A pollution traceability verification system combining spectral fingerprint, amino acid, DNA

Abstract

This invention discloses a pollution source tracing and verification system combining spectral fingerprinting, amino acid analysis, and DNA. It includes a multi-media collaborative sensing module, a multi-dimensional source tracing and analysis module, and a blockchain verification closed-loop module. These modules are linked through data transmission and edge processing technologies. The multi-media collaborative sensing module is used to collect spectral fingerprint, amino acid, and DNA characteristic data from water bodies within a watershed. It includes a research-grade integrated sampling terminal, lightweight sampling components at the grassroots level, and in-situ intelligent monitoring points. The research-grade integrated sampling terminal is equipped with a spectral detection module, a rapid amino acid detection component, and a DNA extraction and amplification device. This invention achieves three-level source tracing—from pollution type identification to pollution source category differentiation and then to specific emission point location—through a progressive collaborative architecture of spectral fingerprinting, amino acid analysis, and DNA analysis.

Description

Technical Field

[0001] This invention belongs to the field of environmental engineering technology, and in particular relates to a pollution source tracing and verification system that combines spectral fingerprinting, amino acids, and DNA. Background Technology

[0002] Currently, tracing pollution sources in industrial parks faces severe challenges, especially in parks with homogeneous industries. These parks are characterized by a high concentration of enterprises and numerous discharge outlets, resulting in highly concentrated industrial structures. This leads to wastewater discharges from various enterprises generally containing similar pollutants, exhibiting homogeneous composition. Furthermore, the underground pipe networks within these parks, after years of renovation, often suffer from incomplete blueprints and mixed storm and sewage connections, resembling underground mazes. This not only increases the physical difficulty of source tracing but also provides cover for illegal wastewater transfer and clandestine discharges. In addition, the irregular discharge times of enterprises within industrial parks, with some engaging in nighttime discharges, make traditional manual investigations inefficient and prone to alerting suspects. Regarding liability determination, industrial parks often involve multiple entities discharging pollutants over long periods, with pollutants migrating, transforming, and accumulating through environmental media. A particular enterprise's discharge may have fully complied with emission standards at the time, but it is difficult to attribute the exceedances to that specific enterprise afterward, making pollution liability determination extremely challenging.

[0003] While existing source tracing technologies have made some progress, they still fall short of meeting the precise source tracing needs of industrial clusters. Spectral fingerprinting technology can identify pollutant types such as pesticides, fertilizers, and humic substances in water bodies, but when faced with wastewater from homogeneous industrial parks with highly similar pollutants, the spectral characteristics are only slightly different, making it difficult to effectively distinguish between different enterprises. Amino acid analysis technology relies on differences in specific amino acid content to help differentiate pollution sources, but its ability to identify industrial organic wastewater when used alone is limited. DNA source tracing technology can locate emission sources by amplifying specific gene fragments of microorganisms, but this technology largely depends on laboratories, professionals, and expensive equipment, significantly reducing the timeliness and accuracy of qualitative and quantitative identification of pollution sources. Digital twin technology is mostly designed for single watersheds and lacks simulation modules for industrial park drainage networks. Existing technologies mostly rely on single methods or combinations of two technologies, lacking a progressive and collaborative architecture from pollution type identification to pollution source classification and then to specific emission point location, making it difficult to achieve precise source tracing and responsibility determination for industrial park discharge outlets. Summary of the Invention

[0004] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a pollution source tracing and verification system that combines spectral fingerprinting, amino acids, and DNA, solving the problems of insufficient accuracy, limited coverage, difficulty in implementation, weak adaptability, and high cost in the prior art for tracing non-point source pollution.

