A method for resolving a source of groundwater sulfate

Abstract

The present application belongs to the technical field of groundwater pollution source tracing, and particularly relates to a method for analyzing the source of groundwater sulfate. The method comprises collecting samples and determining water chemical parameters, stable isotope parameters and dissolved organic matter characteristic parameters, using a parallel factor analysis model or a fluorescence regional integration method to analyze fluorescent components, inputting the characteristic parameters as continuous covariates and stable isotopes into a Bayesian mixed model, establishing a dynamic constraint relationship and running the model to obtain the contribution rate of different sulfate sources. The method solves the problems of end member overlap, static analysis and not considering the dynamic evolution of organic matter in the existing technology, and improves the identification accuracy and dynamic analysis capability of sulfate sources in complex contaminated sites.

Description

Technical Field

[0001] This invention belongs to the field of groundwater pollution source tracing technology, specifically relating to a method for analyzing the source of sulfate in groundwater. Background Technology

[0002] Sulfates in groundwater ( Abnormally high sulfate concentrations are a significant environmental issue of widespread concern in the field of groundwater environmental management. Sulfate sources are complex, including natural geological sources such as evaporites or gypsum, as well as anthropogenic sources such as landfill leachate, agricultural fertilizers, mine drainage, and industrial wastewater. When sulfate concentrations in groundwater are excessively high, they not only affect groundwater quality safety but also alter the redox environment of the groundwater system, promoting the migration and release of heavy metals such as Fe, Mn, and As, thus leading to complex groundwater pollution.

[0003] Currently, the identification of sulfate sources in groundwater mainly employs methods such as hydrochemical analysis, ion ratio analysis, sulfur-oxygen stable isotope tracing, and Bayesian mixture models. Among these, sulfate sulfur stable isotopes ( ) and sulfate oxygen stable isotopes ( Because it can reflect the characteristic information of different sulfate sources, it has been widely used in groundwater sulfate source apportionment. Typically, by establishing sulfur and oxygen dual isotope characteristic ranges and combining them with Bayesian mixture models such as MixSIAR, the contribution rate of sulfate from different sources in groundwater is quantitatively calculated. However, this type of method mainly relies on stable sulfur and oxygen isotopes and hydrochemical parameters for source apportionment, and its source contribution rate calculation is essentially a static mixture model analysis. In complex groundwater environments, the isotopic characteristic ranges of sulfate from different sources are prone to overlap. Simultaneously, biogeochemical processes such as sulfate reduction, microbial metabolism, water-rock interaction, and redox reactions are prevalent in groundwater systems, leading to dynamic changes in sulfur and oxygen isotope characteristics. Furthermore, existing technologies do not consider the regulatory role of dissolved organic matter in groundwater on sulfate migration and transformation processes, and cannot reflect the influence of organic matter humification, microbial activity, and organic matter evolution on sulfate source apportionment results. Therefore, in complex contaminated sites, problems such as insufficient accuracy in sulfate source apportionment, large contribution rate errors, and weak ability to identify dynamic change characteristics easily arise.

[0004] Meanwhile, three-dimensional excitation-emission matrix fluorescence spectroscopy (EEM) and parallel factor analysis (PARAFAC) techniques have been widely applied to the identification and evolution analysis of dissolved organic matter (DOM) sources in groundwater. By calculating parameters such as fluorescence index (FI), biological index (BIX), and humification index (HIX), the degree of humification, microbial activity, and source characteristics of DOM can be characterized. Existing techniques show that, in addition to characterizing groundwater pollution, DOM also participates in sulfate reduction, redox transformation, and microbial metabolic processes in groundwater systems. Therefore, DOM transformation processes can affect sulfate migration and transformation processes in groundwater. However, existing groundwater DOM source identification methods based on three-dimensional fluorescence spectroscopy mainly analyze the composition and source of DOM, without establishing a quantitative coupling relationship between DOM fluorescence parameters and sulfate isotope source apportionment. These methods typically only use DOM as a pollution characteristic identification indicator, failing to quantitatively calculate the contribution rate of sulfate from different sources, and also failing to reflect the dynamic impact of DOM participation in sulfate redox transformation and microbial metabolic processes on sulfate source apportionment results. Therefore, it is difficult to achieve dynamic quantitative analysis of sulfate sources in complex groundwater systems.

