gravitational potential energy; φ edl (C pH , h) is the double-layer repulsive potential energy; φ Born (C pH , h) is the Born repulsive potential energy; C pH is the pH of the solution; ε0 is the vacuum permittivity (8.845×10 -12 F·m -1 ); ε r is the relative permittivity of water (78.5 F·m at 20°C -1 ); R p (C pH ) is the hydrodynamic diameter (m) of the nanoplastics particles; Ψ p (C pH ) is the Zeta potential (V) of the nanoplastics particles; Ψ c (C pH ) is the Zeta potential (V) of each component of the mixed porous medium; k B is the Boltzmann constant (1.38×10 -23 J·K -1 ); T is the temperature (298.15 K); e is the elementary charge of an electron (C); κ is the reciprocal of the Debye length; N A is the Avogadro constant; λ is the characteristic wavelength, generally taken as 10 -7 m; A 123 is the Hamaker constant; A 11 , A 22 and A 33 represent the Hamaker constants of the nanoplastics particles, water, and each component of the mixed porous medium, respectively; σ Bron is the Bron collision parameter.

[0025] Preferably, in the step S3, the migration parameters K1, K 1d , K2, S max2 of the nanoplastics particles under different pH conditions are as follows:

[0026]

[0027] Among them, θ is the porosity of the porous medium (dimensionless); C is the liquid-phase concentration of the nanoplastics (mg·L- 1); t is time (T); ρb is the bulk density of the porous medium (g·m- 3 ); x is the spatial ordinate (cm);

[0028] The dual kinetic site retention model classifies the surface sites of the mixed porous medium into two categories: reversible retention sites and irreversible retention sites. It is assumed that the retention of nanoplastics at site 1 is reversible and at site 2 is irreversible. The specific equation is:

[0029]

[0030]

[0031]

[0032] Among them, S1 is the solid-phase deposition concentration of nanoplastics at site 1, and S2 is the solid-phase deposition concentration of nanoplastics at site 2; k1 and k2 are the adsorption coefficients of nanoplastics at site 1 and site 2 respectively (min- 1 ); k 1d is the desorption coefficient of nanoplastics at site 1 (min -1 ); Ψ L represents the retention parameter related to time, S max2 is the maximum solid-phase adsorption concentration of nanoplastics at site 2 (g·g- 1 ).

[0033] Preferably, in step S3, the mass recovery rate R of nanoplastics under different pH conditions RE is:

[0034]

[0035] Among them, S test is the area of the breakthrough curve obtained by nanoplastics passing through the mixed porous medium; S tracer is the area of the breakthrough curve obtained by the tracer passing through the mixed porous medium.

[0036] Preferably, in step S3, the concentration of nanoplastics after passing through the mixed porous medium is measured, and then the normalized effluent concentration P of nanoplastics under different pH conditions is calculated C / C0 :

[0037]

[0038] Among them, C is the concentration measured after nanoplastics pass through the mixed porous medium; C0 is the measured concentration of nanoplastics.

[0039] Preferably, in the step S4, the comprehensive DLVO barrier is as follows:

[0040] C DB = DB PP × F PP + DB ID × (1 - F PP )

[0041] DB ID = DB QS × P QS + DB CM × P CM

[0042] Wherein, C DB is the comprehensive DLVO barrier (kT); DB ID is the weighted DLVO barrier (kT) between the nanoplastics particles and the mixed porous medium (when the mixed porous medium does not contain clay minerals, the mixed porous medium here is only quartz sand); DB PP is the DLVO barrier (kT) between the nanoplastics particles; DB QS is the DLVO barrier (kT) between the nanoplastics particles and the quartz sand; DB CM is the DLVO barrier (kT) between the nanoplastics particles and the clay minerals; F PP is the DLVO barrier proportionality coefficient between the nanoplastics particles, which needs to be searched and identified; P QS is the mass percentage (%) of quartz sand in the mixed porous medium; P CM is the mass percentage (%) of clay minerals in the mixed porous medium, P QS + P CM = 100%.