[0005] To achieve the above objectives, the present invention provides the following technical solution: A pollution source tracing and verification system combining spectral fingerprinting, amino acids, and DNA includes a multi-media collaborative sensing module, a multi-dimensional source tracing and analysis module, and a blockchain verification closed-loop module. The modules are linked through data transmission and edge processing technologies. The multi-media collaborative sensing module is used to collect spectral fingerprint, amino acid and DNA feature data in water bodies. The spectral fingerprint data is obtained through in-situ online detection or rapid on-site detection, while the amino acid and DNA feature data are obtained through laboratory analysis. The multi-dimensional source tracing and analysis module is used to integrate spectral fingerprint, amino acid and DNA data to achieve pollution source tracing and verification. The spectral fingerprint is obtained and matched to identify the pollution type through in-situ online detection. The amino acid composition characteristics are obtained through laboratory analysis to distinguish the pollution source category. The DNA feature sequence is obtained through laboratory analysis to locate the specific pollution emission point. Combined with the migration simulation results, a pollution source tracing report is output. The blockchain verification closed-loop module is used to realize the trusted storage of traceability data and the closed-loop application of verification results, generate an immutable traceability verification list, and verify and optimize the traceability results through a retesting and comparison mechanism. This technology utilizes a progressive synergy of spectral fingerprinting, amino acids, and DNA to achieve three levels of source tracing, from identifying pollution types to distinguishing pollution source categories and locating specific emission points.

[0006] Preferably, the multi-media collaborative sensing module includes a research-grade integrated sampling terminal, a lightweight sampling component at the grassroots level, an in-situ intelligent monitoring point, an industrial wastewater sampler, and a laboratory analysis unit; the laboratory analysis unit performs amino acid analysis and DNA extraction and amplification on the water samples collected and stored by the research-grade integrated sampling terminal and the in-situ intelligent monitoring point to obtain the amino acid composition and DNA characteristic sequence data of the water body.

[0007] The research-grade integrated sampling terminal is equipped with a portable spectral detection module and a sample collection and preservation component. The sample collection and preservation component is equipped with a spectral detection buffer and a DNA stabilizer, which can acquire water spectral fingerprints on site and collect and preserve water samples for amino acid analysis and DNA extraction for laboratory testing. The spectral detection module includes a miniature calibration component that integrates nitrogen and phosphorus standards, humic acid characteristic fluorescent substances, and industrial characteristic pollutant standards. It is used to calibrate the peak position and intensity of the spectrometer on-site, so that the spectral fingerprint detection error is controlled within ±3%.

[0008] Preferably, the lightweight sampling component at the grassroots level includes a biodegradable sampling box pre-loaded with protective reagents and a mobile application with QR code recognition function. The protective reagents include a DNA stabilizer and a spectral detection buffer. The reagent dosage in the biodegradable sampling box is matched to the needs of a single test. The mobile application is divided into three types based on the sampling subject: farmer's end, enterprise environmental protection officer's end, and supervisor's end. The enterprise environmental protection officer's end adds a function to record industrial wastewater characteristic parameters, and the supervisor's end adds a function to record data quality control parameters. The data quality control parameters include sampling environment temperature and sample storage time.

[0009] Preferably, the in-situ intelligent monitoring point is equipped with a stratified sampling device, a multi-parameter sensor, and an in-situ spectral detection unit. The stratified sampling device collects surface water samples at a depth of 0 to 5 cm and deep water samples at a depth of 5 to 10 cm, respectively. The multi-parameter sensor simultaneously detects water temperature, pH value, and dissolved oxygen content. The in-situ spectral detection unit collects water spectral fingerprint data in real time to assist in correcting spectral fingerprint detection values. The in-situ intelligent monitoring points are deployed at industrial discharge outlets, river sections, or lake and reservoir locations. They are powered by solar energy and automatically collect water samples every four hours, which are then refrigerated at 4°C. Simultaneously, the in-situ spectral detection unit collects real-time fluorescence spectral data of the water body. The refrigerated water samples are periodically retrieved by researchers and sent to the laboratory for amino acid and DNA analysis.