[0005] In summary, most existing groundwater sulfate source apportionment methods rely solely on independent analyses using sulfur and oxygen isotopes, hydrochemical parameters, or DOM fluorescence characteristics. DOM is typically used only as an auxiliary interpretative indicator, without establishing a dynamic coupling relationship between DOM characteristic parameters and sulfate isotopes. In complex contaminated sites, the isotopic characteristic ranges of sulfates from different sources overlap, and groundwater involves complex processes such as microbial activity, redox reactions, and DOM transformation. Relying solely on traditional sulfur and oxygen isotope mixing models easily leads to significant uncertainties in source apportionment results, making it difficult to accurately reflect the dynamic changes in groundwater sulfate sources. Therefore, there is an urgent need to develop an apportionment method that can simultaneously couple DOM characteristic parameters with stable isotopic information and dynamically constrain changes in the contribution rate of groundwater sulfate sources, thereby improving the accuracy and dynamic analysis capabilities of groundwater sulfate source identification in complex contaminated sites. Summary of the Invention

[0006] To address the aforementioned problems, this application discloses a method for analyzing the source of sulfate in groundwater, comprising the following steps:

[0007] Collect groundwater samples and potential sulfate source endmember samples from the target area;

[0008] Determine the stable isotope parameters in the sample;

[0009] The characteristic parameters of dissolved organic matter in the sample were determined, and the three-dimensional fluorescence spectral data were analyzed using a parallel factor analysis model or fluorescence region integration method to obtain the fluorescent components and calculate the characteristic parameters.

[0010] The characteristic parameters are used as continuous covariates and input together with the stable isotope parameters into the Bayesian mixture model to establish a Bayesian mixture model constrained by soluble organic matter covariates.

[0011] The model was run to obtain the contribution rate of different sulfate sources and to determine the main source of sulfate in groundwater.

[0012] Furthermore, the characteristic parameters of the dissolved organic matter include the humification index HIX, the biological index BIX, the fluorescence index FI, the proportion of fluorescent components in parallel factor analysis, the fluorescence region integral parameter, and the 254 nm ultraviolet absorption ratio. Dissolved organic carbon concentration One or more of the following: ratio, humic peak intensity, and protein peak intensity.

[0013] Furthermore, the stable isotope parameters include sulfate sulfur stable isotopes. and sulfate oxygen stable isotopes .

[0014] Furthermore, the stable isotope parameters also include stable hydrogen isotopes from groundwater. Stable isotopes of oxygen in groundwater Nitrate nitrogen stable isotopes Nitrate oxygen stable isotopes Dissolving inorganic carbon stabilizes isotopes Strontium isotopes and boron isotopes One or more of them.

[0015] Furthermore, the Bayesian mixture model is a MixSIAR model, and iterative calculations are performed using the Markov chain Monte Carlo method.

[0016] Furthermore, the potential sulfate source end-members include atmospheric deposition sources, evaporite or gypsum dissolution sources, agricultural activity sources, landfill leachate sources, and industrial or domestic wastewater sources.

[0017] Furthermore, after measuring the three-dimensional fluorescence spectrum, the data is preprocessed, including blank subtraction, scattering correction, internal filtering effect correction, and fluorescence intensity normalization.

[0018] Furthermore, the determination of the sample also includes testing the water chemical parameters of the sample, which include: , , , , , , , , , .

[0019] Furthermore, before using the characteristic parameters as continuous covariates, a response relationship between the characteristic parameters and the sulfate concentration and stable isotope parameters of the sample is established to identify the characteristics of soluble organic matter corresponding to different sulfate sources.

[0020] Furthermore, the method is applicable to complex groundwater contaminated sites with mixed sulfates from multiple sources, including landfills, mining areas, agricultural areas, industrial parks, oil and gas extraction areas, karst areas, riverbanks, or tailings ponds.

[0021] Compared with existing methods for analyzing the sources of sulfate in groundwater, this invention has the following advantages:

[0022] (1) This invention couples the characteristic parameters of dissolved organic matter with the parameters of stable isotopes and introduces the characteristic parameters of dissolved organic matter as a continuous covariate into the stable isotope Bayesian mixture model, which can simultaneously reflect the dynamic response relationship between the evolution process of organic matter in groundwater and the change of sulfate source, thereby improving the accuracy of sulfate source analysis in complex groundwater systems.

[0023] (2) By introducing characteristic parameters of soluble organic matter, the present invention dynamically constrains the traditional stable isotope mixing model, which can effectively reduce the analytical error caused by the overlap of end-member isotope characteristics of different sulfate sources and improve the ability to distinguish sulfates from multiple sources such as atmospheric deposition, evaporite dissolution, agricultural activities and landfill leachate.