[0043] Preferably, the nanoplastics particles are nanoparticle polystyrene particles, nanoparticle polyethylene particles, nanoparticle polyvinyl chloride particles or nanoparticle polyethylene terephthalate particles.

[0044] Preferably, the mixed porous medium is quartz sand, or a mixture of quartz sand and clay minerals; the clay minerals include one or more of kaolinite, montmorillonite and illite.

[0045] More preferably, the mixed porous medium is a mixture of quartz sand and clay minerals; the clay minerals include kaolinite, montmorillonite and illite.

[0046] Compared with the prior art, the present invention has the following beneficial effects: The present invention provides a method for determining the continuous variation of nanoplastics migration parameters with pH. The method based on the two-dimensional surface of the DLVO interaction energy can not only clearly show the variation of the interaction energy between micro / nanoplastic particles and each component of the mixed medium with pH, but also proposes a new calculation method for the comprehensive DLVO barrier. Through regression analysis, the fitting relationship between the comprehensive DLVO barrier and the mass recovery rate (R RE ) of nanoplastics particles is obtained, and the quantitative variation relationship of the determination coefficient R 2 with the DLVO barrier proportionality coefficient between nanoplastics particles is searched, and the DLVO barrier proportionality coefficient between nanoplastics particles when R 2 is the largest is determined. Finally, the double-point kinetic parameters (i.e., migration parameters K1, K 1d , K2, S max2 ) related to the migration of nanoplastics particles are determined to vary continuously and quantitatively with the pH in the environment, so as to better quantitatively predict the migration ability of nanoplastics particles in the mixed porous medium. Description of the Drawings

[0047] Figure 1 is the scanning electron micrograph of the mixed porous medium; among them, Figure 1 (a) is the scanning electron micrograph of quartz sand; Figure 1 (b) is the scanning electron micrograph of kaolinite; Figure 1 (c) is the scanning electron micrograph of illite; Figure 1 (d) is the scanning electron micrograph of montmorillonite.

[0048] Figure 2 is the Zeta potential of nanoplastics particles (PSNPs) and the mixed porous medium under different pH conditions; among them, Figure 2 (a) is the Zeta potential of quartz sand under different pH conditions; Figure 2 (b) is the Zeta potential of kaolinite under different pH conditions; Figure 2 (c) is the Zeta potential of illite under different pH conditions; Figure 2 (d) is the Zeta potential of montmorillonite under different pH conditions; Figure 2 (e) is the Zeta potential of PSNPs under different pH conditions; Figure 2 (f) is the hydrodynamic diameter of PSNPs under different pH conditions;

[0049] Figure 3 is the two-dimensional distribution (surface) of the DLVO interaction energy between nanoplastics particles (PSNPs) and each component of the mixed porous medium; among them, Figure 3(a) is the two-dimensional distribution (surface) of the DLVO interaction energy between PSNPs and quartz sand; Figure 3 (b) is the two-dimensional distribution (surface) of the DLVO interaction energy between PSNPs and kaolinite; Figure 3 (c) is the two-dimensional distribution (surface) of the DLVO interaction energy between PSNPs and illite; Figure 3 (d) is the two-dimensional distribution (surface) of the DLVO interaction energy between PSNPs and montmorillonite; Figure 3 (e) is the two-dimensional distribution (surface) of the DLVO interaction energy between PSNPs and PSNPs.

[0050] Figure 4 is the one-dimensional curve of the interaction energy between nanoplastics particles (PSNPs) and each component of the mixed porous medium varying with the spacing h at pH = 7; among them, Figure 4 (a) is the one-dimensional curve of the interaction energy between PSNPs - quartz sand particles varying with the spacing h at pH = 7; Figure 4 (b) is the one-dimensional curve of the interaction energy between PSNPs - kaolinite particles varying with the spacing h at pH = 7; Figure 4 (c) is the one-dimensional curve of the interaction energy between PSNPs - illite particles varying with the spacing h at pH = 7; Figure 4 (d) is the one-dimensional curve of the interaction energy between PSNPs - montmorillonite particles varying with the spacing h at pH = 7.