[0010] Preferably, the multi-dimensional source tracing and analysis module includes a digital twin mapping unit and a multi-source data analysis engine; The digital twin mapping unit is constructed according to the actual scale of the watershed, and maps the data collected by the multi-media collaborative sensing module in real time to simulate the migration characteristics and paths of spectral fingerprints, amino acids and DNA in the aquatic environment. The digital twin mapping unit is configured with corresponding simulation modules for four types of scenarios: industrial parks, irrigation areas, hilly areas and plain river networks. The industrial park corresponds to the park drainage pipe network simulation module, which is used to simulate the transmission time delay and mixing characteristics of industrial wastewater in the enterprise's internal pipe network and the park's main pipe network. The multi-source data analysis engine is equipped with an industrial characteristic pollutant spectral library, an industrial source amino acid fingerprint library, and an industrial microbial DNA fingerprint library.

[0011] Preferably, the spectral fingerprint matching function of the multi-source data analysis engine identifies typical pollution types, including industrial characteristic pollutants, pesticides, fertilizers, livestock and poultry manure, and domestic sewage, by comparing the sample spectral peak positions and peak intensities with the characteristic pollutant spectral library, with a matching accuracy of not less than 85%.

[0012] Preferably, the amino acid composition feature analysis function of the multi-source data parsing engine detects the relative content of characteristic amino acids in the sample, analyzes the relative content and ratio relationship of characteristic amino acids through a preset amino acid discrimination model, and outputs the pollution source category determination result.

[0013] Preferably, the DNA feature sequence tracing function of the multi-source data analysis engine amplifies microbial-specific gene fragments in the sample and compares them with the DNA fingerprint database of known emission sources, including industrial enterprises, livestock and poultry farms, domestic sewage treatment plants and farmland areas. When the matching similarity is higher than 95%, the specific pollution emission point is located, and the source tracing accuracy is down to a single enterprise discharge outlet, a single breeding farm, or a single farmland area.

[0014] Preferably, the blockchain verification closed-loop module includes a two-layer evidence storage chain and a verification-feedback unit; The dual-layer evidence storage chain includes a consortium chain and a public supervision chain. The consortium chain stores spectral fingerprints, amino acid detection results, DNA sequence data, and hash values ​​of the parsing process, generating an immutable traceability verification list. The public oversight chain is used to upload the anonymized verification results and supports the public in querying traceability information and reporting suspected contamination points through the application; The source tracing verification list includes sampling time, sampling location, key parameters of spectral fingerprint, raw data of amino acid detection, DNA sequence comparison results, analysis basis, and information on responsible entities. The responsible entities include industrial enterprises, farmland management entities, and aquaculture enterprises. This list is connected to the local environmental protection supervision system as the basis for assessing the effectiveness of pollution control and determining pollution responsibility.

[0015] Preferably, the preset threshold of the verification-feedback unit is 15%; When the deviation between the retested spectral characteristic value and the source tracing report exceeds 15%, or the amino acid concentration deviation exceeds 15%, the scientific research-grade integrated sampling terminal will be automatically activated to retest and sample key areas, additional water samples will be sent to the laboratory for DNA characteristic sequence comparison and verification, and the multi-source data analysis engine will be called to regenerate the pollution source tracing verification results.