[0024] (3) This invention utilizes the degree of humification of dissolved organic matter, the characteristics of microbial sources and the differences in fluorescent components to establish a coupling relationship between the evolution of organic matter in groundwater and the migration and transformation of sulfate. It can reflect the influence of biogeochemical processes such as sulfate redox reaction, microbial metabolism and water-rock interaction in groundwater on the contribution rate of sulfate sources.

[0025] (4) The present invention adopts a Bayesian mixture model with covariate constraints of dissolved organic matter, which transforms the contribution rate of sulfate source from the traditional static analysis method to a dynamic analysis method, and can realize the dynamic change analysis of the contribution rate of sulfate source under different seasons, different regions and different groundwater environment conditions.

[0026] (5) The present invention can simultaneously output the contribution rate of different sulfate sources in groundwater and the response results of organic matter evolution characteristics, and can realize the identification of groundwater pollution sources, analysis of groundwater pollution plume migration and analysis of groundwater pollution causes, providing technical support for groundwater pollution prevention and control and pollution remediation.

[0027] (6) The method of the present invention combines water chemical parameters, stable isotope parameters and dissolved organic matter characteristic parameters to construct a multi-parameter coupled analysis system. Compared with the single isotope method or the single organic matter characteristic method, it has stronger applicability to complex pollution scenarios and dynamic identification capability of groundwater pollution sources.

[0028] (7) The method and steps of the present invention are clear, the data acquisition method is mature, and it can be realized based on existing groundwater quality monitoring, stable isotope testing and three-dimensional fluorescence analysis equipment. It has good engineering applicability and promotion value. Attached Figure Description

[0029] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0030] Figure 1 This is a flowchart of the parsing method for this application;

[0031] Figure 2 a and b represent Piper plots showing the hydrochemical characteristics of water bodies during the high and low water periods, respectively.

[0032] Figure 3 a and b represent the sulfate content in water bodies during the high and low water periods, respectively. and Biisotope diagram;

[0033] Figure 4 These are the six DOM fluorescent components Component1-6 as determined by the PARAFAC model;

[0034] Figure 5 a and b represent the correlation between fluorescence characteristics and sulfate characteristics during the wet and dry seasons, respectively.

[0035] Figure 6 a and b represent the contribution rates of sulfate sources in different water bodies during the wet and dry seasons, respectively. 6c represents the source data obtained after modifying the traditional isotope mixing model by introducing the DOM humification index HIX as a covariate during the wet season. Detailed Implementation

[0036] To make the technical problems solved, the technical solutions, and the beneficial effects of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0037] This invention provides a method for dynamic source apportionment of sulfate in groundwater by coupling characteristic parameters of dissolved organic matter (DOM) with stable isotope parameters. This method simultaneously acquires groundwater hydrochemical parameters, stable sulfate isotope parameters, and DOM characteristic parameters, and introduces the DOM characteristic parameters as continuous covariates into a stable isotope Bayesian mixture model. This establishes a dynamic constraint relationship between organic matter evolution characteristics and the contribution rate of sulfate sources, thereby achieving quantitative identification and dynamic analysis of sulfate sources in complex groundwater systems.

[0038] like Figure 1 As shown, the technical route of this invention includes: determining the study area and potential sulfate sources → collecting groundwater samples and source end-member samples → measuring water chemical parameters → measuring stable sulfate isotopes → obtaining and analyzing DOM three-dimensional fluorescence spectra → establishing the response relationship between DOM and sulfate characteristics → constructing a Bayesian mixture model with DOM covariate constraints → running the model and outputting the source contribution rate → determining the main sources and pollution causes.

[0039] Step 1: Collect groundwater samples and potential sulfate source endmember samples from the target area.

[0040] Based on the hydrogeological conditions, groundwater flow direction, pollution source distribution, land use type, and stratigraphic lithology of the study area, potential source end-members of groundwater sulfate are identified. These potential sulfate source end-members include, but are not limited to: atmospheric deposition, evaporite or gypsum dissolution, agricultural activities, landfill leachate, and industrial or domestic wastewater. In a preferred embodiment, when the study area is within a landfill impact zone, atmospheric deposition, evaporite dissolution, agricultural activities, and landfill leachate are considered as sulfate source end-members.

[0041] Groundwater sampling points were established within the study area, including background points, points near pollution sources, points downstream of groundwater runoff, and sensitive receptor points that may be affected by pollution. Collected samples included source end-member samples such as groundwater, surface water, landfill leachate, atmospheric precipitation, agricultural drainage or fertilizer leachate, and sulfate-containing mineral dissolution simulation solutions.