[0051] Figure 5 is the continuous variation of the potential barrier of the DLVO interaction energy between nanoplastics particles (PSNPs) and each component of the mixed porous medium with pH; among them, Figure 5 (a) is the continuous variation of the potential barrier of the DLVO interaction energy between PSNPs - quartz sand particles with pH; Figure 5 (b) is the continuous variation of the potential barrier of the DLVO interaction energy between PSNPs - kaolinite particles with pH; Figure 5 (c) is the continuous variation of the potential barrier of the DLVO interaction energy between PSNPs - illite particles with pH; Figure 5 (d) is the continuous variation of the potential barrier of the DLVO interaction energy between PSNPs - montmorillonite particles with pH.

[0052] Figure 6 (a) is the continuous variation of the potential barrier of the DLVO interaction energy between PSNPs - PSNPs with pH; Figure 6 (b) is the one-dimensional curve of the interaction energy between PSNPs - PSNPs varying with the spacing h at pH = 7.

[0053] Figure 7For the determination coefficient R of the fitting relationship between the comprehensive DLVO barrier and the mass recovery rate (R RE ) of nano-plastic particles, 2 with the quantitative change of the DLVO barrier proportionality coefficient (F PP ) between nano-plastic particles.

[0054] Figure 8 For the migration parameters of nano-plastic particles (PSNPs), the mass recovery rate (R RE ) and the relative effluent concentration (P C / C0 ) with the change of the comprehensive DLVO energy barrier; among them, Figure 8 (a) shows the change of the adsorption coefficient (K1) of PSNPs at site 1 with the comprehensive DLVO energy barrier; Figure 8 (b) shows the change of the desorption coefficient (K 1d ) of PSNPs at site 1 with the comprehensive DLVO energy barrier; Figure 8 (c) shows the change of the adsorption coefficient (K2) of PSNPs at site 2 with the comprehensive DLVO energy barrier; Figure 8 (d) shows the change of the adsorption concentration (S max2 ) of PSNPs at site 2 with the comprehensive DLVO energy barrier; Figure 8 (e) shows the change of the mass recovery rate (R RE ) of PSNPs with the comprehensive DLVO energy barrier; Figure 8 (f) shows the change of the relative effluent concentration (P C / C0 ) of PSNPs with the comprehensive DLVO energy barrier.

[0055] Figure 9 For the migration parameters of nano-plastic particles (PSNPs), the mass recovery rate (R RE ) and the relative effluent concentration (P C / C0 ) with the change of pH and the prediction of K1 through the DLVO two-dimensional surface; among them, Figure 9 (a) shows the experiment of the change of the adsorption coefficient (K1) of PSNPs at site 1 with pH and the prediction of K1 through the DLVO two-dimensional surface; Figure 9 (b) shows the experiment of the change of the desorption coefficient (K 1d ) of PSNPs at site 1 with pH and the prediction of K 1d through the DLVO two-dimensional surface; Figure 9 (c) shows the experiment of the change of the adsorption coefficient (K2) of PSNPs at site 2 with pH and the prediction of K2 through the DLVO two-dimensional surface; Figure 9 (d) shows the experiment of the change of the adsorption concentration (S max2 ) of PSNPs at site 2 with pH and the prediction of S max2 through the DLVO two-dimensional surface;Figure 9 (e) Experimental results of the mass recovery rate (R RE ) of PSNPs with respect to pH and prediction of R RE through the DLVO two-dimensional surface; Figure 9 (f) Experimental results of the relative effluent concentration (P C / C0 ) of PSNPs with respect to pH and prediction of P C / C0 through the DLVO two-dimensional surface. Detailed implementation manners

[0056] The present invention will be further described below in conjunction with the accompanying drawings of the specification and specific embodiments, but the embodiments do not limit the present invention in any form. Unless otherwise specified, the reagents, methods, and equipment used in the present invention are conventional reagents, methods, and equipment in the technical field.