[0016] The technical effects and advantages of the pollution source tracing and verification system combining spectral fingerprinting, amino acids, and DNA in this invention are as follows: 1. This invention utilizes a progressive collaborative architecture of "spectral fingerprinting (in situ online) + amino acid (laboratory analysis) + DNA (laboratory calibration)" to achieve three-level source tracing from "pollution type identification" to "pollution source category differentiation" and then to "specific emission point location": spectral fingerprinting, through in-situ online detection and comparison with a pollution source database, quickly identifies pollution types such as pesticides, fertilizers, livestock and poultry manure, and industrial characteristic pollutants (such as benzene series compounds, halogenated hydrocarbons, heavy metal complexes, etc.); amino acid composition characteristics, through laboratory HPLC analysis, supplement and verify the spectral fingerprint results, accurately distinguishing livestock and poultry breeding sources, farmland non-point source pollution, domestic pollution sources, and industrial organic wastewater sources; DNA characteristic sequences, through laboratory PCR amplification and sequencing comparison, correct the source tracing results and locate the source to a single enterprise discharge outlet, a single farm, or a region of farmland. The three technologies form a progressive relationship of "in-situ screening—laboratory verification—precise location." Spectral fingerprinting is the primary method for real-time monitoring, amino acids are a supplementary method for verification analysis, and DNA is a correction method for precise location. This effectively avoids the shortcomings of single technologies that "can only roughly screen broad categories but cannot precisely locate," significantly reducing the separation error of mixed source pollution and improving the accuracy and reliability of source tracing results. Especially for industrial parks with homogeneous industries, DNA characteristic sequences can further distinguish the differences in microbial communities among homogeneous enterprises, precisely locating the discharge outlet of a single enterprise workshop.

[0017] 2. This invention relies on a multi-entity collaborative sensing network consisting of a "research-grade integrated sampling terminal + lightweight grassroots sampling components + in-situ intelligent monitoring points," breaking the limitations of traditional source tracing that depends on single sampling by research units. The research-grade terminal meets high-precision detection requirements, while the lightweight grassroots components (including biodegradable sampling boxes and differentiated APP guidelines) lower the operational threshold for farmers and township environmental protection personnel, achieving sampling coverage in weak areas such as river tributaries and remote farmland. The in-situ intelligent monitoring points transmit spectral fingerprint data in real time and automatically save water samples for laboratory analysis through in-situ spectral detection and automatic sampling. Simultaneously, quality control designs such as reagent pre-loading and environmental parameter recording during the sampling process further ensure data quality, providing comprehensive and reliable basic data support for subsequent source tracing analysis. The configuration of industrial wastewater samplers, exhaust gas adsorption tubes, and solid waste leachate collection devices enables the system to comprehensively cover three types of emission sources in industrial parks: wastewater, exhaust gas, and solid waste, filling the gap in existing technologies for multi-media sampling in industry.

[0018] 3. This invention utilizes a two-layer blockchain evidence storage system (consortium blockchain + public supervision blockchain) to achieve immutable evidence storage of "sampling data - analysis process - source tracing results," addressing the pain points of traditional source tracing methods where "data is easily tampered with and results are easily disputed." The source tracing verification list generated by the consortium blockchain connects to the local environmental protection regulatory system, clarifying the pollution responsibilities of industrial enterprises, townships, farms, and farmers, providing a rigid basis for pollution control assessment and responsibility determination. The public supervision blockchain, through the public disclosure of anonymized data and feedback on suspected pollution points, forms a collaborative mechanism of "government control + public supervision," promoting the transformation of source tracing results from "laboratory data" into "actual pollution control actions," and constructing a complete closed loop of "source tracing - control - verification." The responsibility is clearly included in industrial enterprises, effectively solving the problem of shirking pollution responsibility among similar enterprises in industrial parks.

[0019] 4. The digital twin mapping unit in the multi-dimensional source tracing and analysis module of this invention is customized with specialized modules such as park drainage network simulation, irrigation cycle pollution migration, rainfall runoff pollution simulation, and river network water exchange analysis, tailored to the characteristics of different scenarios such as industrial parks, irrigation areas, hilly areas, and plain river networks. This accurately restores the migration patterns of pollutants under different scenarios. Among them, the park drainage network simulation module can accurately simulate the transmission time delay and mixing characteristics of industrial wastewater in the internal pipe network of enterprises and the main pipe network of the park, providing key spatiotemporal data support for source tracing of industrial park discharge outlets. At the same time, the solar-energy storage power supply design of the in-situ intelligent monitoring point and the data priority adjustment and rapid analysis logic in emergency source tracing further adapt to special scenarios such as remote areas without power grids and rainstorm emergencies. This allows the system to be applied to various typical non-point source pollution scenarios in industrial parks and the Yangtze and Yellow River basins without significant adjustments, and has broad promotional value.