[0042] During sampling, pH, oxidation-reduction potential (ORP), conductivity (EC), dissolved oxygen (DO), temperature, and total dissolved solids (TDS) were measured on-site. After collection, samples were filtered through a 0.45 μm filter and aliquoted according to different testing purposes. Samples for sulfate isotope testing were adjusted to pH < 2 with hydrochloric acid; samples for DOM three-dimensional fluorescence testing were stored in the dark and at low temperatures; and samples for water chemistry testing were stored separately according to the requirements for anion and cation testing.

[0043] Step 2: Determine the stable isotope parameters in the sample.

[0044] Stable isotope analysis was performed on sulfate samples and source end-member samples from groundwater. In a preferred embodiment, the stable isotope of sulfate sulfur was determined (…). ) and sulfate oxygen stable isotopes ( The specific testing procedure includes: adding [a certain substance] to the water sample. Solution, making form Precipitation; for The precipitate was filtered, washed, and dried; its determination was performed using stable isotope ratio mass spectrometry (IRMS). In and Value. Among them, The results are expressed using V-CDT as the standard. The results are expressed in V-SMOW format.

[0045] In other embodiments, one or more of the following stable isotope parameters may also be used or combined: groundwater hydrogen stable isotope ( ), groundwater oxygen stable isotopes ( ), Nitrate nitrogen stable isotopes ( ), nitrate oxygen stable isotopes ( ), dissolved inorganic carbon stable isotopes ( ), Strontium isotopes ( ) and boron isotopes ( The aforementioned isotopic parameters can reflect the sources of groundwater recharge, water-rock interactions, agricultural activities, and characteristics of anthropogenic pollution, and can further enhance the ability to identify pollution sources in complex groundwater systems.

[0046] like Figure 3 As shown, sulfate levels vary depending on the source of the water. and Isotopic composition; such as Figure 4 As shown, sulfate levels in different water bodies The dual isotope plots show different distribution ranges. This step utilizes the differential isotopic characteristics of sulfates from different sources to provide endmember input values ​​for the Bayesian mixture model.

[0047] Step 3: Determine the characteristic parameters of dissolved organic matter in the sample, and use a parallel factor analysis model or fluorescence region integration method to analyze the three-dimensional fluorescence spectrum data, obtain the fluorescent components, and calculate the characteristic parameters.

[0048] Three-dimensional fluorescence spectroscopy (Excitation-Emission Matrix, EEM) was performed on groundwater samples and source end-member samples. Three-dimensional fluorescence spectroscopy is a testing method that obtains DOM fluorescence characteristic information by simultaneously varying the excitation and emission wavelengths. In one embodiment, the excitation wavelength range was 250–500 nm, the emission wavelength range was 250–550 nm, and the scanning interval was 5 nm.

[0049] After obtaining the raw EEM data, it is preprocessed, including blank subtraction, Rayleigh scattering correction, Raman scattering correction, internal filtration effect correction, and fluorescence intensity normalization. Rayleigh scattering correction is used to eliminate scattering interference when the excitation wavelength is equal to or a multiple of the emission wavelength; Raman scattering correction is used to eliminate interference from water molecule Raman scattering on the fluorescence signal; and internal filtration effect correction is used to eliminate fluorescence quenching caused by light absorption in high-concentration samples.

[0050] Based on the preprocessed three-dimensional fluorescence data, the DOM fluorescence components were analyzed using the Parallel Factor Analysis (PARAFAC) model or the Fluorescence Region Integration (FRI) method. The PARAFAC model is a mathematical analysis method based on trilinear decomposition, capable of decomposing complex EEM data into multiple independent fluorescence components and their corresponding scores (maximum fluorescence intensity) and loadings (excitation / emission spectra). Figure 6 As shown, the PARAFAC model can be used to analyze humic components (such as C1), fulvic acid components (such as C2), protein components (such as C3), and microbial metabolic byproduct components (such as C4).

[0051] Further calculation of DOM characteristic parameters, including but not limited to: humification index (HIX), biological index (BIX), fluorescence index (FI), PARAFAC fluorescent component ratio, fluorescence region integral (FRI) parameter, and 254 nm UV absorbance ratio (…). ), Dissolved organic carbon (DOC) concentration, One or more of the following: ratio, humic peak intensity, and protein peak intensity.

[0052] Among them, HIX is used to characterize the degree of humification of DOM, and is calculated as the ratio of the integral value of fluorescence intensity in the emission wavelength range of 435-480 nm to 300-345 nm; BIX is used to characterize the neon and microbial origin characteristics of DOM, and is calculated as the ratio of fluorescence intensity at emission wavelength of 380 nm to 430 nm; FI is used to characterize the origin attribute of DOM, and is calculated as the ratio of fluorescence intensity at emission wavelength of 470 nm to 520 nm.