[0057] In addition, unless otherwise specified, the reagents and materials used in the following embodiments are all commercially available.

[0058] Example 1

[0059] A method for quantitatively determining the migration and diffusion ability of nano-polystyrene particles (PSNPs) in a quartz sand-clay mineral mixed porous medium and the continuous variation of the two-point kinetic migration parameters with the environmental pH is as follows:

[0060] According to the method described in the content of the invention, nano-polystyrene particles (PSNPs) are used as the nano-plastic particles to be studied, kaolinite (KL), montmorillonite (MT), and illite (IL) are used as the clay minerals to be studied, and quartz sand (QS) is mixed with a certain mass of clay minerals as the mixed porous medium. The surface morphologies of quartz sand and clay minerals are observed by scanning electron microscopy (SEM).

[0061] As Figure 1 shown, the scanning electron micrographs of quartz sand and clay mineral particles are presented. It can be seen that quartz sand is an irregular particle with a relatively flat surface, while the three clay minerals are all irregularly shaped particles with a relatively rough surface.

[0062] (1) Using a Malvern nanoparticle size and zeta potential analyzer, the Zeta potential of quartz sand, clay minerals, and PSNPs under different pH conditions is measured by laser Doppler microelectrophoresis, and the hydrodynamic diameter of PSNPs under different conditions is measured by dynamic light scattering; the concentration C0 of the nanoplastics before passing through the mixed porous medium and the concentration C of the nanoplastics after passing through the mixed porous medium are measured. Regression analysis is performed on the measurement results of the Zeta potential and the hydrodynamic diameter of PSNPs. As Figure 2 shown, the mathematical relationship Ψ of the Zeta potential of the nanoplastics varying with pH is obtained by regression analysis fitting p(C pH ) to C pH , the mathematical relationship R of the hydrodynamic diameter of the nanoplastics particles varying with pH p (C pH ) to C pH , the mathematical relationship Ψ of the Zeta potential of each component of the mixed porous medium varying with pH c (C pH ) to C pH .

[0063] (2) Input the fitting relationships of the Zeta potential of quartz sand, clay minerals and PSNPs, and the hydrodynamic diameter of PSNPs and pH into the DLVO interaction energy calculation model, as Figure 3 shown, and calculate the two-dimensional distribution surface of the DLVO interaction energy between PSNPs and quartz sand and clay minerals. The calculation method of the two-dimensional distribution surface of the DLVO interaction energy is as follows:

[0064] φ tot (C pH , h) = φ vdw (C pH , h) + φ edl (C pH , h) + φ Born (C pH , h)

[0065]

[0066]

[0067]

[0068]

[0069]

[0070] Among them, h represents the distance (m) between the nanoplastics particles and the mixed porous medium; φ tot (C pH , h) is the two-dimensional distribution of the total interaction energy varying with pH and h; φ vdw (C pH , h) is the van der Waals gravitational potential energy; φ edl (C pH , h) is the double-layer repulsive potential energy; φ Born (C pH , h) is the Born repulsive potential energy; C pH is the solution pH; ε0 is the vacuum permittivity (8.845×10 -12 F·m -1 ); ε ris the relative permittivity of water (78.5 F·m at 20 °C); R -1 ); R p (C pH ) is the hydrodynamic diameter of the nanoplastics particles (m); Ψ p (C pH ) is the Zeta potential of the nanoplastics particles (V); Ψ c (C pH ) is the Zeta potential of each component of the mixed porous medium (V); k B is the Boltzmann constant (1.38×10 -23 J·K -1 ); T is the temperature (298.15 K); e is the elementary charge of an electron (C); κ is the reciprocal of the Debye length; N A is the Avogadro constant; λ is the characteristic wavelength, generally taken as 10 -7 m; A 123 is the Hamaker constant; A 11 , A 22 and A 33 represent the Hamaker constants of the nanoplastics particles, water, and each component of the mixed porous medium, respectively; σ Bron is the Bron collision parameter.