[0020] 5. The biodegradable design and pre-packaged reagents of the lightweight sampling components used in this invention reduce the generation of chemical waste and waste of consumables, thereby lowering the cost of sampling hardware. The graphic guide and automated data upload function of the mobile APP simplify the operation process, enabling non-professionals to participate in sampling. The automated operation of blockchain and digital twins reduces human intervention and lowers the labor and time costs in the traceability process, providing a convenient and economical technical solution for the routine traceability work of grassroots environmental protection departments, watershed management agencies and other units.

[0021] 6. This invention, through a three-level progressive synergy of "spectral fingerprint + amino acid + DNA", addresses pollution from multiple scenarios such as industrial park discharge outlets, agricultural non-point source pollution, and domestic pollution sources. It achieves a complete source tracing chain from pollution type identification and pollution source classification to specific emission point location, filling the gap in existing technologies for precise location of discharge outlets in isomorphic industrial parks and pollution separation in industrial-agricultural mixed zones. It provides a reliable technical means for environmental supervision and pollution liability determination in isomorphic industrial parks. Attached Figure Description

[0022] Figure 1 This is a system connection diagram of a pollution source tracing and verification system combining spectral fingerprinting, amino acids, and DNA proposed in this invention; Figure 2 This is a flowchart of a three-level source tracing system proposed in this invention, which combines spectral fingerprinting, amino acids, and DNA. Detailed Implementation

[0023] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0024] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include," "contain," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "includes..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0025] refer to Figure 1-2 This invention proposes a pollution source tracing and verification system combining spectral fingerprinting, amino acid analysis, and DNA. For each technical feature of the multi-media collaborative sensing module, multi-dimensional source tracing and analysis module, and blockchain verification closed-loop module, it supplements the information with equipment models and parameters, reagent specifications, operating procedures, data processing methods, and scenario details to ensure that those skilled in the art can fully reproduce the system. In all embodiments, the selection and operation of core equipment and reagents comply with industry standards.

[0026] Core equipment and reagent specifications (general basic configuration): Spectral detection module: It adopts a miniature fiber optic spectrometer with a wavelength range of 200-1100nm, a resolution of 0.02nm, and an integration time that can be adjusted between 10-10000ms. It is mainly used to collect spectral fingerprints of water and soil to ensure the capture of fine spectral information of characteristic functional groups of pollutants.

[0027] Amino Acid Analysis Kit (Laboratory Configuration): Composed of a high-performance liquid chromatograph and a fluorescence detector, using a ZORBAX SB-C18 column (4.6 × 250 mm, 5 μm particle size). The column temperature is set at 35℃, and the mobile phase consists of methanol and 0.05 mol / L potassium dihydrogen phosphate at a volume ratio of 30:70 (pH adjusted to 6.8). This kit is used for the accurate detection of amino acid composition and content in samples, ensuring accurate differentiation of pollution sources. Deployed in the laboratory, this kit is used for laboratory analysis of water samples collected and stored by a research-grade integrated sampling terminal.

[0028] DNA Extraction and Amplification Device (Laboratory Configuration): Includes a real-time quantitative PCR instrument and a DNA extraction kit; the total volume of the PCR reaction system is 20 μL, containing 10 μL of SYBR GreenMix, 0.5 μL of upstream and downstream primers (both at a concentration of 10 μmol / L), 2 μL of template DNA, and 7 μL of enzyme-free water; the amplification program is set to 95℃ pre-denaturation for 3 min, followed by 40 cycles of 95℃ denaturation for 15 s, 58℃ annealing for 30 s, and 72℃ extension for 30 s, used to extract and amplify specific DNA fragments from contaminated samples to pinpoint specific emission sources. This device is deployed in the laboratory for laboratory analysis of water samples collected from a research-grade integrated sampling terminal and preserved with DNA stabilizers.