[0053] In a preferred embodiment, before using the characteristic parameters as continuous covariates, a response relationship between the characteristic parameters and the sulfate concentration and stable isotope parameters of the sample is established to identify DOM fluorescence characteristics corresponding to different sulfate sources. For example, establishing the relationship between HIX and... Concentration and Establish the response relationship between BIX and Concentration and The response relationship between them. For example... Figure 5 As shown, when HIX and Concentration and It shows a positive correlation, while BIX and Concentration and When the correlation is negative, it indicates that groundwater sulfate is strongly affected by landfill leachate; when the proportion of protein-like components in groundwater DOM is high, and and When there is a significant correlation, it indicates that groundwater sulfate is strongly affected by the dissolution of evaporites or gypsum.

[0054] This step analyzes the fluorescent components of DOM using the PARAFAC model or the FRI method and calculates characteristic parameters such as HIX, BIX, and FI. It can quantitatively characterize the source attributes, humification degree, and microbial activity of DOM in groundwater, providing a parameter basis for establishing the dynamic coupling relationship between organic matter evolution and sulfate source.

[0055] Step 4: Input the feature parameters as continuous covariates and the stable isotope parameters into the Bayesian mixture model to establish a Bayesian mixture model constrained by soluble organic matter covariates.

[0056] A Bayesian mixture model for sulfate sources is constructed using stable isotope parameters from groundwater samples as isotope inputs for the mixture and stable isotope parameters from each potential sulfate source endmember as source endmember inputs. Unlike existing static mixture models, this invention introduces DOM feature parameters as continuous covariates into the Bayesian mixture model.

[0057] The continuous covariate refers to a parameter that changes continuously with the evolution of the groundwater environment. It establishes a response relationship with the source contribution rate through a continuous covariate function, so that the contribution rate of different sulfate sources is no longer set to a fixed value, but dynamically adjusted according to changes in DOM characteristic parameters. The model is expressed as: different source contribution rate = f(DOM characteristic parameters, stable isotope parameters). In this way, the sulfate source contribution rate is simultaneously constrained by both stable isotope endmember characteristics and DOM evolution characteristics.

[0058] In a preferred embodiment, the Bayesian mixture model is a MixSIAR model, which is iteratively calculated using the Markov Chain Monte Carlo (MCMC) method. The MixSIAR model is a stable isotope mixture model based on Bayesian theory, capable of calculating the contribution rates of different sources to the mixture and their posterior probability distributions. The MCMC method quantifies the uncertainty of source contribution rates by constructing a Markov chain and performing extensive random sampling to obtain the posterior distribution of model parameters.

[0059] In a preferred embodiment, HIX and BIX are used as continuous covariate parameters. HIX reflects the degree of DOM humification and is significantly associated with strong humic sources such as landfill leachate; BIX reflects DOM microbial activity and is closely related to the metabolic processes of microorganisms such as sulfate-reducing bacteria in the groundwater system. Therefore, introducing HIX and BIX as covariates into the model can effectively constrain the dynamic changes in sulfate isotopes caused by microbial activity and organic matter evolution.

[0060] This step, by introducing DOM feature parameters as continuous covariates into the Bayesian mixture model, realizes the transformation of sulfate source contribution rate from static analysis to dynamic analysis, effectively overcoming the shortcomings of traditional static models in terms of overlapping end-member isotope features and inability to reflect the dynamic changes of biogeochemical processes.

[0061] Step 5: Run the model to obtain the contribution rate of different sulfate sources and determine the main source of sulfate in groundwater.

[0062] The Markov chain Monte Carlo method was used to iteratively calculate the posterior probability distribution of the contribution rates of different sulfate sources in the established Bayesian mixture model, obtaining the results. The output includes: contribution rates of atmospheric deposition, evaporite or gypsum dissolution, agricultural activities, landfill leachate, and industrial or domestic wastewater; as well as seasonal variations in sulfate source contribution rates, spatial variations of sulfate sources at different sampling points, and dynamic constraints of DOM feature parameters on the sulfate source contribution rate.

[0063] Based on the obtained contribution rate results, the main source of sulfate in the target groundwater sample is determined. In one embodiment, when the contribution rate of landfill leachate is the highest, and the DOM (Dissolved Oxygen Species) shows high HIX, low BIX, and a high proportion of humic or fulvic acid-like components, it is determined that sulfate is mainly affected by landfill leachate. When the contribution rate of evaporite or gypsum dissolution is the highest, and... and When the correlation is significant and the proportion of protein-like components in the DOM is high, it is determined that sulfate is mainly controlled by geological sources. When the contribution rate of agricultural activities is high, and the sampling points are located in farmland or riparian areas, and the contribution rate increases during the rainy season, it is determined that sulfate is mainly affected by agricultural leaching. When the contribution rate of atmospheric deposition is high, and... When the concentration is low and the DOM fluorescence signal is weak, it is determined that sulfate is mainly affected by atmospheric deposition replenishment.