[0071] Among them, during the calculation, the Hamaker constant of PSNPs is 6.6×10 -20 J, the Hamaker constant of water is 3.70×10 -20 J, the Hamaker constant of quartz sand is 6.50×10 -20 J, and the Hamaker constants of kaolinite, illite, and montmorillonite are 9.63×10 -20 J, 5.62×10 -20 J, and 7.81×10 -20 J, respectively.

[0072] As Figure 4 shown, Figure 4 is the DLVO one-dimensional curve at pH = 7 intercepted from the two-dimensional distribution surface of the DLVO interaction energy. It can be seen from the figure that there is a potential barrier on the curve.

[0073] The potential barriers of the DLVO interaction energy are identified in the range of pH 2 to 11. As Figure 5 and Figure 6 (a) shown, the continuous variation of the potential barriers with pH among PSNPs - quartz sand, PSNPs - kaolinite, PSNPs - montmorillonite, PSNPs - illite, and PSNPs - PSNPs is obtained. From Figure 5It can be seen that the barriers of PSNPs - quartz sand, PSNPs - kaolinite, PSNPs - montmorillonite, and PSNPs - illite decrease with the decrease of pH. When the pH decreases to a certain value, the barrier value drops to the minimum and no longer changes. At this time, the deposition of PSNPs on the surface of quartz sand or clay minerals reaches the strongest. From Figure 6 (a) and Figure 6 (b), it can be seen that the DLVO barrier between PSNPs decreases with the increase of pH. When the pH increases to a certain value, the DLVO barrier between PSNPs drops to the minimum.

[0074] (3) The kinetic parameters (K1, K 1d , K2, S max2 ) related to the transport of PSNPs under different pH conditions were obtained by fitting with the double - kinetic - site retention model, and the mass recovery rate (R RE ) and the normalized effluent concentration (P C / C0 ) of PSNPs under different pH conditions were calculated.

[0075] The calculation methods of the transport parameters K1, K 1d , K2, S max2 of nanoplastics under different pH conditions are as follows:

[0076]

[0077] where θ is the porosity of the porous medium (dimensionless); C is the liquid - phase concentration of nanoplastics (mg·L -1 ); t is the time (T); ρb is the bulk density of the porous medium (g·m -3 ); x is the spatial ordinate (cm);

[0078] The double - kinetic - site retention model divides the surface sites of the mixed porous medium into two types: reversible retention sites and irreversible retention sites. It is assumed that the retention of nanoplastics on site 1 is reversible retention and the retention on site 2 is irreversible retention. The specific equation is:

[0079]

[0080]

[0081]

[0082] where S1 is the solid - phase deposition concentration of nanoplastics on site 1, S2 is the solid - phase deposition concentration of nanoplastics on site 2; k1 and k2 are the adsorption coefficients of nanoplastics on site 1 and site 2 respectively (min -1 ); k 1dis the desorption coefficient of the nanoplastics particles at site 1 (min -1 ); Ψ L represents the retention parameter related to time, and S max2 is the maximum solid-phase adsorption concentration of the nanoplastics particles at site 2 (g·g -1 ).

[0083] Among them, the mass recovery rate R of the nanoplastics particles under different pH conditions RE is:[[]]

[0084]

[0085] Among them, S test is the area of the breakthrough curve obtained by the nanoplastics particles passing through the mixed porous medium; S tracer is the area of the breakthrough curve obtained by the tracer passing through the mixed porous medium.[[]]

[0086] Measure the concentration of the nanoplastics particles obtained after passing through the mixed porous medium, and then calculate the normalized effluent concentration P of the nanoplastics particles under different pH conditions C / C0 :[[]]

[0087]

[0088] Among them, C is the concentration of the nanoplastics particles measured after passing through the mixed porous medium; C0 is the measured concentration of the nanoplastics particles.[[]]

[0089] The final fitting results and calculation results are shown in Table 1.[[]]

[0090] Table 1[[]]

[0091]

[0092]

[0093] (4) Calculate the comprehensive DLVO barrier including the DLVO barrier between PSNPs-PSNPs and the DLVO barrier between PSNPs and the mixed porous medium:[[]]