[0029] Multi-media collaborative sensing module supporting equipment: For industrial pollution scenarios, it is equipped with an industrial wastewater sampler (corrosion-resistant material, sampling depth 0-5m), a waste gas adsorption tube (filled with TenaxTA adsorbent, suitable for active sampling of volatile organic compounds) and a solid waste leachate collection device (compliant with HJ299-2007 standard), used for collecting environmental samples from wastewater discharge outlets, waste gas emission outlets and the surrounding environment of solid waste storage yards in industrial parks.

[0030] In-situ intelligent monitoring point: Equipped with a 20W monocrystalline solar panel (conversion efficiency 23%), a 12V / 100Ah lithium iron phosphate energy storage battery, a multi-parameter sensor (model HACHHQ40d), and an in-situ fluorescence spectroscopy detection unit; the multi-parameter sensor has a dissolved oxygen detection range of 0-50mg / L (accuracy ±0.1mg / L), a pH detection range of 0-14 (accuracy ±0.01), and a water temperature detection range of -5-60℃ (accuracy ±0.1℃), which can collect key environmental parameters of the water body in real time and assist in correcting the in-situ spectral detection results; the in-situ fluorescence spectroscopy detection unit automatically collects water body fluorescence spectrum data every four hours, performs preliminary matching with the pollution source spectral fingerprint database through the edge computing module, and outputs pollution type identification results in real time; at the same time, water samples are automatically collected and stored at 4℃, and periodically retrieved by researchers and sent to the laboratory for amino acid and DNA analysis.

[0031] Blockchain nodes: Deployed on Alibaba Cloud servers (ECSg6.xlarge, 4 cores, 8GB memory), using the Hyperledger Fabric 2.4 blockchain framework; the hash algorithm used is SHA-256, the consensus mechanism is Raft, and a total of 5 nodes are set up (1 for scientific research institutions, 2 for environmental protection departments, and 2 for third-party testing agencies) to ensure the immutability of traceability data and multi-party consensus.

[0032] Core reagents: DNA stabilizer containing 10 mmol / L EDTA and 50 mmol / L Tris-HCl (pH adjusted to 8.0) to prevent DNA degradation after sampling; spectral detection buffer containing 0.1 mol / L Tris-HCl (pH 7.5) to eliminate the influence of water sample pH on spectral peak positions; amino acid standards (Sigma-Aldrich brand, purity ≥99%) to plot standard curves for quantitative calculation of amino acid content in samples; industrial characteristic pollutant standards including mixed standard solutions of benzene series compounds (benzene, toluene, xylene), halogenated hydrocarbons (chloroform, carbon tetrachloride) and heavy metal ions (copper, zinc, lead) for on-site spectrometer calibration.

[0033] Example 1 This embodiment proposes a pollution source tracing and verification system combining spectral fingerprinting, amino acids, and DNA for tracing pollution sources at discharge outlets in industrial parks with isomorphic industries. Specific implementation details include: Implementation Background: This example focuses on a chemical industrial park housing 12 fine chemical companies, primarily producing dye intermediates, exhibiting a highly homogeneous industrial structure. Wastewater from the park is pre-treated by the companies before being discharged into the park's sewage network, ultimately flowing into a surrounding river via the park's main discharge outlet. The river section detected that the characteristic pollutant p-nitrochlorobenzene exceeded the standard by three times (the surface water standard limit is 0.05 mg / L). The specific companies and discharge outlets involved in the illegal discharge must be identified within 48 hours to provide evidence for environmental enforcement. While all company discharge outlets in the park are equipped with flow meters and online pH monitors, the companies' discharge times are irregular, and some engage in illegal nighttime discharges. Traditional manual investigations are inefficient and prone to alerting the companies.