[0064] This step, through the probabilistic output of the Bayesian mixture model, not only achieves the quantitative calculation of the contribution rate of sulfate from different sources, but also provides the uncertainty range of the contribution rate; at the same time, combined with the constraints of DOM feature parameters, it achieves the accurate identification of the dynamic changes of sulfate sources in complex groundwater systems.

[0065] In a further embodiment, the method may also include measuring the water chemical parameters of the sample, which can be combined with stable isotope parameters and dissolved organic matter characteristic parameters to construct a multi-parameter coupled analytical system. Compared with the single isotope method or the single organic matter characteristic method, it has stronger applicability to complex pollution scenarios and dynamic identification capability of groundwater pollution sources.

[0066] The water chemical parameter test indicators include , , , , , , , , , .in, , , The determination was performed using ion chromatography. , , , , , The determination was performed using inductively coupled plasma optical emission spectrometry (ICP-OES). The determination was performed using titration.

[0067] Based on the test results, calculate the ion correlation and plot the Piper triline plot (e.g.) Figure 2 (As shown) This step determines the hydrochemical type and sulfate enrichment characteristics of groundwater. Through hydrochemical analysis, this step preliminarily identifies the degree of sulfate enrichment and hydrochemical evolution characteristics in groundwater, providing basic hydrochemical constraints for subsequent isotope and DOM coupling analysis.

[0068] The technical solution and effects of the present invention will be further illustrated below through specific embodiments.

[0069] Example 1: Dynamic analysis of the source of sulfate in groundwater in the affected area of ​​a landfill.

[0070] 1. Sampling.

[0071] Based on the hydrogeological survey, the potential source end-members of groundwater sulfate in the study area were identified as: atmospheric deposition, evaporite / gypsum dissolution, agricultural fertilizer leaching, and landfill leachate. During sampling, pH, ORP, EC, dissolved oxygen (DO), temperature, and TDS were measured on-site. After collection, samples were filtered through a 0.45 μm filter and aliquoted according to different testing purposes. Samples used for sulfate isotope testing were adjusted to pH < 2 with hydrochloric acid; samples used for DOM three-dimensional fluorescence testing were stored in the dark and at low temperatures; and samples used for water chemistry testing were stored separately according to the requirements for anion and cation testing.

[0072] 2. Test analysis results.

[0073] (1) Hydrochemical characteristics: Test indicators include: , , , , , , , , , .

[0074] in, , , The determination was performed using ion chromatography. , , , , , The determination was performed using inductively coupled plasma atomic emission spectrometry. The titration method was used for determination. Based on the test results, the ion correlation was calculated, and a Piper triline plot was plotted as follows. Figure 2 As shown, the hydrochemical type and sulfate enrichment characteristics of groundwater were determined: the results showed that the background point water chemical type was... The main hydrochemical types in the study area during the high-water season are: Type, accounting for 50%; dry season Type 38% mainly consisted of monitoring wells affected by landfills. Overall, the water cations in the study area were predominantly... Mainly, anions are the main components. , Mainly. , As the dominant factor controlling the evolution of water chemistry in the study area, landfill pollution led to a change in the groundwater type of the landfill monitoring wells. Type transformation type.

[0075] (2) Stable isotopic characteristics of sulfur and oxygen: Add sulfur to water sample Solution, making form Precipitation; for The precipitate was filtered, washed, and dried; and analyzed using a stable isotope mass spectrometer. In and Values; obtain sulfur and oxygen isotope characteristic values ​​of groundwater samples and each source endmember. Among them, The results are expressed using V-CDT as the standard. The results are expressed in V-SMOW standard, as follows: Figure 3 As shown: Samples from the study area and The characteristics show that its composition ranges from 0.84 to 24.27‰ and 3.99 to 13.40‰, respectively. Groundwater in the study area... There are four main sources: agricultural activities, atmospheric deposition, evaporite mineral dissolution, and landfill leachate leakage. Due to the influence of different pollution sources, different locations exhibit seasonal variations.