[0094] C DB = DB PP × F PP + DB ID × (1 - F PP )

[0095] DB ID = DB QS × P QS + DB CM × P CM

[0096] Among them, CDB is the comprehensive DLVO barrier (kT); DB ID is the weighted DLVO barrier (kT) between nano - plastic particles and the mixed porous medium (when the mixed porous medium does not contain clay minerals, here the mixed porous medium is only quartz sand); DB PP is the DLVO barrier (kT) between nano - plastic particles; DB QS is the DLVO barrier (kT) between nano - plastic particles and quartz sand; DB CM is the DLVO barrier (kT) between nano - plastic particles and clay minerals; F PP is the DLVO barrier proportionality coefficient between nano - plastic particles, which needs to be searched and identified; P QS is the mass percentage (%) of quartz sand in the mixed porous medium; P CM is the mass percentage (%) of clay minerals in the mixed porous medium, P QS +P CM = 100%.

[0097] Based on the calculated comprehensive DLVO barrier, the fitting relationship between the comprehensive DLVO barrier and the mass recovery rate (R RE ) of PSNPs is obtained through regression analysis, and the determination coefficient R 2 is searched for its quantitative change relationship with the DLVO barrier proportionality coefficient (F PP ) between PSNPs, and the DLVO barrier proportionality coefficient between nano - plastic particles when R 2 is the largest is determined. The results are as Figure 7 , the DLVO barrier proportionality coefficient F PP = 0.15.

[0098] (5) According to the DLVO barrier proportionality coefficient F PP = 0.15 between nano - plastic particles, calculate the comprehensive DLVO barrier, and obtain the parameter fitting equations of the comprehensive DLVO barrier and the nano - plastic particle migration parameters K1, K 1d , K2, S max2 , R RE and P C / C0 through regression analysis. The obtained migration parameters K1, K 1d , K2, S max2 , R RE and P C / C0 related to the mobility of PSNPs are shown in Table 2.

[0099] As Figure 8 shown, the regression analysis results show that K1, K 1d , K2 and S max2All decreased linearly with the logarithm of the integrated DLVO energy barrier value. The mass recovery rate (R RE ) and the normalized effluent concentration (P C / C0 ) of PSNPs increased linearly with the logarithm of the integrated DLVO energy barrier value. The R 1d values of the fitting equations for K1, K max2 , K2, S RE , R C / C0 , and P 2 were 0.834, 0.732, 0.635, 0.768, 0.879, and 0.825 respectively, indicating a high correlation between K1, K 1d , K2, S max2 , R RE , and P C / C0 of PSNPs and the DLVO energy barrier.

[0100] As Figure 9 shown, the transport kinetic parameters (K1, K 1d , K2, S max2 , R RE , and P C / C0 ) of PSNPs in saturated quartz sand-clay mineral porous media under different pH conditions were predicted by integrating the calculation results of the DLVO barrier, and the prediction results were in agreement with the experimental results.

[0101] Table 2 Relationship equations between relevant migration parameters and the integrated DLVO barrier

[0102]

[0103] Example 2

[0104] The difference between this example and Example 1 is that the nanoplastics used are nano-polyvinyl chloride particles.

[0105] Example 3

[0106] The difference between this example and Example 1 is that the nanoplastics used are nano-polyethylene particles.

[0107] Example 4

[0108] The difference between this example and Example 1 is that the nanoplastics used are nano-polyethylene terephthalate particles.

[0109] As can be seen from the above Examples 1 to 4, the present invention provides a method for determining the continuous variation of the migration parameters of nanoplastics with pH. The method based on the two-dimensional surface of the DLVO interaction energy can not only clearly show the variation of the interaction energy between micro / nanoplastic particles and each component of the mixed medium with pH, but also proposes a new calculation method for the comprehensive DLVO barrier. By regression analysis, the fitting relationship between the comprehensive DLVO barrier and the mass recovery rate (R RE ) of nanoplastics particles is obtained, and the quantitative variation relationship of the determination coefficient R 2 with the proportionality coefficient of the DLVO barrier between nanoplastics particles is searched to determine the proportionality coefficient of the DLVO barrier between nanoplastics particles when R 2 is the largest. Finally, the two-point kinetic parameters (i.e., migration parameters K1, K 1d , K2, S max2 ) related to the migration of nanoplastics particles are determined to vary continuously and quantitatively with the pH in the environment, so as to better quantitatively predict the migration ability of nanoplastics particles in the mixed porous medium.