[0034] System Configuration: (1) Multi-media collaborative sensing module: Industrial wastewater samplers (GW-2000 type) were used to collect water samples from the main discharge outlet of the industrial park and the discharge outlets of 12 individual enterprises. Three parallel samples (500 mL each) were collected from each discharge outlet. Simultaneously, sediment samples (top 0-5 cm) were collected from the discharge outlets for DNA analysis. Waste gas adsorption pipes were installed at the emission outlets of each enterprise's waste gas treatment facilities and at the park boundary, with a sampling flow rate of 0.5 L / min and a sampling time of 2 hours, to assist in assessing the enterprises' production conditions. Considering the characteristics of dye intermediate production wastewater, p-nitrochlorobenzene standards (concentration gradient 0.01-1.0 mg / L) were added to the micro-calibration component of the spectroscopic detection module for on-site calibration of the spectrometer's characteristic peak response at 280 nm.

[0035] (2) Multi-dimensional source tracing and analysis module: The digital twin mapping unit constructs a 3D model of the park's drainage network (based on the park's CAD network map and on-site survey data), simulating the pollutant flow time lag from each enterprise's outlet to branch pipes, main pipes, and the main outlet (the park's network is 8.6km long, with an average flow velocity of 0.5m / s, and the transmission time from a single enterprise's outlet to the main outlet varies from 2 to 15 minutes). The multi-source data analysis engine has previously established a spectral-amino acid-DNA fingerprint database for 12 enterprises (constructed through continuous sampling and testing during normal production for 3 days, covering the characteristic spectral peak positions, amino acid ratios, and unique microbial communities of each enterprise under normal operating conditions).

[0036] (3) Blockchain verification closed-loop module: By setting a "Responsible Data for Industrial Discharge Outlets" priority tag, the data upload and consensus confirmation speed is increased by 50%, and the block generation time is ≤5 seconds, ensuring that parsing and verification are completed within 48 hours. The GPS coordinates of all sampling points, raw detection data, and traceability results are uploaded to the blockchain in real time, generating an immutable industrial discharge outlet traceability verification list.

[0037] Operating steps: (1) Sampling phase (Day 1, 8:00-12:00): Water samples were collected from the main discharge outlet (P0) of the park and the discharge outlets of 12 enterprise workshops (P1-P12). Three parallel samples were collected from each discharge outlet (marked as P0-1, P0-2, P0-3, and so on), with a sampling depth of 0.5m below the water surface. At the same time, 50g of surface sediment samples (0-5cm) were collected from each discharge outlet. The samples were immediately refrigerated at 4℃ after collection and delivered to the laboratory for testing within 2 hours.

[0038] (2) Detection and Analysis Phase (12:00-18:00): Spectroscopic detection: Water samples were filtered through a 0.45μm filter membrane and then injected into a quartz cuvette. The spectrometer integration time was set to 500ms, and three scans were performed, with the average value taken. The peak intensities at 280nm (characteristic peak of p-nitrochlorobenzene) and 450nm (characteristic peak of humic acid) were recorded. The results showed that the peak intensity of sample P0 at the main discharge outlet was 0.92 at 280nm, while the peak intensities of P3 (discharge outlet of company A), P5 (discharge outlet of company B), and P8 (discharge outlet of company C) at 280nm were 0.88, 0.45, and 0.51, respectively, preliminarily identifying anomalies at discharge outlets of companies A, B, and C.

[0039] Amino acid detection: Take 10 mL of water sample, add 0.5 mL of phthalaldehyde derivatizing agent, react at 37℃ for 10 min; injection volume 10 μL, mobile phase flow rate 1.0 mL / min, fluorescence detector excitation wavelength 340 nm, emission wavelength 450 nm. The results show that the D-alanine / glutamic acid ratio in the P0 sample from the total discharge outlet is 1.05 (this ratio is usually ≤0.3 in normal chemical wastewater), and the ratio in the P3 sample from enterprise A discharge outlet is 1.12, which is highly consistent.