[0076] (3) DOM three-dimensional fluorescence characteristics: The excitation wavelength range was 250–500 nm, the emission wavelength range was 250–550 nm, and the scanning interval was 5 nm. After obtaining the raw EEM data, it was preprocessed, including blank subtraction; Rayleigh scattering correction; Raman scattering correction; internal filtration effect correction, and normalization. After normalization, the PARAFAC model was used to perform spectral analysis on the data of the study area. A total of 6 fluorescent components were extracted during the dry season, such as... Figure 4The figures show C1, C2, C3, C4, C5, and C6, respectively. C1-C5 are humic components, reflecting complex humification processes, while C6 is a protein-like component, indicating microbial metabolites or wastewater discharge. Visual analysis of the relative distribution of fluorescent components from different locations revealed significant differences in DOM components among water bodies from different sources. The DOM components in the leachate mainly consist of three types of humic substances: C1, C2, and C3, accounting for 53%, 33%, and 13%, respectively, representing the fluorescent fingerprint characteristics of this landfill. The fluorescent component structures and proportions of the water bodies from monitoring wells JC#4, JC#6, and SW, which share the same source and hydraulic connection, are similar. Compared to the leachate, JC#4 and SW water bodies produced a new humic component (C5). This may be because the strongly reducing environment of the landfill leachate inhibits the formation of this humic substance, while increased oxygen content in groundwater and surface water promotes its synthesis. The characteristic parameters such as HIX, BIX, and FI were calculated and obtained. Among them, HIX is used to characterize the degree of DOM humification, BIX is used to characterize the DOM nascentity and microbial origin characteristics, and FI is used to characterize the DOM origin attributes.

[0077] like Figure 5 As shown, the response relationship between DOM feature parameters and sulfate characteristics is established: HIX of groundwater samples from downstream contaminated plume and... Concentration and Significant positive correlation ( >0.75), BIX and Concentration and Significantly negative correlation ( The correlation was >0.68, while no such correlation was found at the upstream background point. This response relationship indicates that downstream groundwater sulfate enrichment, increased DOM humification, and decreased microbial activity exhibit a co-evolutionary pattern, suggesting that landfill leachate has a significant impact on downstream groundwater.

[0078] Model building and comparative analysis: such as Figure 6 As shown, the traditional static MixSIAR model (using only...) is used respectively. and As input) and the DOM covariate constraint MixSIAR model of the present invention (with , The contribution rate of sulfate source at the sampling points was calculated using HIX and BIX as isotopic inputs and continuous covariates.

[0079] Traditional source tracing results indicate that atmospheric deposition dominates during the high-water season, primarily relying on precipitation for atmospheric sulfate transport. Atmospheric deposition contributes the most to atmospheric sulfate transport during this period, exceeding 40% at multiple sites (e.g., M3, GB2), while contributions from landfill leachate, evaporite mineral dissolution, and agricultural activities are generally lower. This may be due to increased rainfall during the high-water season, leading to higher levels of sulfate in the atmosphere. - Large amounts of water enter the surface and groundwater systems through wet deposition, directly increasing its contribution rate, while other sources may be diluted, thus reducing their relative contribution rate.

[0080] During the dry season, the main sources of sulfate shift to landfill leachate and evaporite mineral dissolution. At some sites (JC#4, M4), the contribution rate of landfill leachate reached as high as 47%, and evaporite mineral dissolution reached 45%. This is likely due to reduced rainfall, decreased groundwater recharge, and lower water levels, making it easier for leachate from landfills or dumping grounds to infiltrate into aquifers without being diluted. Simultaneously, reduced rainfall decreases the scouring effect of surface runoff on leachate, leading to a relative enrichment of pollutants. Therefore, the increased proportion of landfill leachate during the dry season may be related to slower groundwater runoff and the cumulative effect of pollutants. Agricultural activities have the lowest contribution to groundwater sulfate sources in the study area, with no significant seasonal variation. The contribution rate of agricultural activities is low (mostly <25%) during both the wet and dry seasons, and there is no significant fluctuation. Only a few sites, such as M4, show a higher value of 29% during the wet season, which may be an occasional phenomenon caused by localized farmland fertilization.

[0081] Compared with the traditional approach based solely on and Compared to the MixSIAR model based on isotopic information, this invention introduces the HIX parameter, which characterizes the degree of humification of dissolved organic matter, as a continuous covariate to constrain the calculation of the contribution rate of pollution sources. Figure 6 The results show that the introduction of HIX effectively enhanced the identification of leachate sources in contaminated areas, while reasonably correcting the contribution rates of natural sources such as evaporite dissolution, reducing the uncertainty in source tracing caused by overlapping isotopic characteristics from different sources. Simultaneously, the spatial distribution of source contribution rates at each sampling point showed better consistency with the migration characteristics of groundwater pollution plumes and the variation patterns of DOM humification, improving the accuracy of landfill leachate pollution identification and the reliability of quantitative analysis of sulfate sources.