[0110] The foregoing examples are merely illustrative and are used to explain some features of the method of the present invention. The appended claims are intended to claim the broadest scope conceivable, and the examples presented herein are supported by the applicant's actual test results. Therefore, the applicant's intention is that the appended claims are not limited by the selection of examples that illustrate the features of the present invention. Some of the numerical ranges used in the claims also include sub-ranges within them, and variations within these ranges should also be interpreted as being covered by the appended claims whenever possible.

Claims

1. A method for determining the continuous variation of migration parameters of nanoplastics particles with pH, characterized in that, It includes the following steps: S1. Measure the Zeta potential of the nanoplastics particles and each component of the mixed porous medium under different pH conditions; measure the hydrodynamic diameter of the nanoplastics particles under different pH conditions; obtain the mathematical relationships between the Zeta potential of the nanoplastics particles and each component of the mixed porous medium and the variation of the hydrodynamic diameter of the nanoplastics particles with pH; Measure and obtain the concentration C0 of the nanoplastics particles before passing through the mixed porous medium and the concentration C of the nanoplastics particles after passing through the mixed porous medium; S2. Calculate the two-dimensional surface of the DLVO interaction energy between the nanoplastics particles and each component of the mixed porous medium varying with pH according to the mathematical relationships obtained in step S1; S3. Fit the double kinetic site retention model to obtain the migration parameters K1, K 1d , K2, S max2 ; and calculate the mass recovery rate R RE of nanoplastics and the normalized effluent concentration P C / C0 under different pH conditions; S4. Calculate the comprehensive DLVO barrier. Based on the calculated comprehensive DLVO barrier, obtain the fitting relationship between the comprehensive DLVO barrier and the mass recovery rate R of the nanoplastics through regression analysis; search for its determination coefficient R RE ; search for the quantitative variation relationship of R 2 with the DLVO barrier proportionality coefficient between the nanoplastics, and determine the DLVO barrier proportionality coefficient between the nanoplastics when R 2 is the largest; S5. Calculate the comprehensive DLVO barrier according to the DLVO barrier proportionality coefficient between nanoplastics when R is at its maximum, and obtain the parameter fitting equations of the comprehensive DLVO barrier with the nanoplastics migration parameters K1, K 2 , K2, S 1d , max2 , R RE , and P C / C0 through regression analysis. Calculate the migration parameters K1, K 1d , K2, S max2 , R RE , and P C / C0 for their continuous changes with pH.

2. The method according to claim 1, wherein In step S1, use a Malvern nanoparticle sizer and zeta potential analyzer to measure the Zeta potential of the nanoplastics particles and each component of the mixed porous medium under different pH conditions by laser Doppler microelectrophoresis; use dynamic light scattering to measure the hydrodynamic diameter of the nanoplastics particles under different pH conditions.

3. The method according to claim 1, wherein In the step S1, the mathematical relationship Ψ of the Zeta potential of the nanoplastics varying with pH is obtained by regression analysis fitting p (C pH )~C pH , the mathematical relationship R of the hydrodynamic diameter of the nanoplastics varying with pH p (C pH )~C pH , and the mathematical relationship Ψ of the Zeta potential of each component of the mixed porous medium varying with pH c (C pH )~C pH .