[0082] The above embodiments demonstrate the following technical effects of the present invention:

[0083] (1) By coupling DOM feature parameters with sulfur and oxygen stable isotopes and introducing HIX and BIX as continuous covariates, the analytical error caused by end-member overlap in the traditional sulfur and oxygen isotope mixing model is effectively solved, and the accuracy of distinguishing between landfill leachate and evaporite dissolution sources is significantly improved.

[0084] (2) By establishing a dynamic response relationship between DOM evolution and sulfate source contribution rate, the sulfate source contribution rate was transformed from static analysis to dynamic analysis, which can accurately reflect the dynamic changes of sulfate contribution rate from different sources during the rainy and dry seasons.

[0085] (3) The PARAFAC model was used to analyze the fluorescent components of DOM and combined with characteristic parameters such as HIX and BIX to effectively characterize the increased DOM humification and changes in microbial activity caused by the input of landfill leachate, providing constraining evidence at the level of organic matter biogeochemical processes for sulfate source identification.

[0086] (4) In this embodiment, all tests and analyses were completed based on existing groundwater quality monitoring, stable isotope testing and three-dimensional fluorescence analysis equipment, which verified that the present invention has good engineering applicability and promotion value.

[0087] In other embodiments, the method of the present invention is also applicable to complex groundwater pollution sites with mixed sulfates from multiple sources, such as mining areas, agricultural areas, industrial parks, oil and gas extraction areas, karst areas, riverbanks, or tailings ponds. By selecting appropriate stable isotope parameters and DOM characteristic parameters, dynamic quantitative analysis of sulfate sources can be achieved.

[0088] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for analyzing the source of sulfate in groundwater, characterized in that, Includes the following steps: Collect groundwater samples and potential sulfate source endmember samples from the target area; Determine the stable isotope parameters in the sample; The characteristic parameters of dissolved organic matter in the sample were determined, and the three-dimensional fluorescence spectral data were analyzed using a parallel factor analysis model or fluorescence region integration method to obtain the fluorescent components and calculate the characteristic parameters. Establish the response relationship between the characteristic parameters of the dissolved organic matter and the sulfate concentration and stable isotope parameters in the sample, so as to identify the characteristics of dissolved organic matter corresponding to different sulfate sources; The characteristic parameters are used as continuous covariates and input together with the stable isotope parameters into the Bayesian mixture model to establish a Bayesian mixture model constrained by soluble organic matter covariates. The model was run to obtain the contribution rate of different sulfate sources and to determine the main source of sulfate in groundwater.

2. The method according to claim 1, characterized in that, The characteristic parameters of the dissolved organic matter include the humification index HIX, the biological index BIX, the fluorescence index FI, the proportion of fluorescent components in parallel factor analysis, the fluorescence region integral parameter, and the 254 nm ultraviolet absorbance ratio. One or more of the following: dissolved organic carbon concentration, E2 / E2 ratio, humic peak intensity, and protein peak intensity.

3. The method according to claim 1, characterized in that, The stable isotope parameters include sulfate sulfur stable isotopes. and sulfate oxygen stable isotopes .

4. The method according to claim 1 or 3, characterized in that, The stable isotope parameters also include groundwater hydrogen stable isotopes. Stable isotopes of oxygen in groundwater Nitrate nitrogen stable isotopes Nitrate oxygen stable isotopes Dissolving inorganic carbon stabilizes isotopes Strontium isotopes and boron isotopes One or more of them.

5. The method according to claim 1, characterized in that, The Bayesian mixture model is the MixSIAR model, which is used for iterative calculations using the Markov chain Monte Carlo method.

6. The method according to claim 1, characterized in that, The potential sulfate source end-members include atmospheric deposition sources, evaporite or gypsum dissolution sources, agricultural activity sources, landfill leachate sources, and industrial or domestic wastewater sources.

7. The method according to claim 1, characterized in that, After measuring the three-dimensional fluorescence spectrum, the data is preprocessed, including blank subtraction, scattering correction, internal filter effect correction, and fluorescence intensity normalization.

8. The method according to claim 1, characterized in that, The determination of the sample also includes testing the water chemical parameters of the sample, which include: , , , , , , and .

9. The method according to claim 1, characterized in that, The method is applicable to complex groundwater contamination sites with mixed sulfates from multiple sources, including landfills, mining areas, agricultural areas, industrial parks, oil and gas extraction areas, karst areas, riverbanks, or tailings ponds.

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