4. The method according to claim 3, wherein In step S2, the two-dimensional surface of the DLVO interaction energy is: φ tot (C pH , h) = φ vdw (C pH , h) + φ edl (C pH , h) + φ Born (C pH , h) where, h represents the distance between the nanoplastics particles and the mixed porous medium; φ tot (C pH , h) is the two-dimensional distribution of the total interaction energy with respect to pH and h; φ vdw (C pH , h) is the van der Waals gravitational potential energy; φ edl (C pH , h) is the double-layer repulsive potential energy; φ Born (C pH , h) is the Born repulsive potential energy; C pH is the solution pH; ε0 is the vacuum permittivity; ε r is the relative permittivity of water; R p (C pH ) is the hydrodynamic diameter of the nanoplastics particles; Ψ p (C pH ) is the Zeta potential of the nanoplastics particles; Ψ c (C pH ) is the Zeta potential of each component of the mixed porous medium; k B is the Boltzmann constant; T is the temperature; e is the elementary charge of an electron; κ is the reciprocal of the Debye length; N A is the Avogadro constant; λ is the characteristic wavelength; A 123 is the Hamaker constant; A 11 , A 22 and A 33 represent the Hamaker constants of the nanoplastics particles, water, and each component of the mixed porous medium, respectively; σ Bron is the Bron collision parameter.

5. The method according to claim 1, wherein In the step S3, the migration parameters K1, K 1d , K2, S max2 are as follows: where θ is the porosity of the porous medium; C is the liquid-phase concentration of the nanoplastics; t is the time; ρb is the bulk density of the porous medium; x is the spatial ordinate; The double kinetic site retention model divides the surface sites of the mixed porous medium into two types: reversible retention sites and irreversible retention sites. Assume that the retention of the nanoplastics particles at site 1 is reversible retention and the retention at site 2 is irreversible retention. Its specific equation is: Among them, S1 is the solid-phase deposition concentration of nano-plastic particles at site 1, and S2 is the solid-phase deposition concentration of nano-plastic particles at site 2; k1 and k2 are the adsorption coefficients of nano-plastic particles at site 1 and site 2 respectively; k 1d is the desorption coefficient of nano-plastic particles at site 1; Ψ L represents the retention parameter related to time, S max2 is the maximum solid-phase adsorption concentration of nano-plastic particles at site 2.

6. The method according to claim 1, wherein In the step S3, the mass recovery rate R of the nanoplastics under different pH conditions RE is as follows: Among them, S test is the area of the breakthrough curve obtained by the nano-plastic particles passing through the mixed porous medium; S tracer is the area of the breakthrough curve obtained by the tracer passing through the mixed porous medium.

7. The method according to claim 1, wherein In the step S3, the standardized effluent concentration P of the nanoplastics particles is further calculated under different pH conditions C / C0 : where C is the concentration measured after the nanoplastics particles pass through the mixed porous medium; C0 is the concentration measured of the nanoplastics particles.

8. The method according to claim 1, wherein In step S4, the comprehensive DLVO barrier is: C DB = DB PP × F PP + DB ID × (1 - F PP ) DB ID = DB QS × P QS + DB CM × P CM Among them, C DB is the comprehensive DLVO barrier; DB ID is the weighted DLVO barrier between the nanoplastics particles and the mixed porous medium; DB PP is the DLVO barrier between the nanoplastics particles; DB QS is the DLVO barrier between the nanoplastics particles and the quartz sand; DB CM is the DLVO barrier between the nanoplastics particles and the clay minerals; F PP is the proportionality coefficient of the DLVO barrier between the nanoplastics particles, which needs to be searched and identified; P QS is the mass percentage of the quartz sand in the mixed porous medium; P CM is the mass percentage of the clay minerals in the mixed porous medium, P QS +P CM = 100%.

9. According to the method described in any one of claims 1 to 8, characterized in that, The nanoplastics particles are nanoparticle polystyrene particles, nanoparticle polyethylene particles, nanoparticle polyvinyl chloride particles or nanoparticle polyethylene terephthalate particles.

10. According to the method described in any one of claims 1 to 8, characterized in that, The mixed porous medium is quartz sand or a mixture of quartz sand and clay minerals; the clay minerals include one or more of kaolinite, montmorillonite and illite.

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