[0040] DNA Amplification and Alignment: PCR reactions were performed using a Bio-Rad CFX96 system. Specific primers (upstream F-5'-ACACGGCCCAGACTCCTACG-3', downstream R-5'-GCTTGCGACCGTACTCCTTC-3', amplifying fragment length 150bp) were designed for the halophilic thermophilus specific to the wastewater treatment unit A of Company A. After amplification, 5μL of the product was subjected to 1.5% agarose gel electrophoresis (120V, 20min) to confirm the 150bp target band; positive products were sent to a sequencing company for Sanger sequencing. The results showed that the sequence match between the total discharge port P0 and the halophilic thermophilus in the discharge port P3 sample of Company A reached 98.2%, while the match between the samples from discharge ports B and C of Company A was less than 75%.

[0041] (3) Retesting and Confirmation Phase (18:00-22:00): Based on multi-source data, the system automatically identified Company A as the most suspected source and sent a retesting instruction to the research department at 18:30. At 19:00, three additional sampling points were set up at Company A's discharge outlet (P3) and 50m upstream and downstream for retesting. The retesting results showed that the concentration of p-nitrochlorobenzene at the P3 discharge outlet was 0.48mg / L (exceeding the limit by 9.6 times), the 280nm spectral peak intensity was 0.91 (deviation 2.3%, below the 15% preset threshold), the D-alanine / glutamate ratio was 1.08, and the DNA matching degree was 98.5%. All five indicators were highly consistent. At 22:00, the source tracing report was completed, confirming that the discharge outlet of Company A's workshop was the source of the exceedance.

[0042] Source tracing results: It was confirmed that the discharge outlet of Company A (a fine chemical company) was the main source of excessive p-nitrochlorobenzene emissions. The company's daily discharge volume was 85m³. 3 The average concentration of p-nitrochlorobenzene was 0.46 mg / L, exceeding the standard by 9.2 times; the concentration of p-nitrochlorobenzene at the total discharge outlet of the park was 0.15 mg / L, and Company A contributed 82% of this characteristic pollutant.

[0043] Key parameters and reproduction instructions: Spectral detection error: The average value of the 280nm peak intensity of the three parallel samples is 0.92, the standard deviation is 0.018, and the error = (standard deviation / average value) × 100% ≈ 1.96%, which meets the technical specification of "spectral detection error ≤ ±3%".

[0044] Responsibility Determination: The traceability verification list generated by the blockchain includes the sampling GPS coordinates (120°22′15″E, 31°55′30″N), raw test data, DNA sequence comparison results (match rate 98.2%), analytical basis, and the responsible party (Company A). This list was pushed to the local environmental protection regulatory system at 9:00 AM the following day as the basis for punishment. Faced with the evidence, Company A admitted on the spot to the illegal nighttime emissions, and the environmental protection department imposed a fine and ordered rectification within a specified period.

[0045] Reproduction Instructions: Those skilled in the art can reproduce this process by following these steps: First, purchase the same equipment and reagents according to the "Core Equipment and Reagent Specifications," and select a chemical industrial park of the same scale as this embodiment (containing more than 12 similar enterprises and a total discharge outlet with p-nitrochlorobenzene exceeding the standard by more than 3 times); Second, strictly follow the "Spectral Calibration Steps" (including gradient calibration of p-nitrochlorobenzene standards) and the "DNA Extraction-Amplification Steps" (including specific primers for halophilic thermophilic bacteria); Third, use the multi-source data fusion judgment rules described in the text (spectral peak intensity at 280nm ≥ 0.8, D-alanine / glutamate ratio ≥ 1.0, and DNA matching degree ≥ 95% simultaneously) to obtain a consistent traceability conclusion.

[0046] Example 2 This embodiment proposes a pollution source tracing and verification system combining spectral fingerprinting, amino acids, and DNA for tracing non-point source pollution during the spring irrigation season in large-scale gravity-flow irrigation areas. Specific implementation details include: Implementation Background: The study area in this embodiment is a large-scale gravity-flow irrigation area in the Yellow River Basin, with a total irrigated area of ​​8.61 million mu and an average daily diversion of 5 million cubic meters of water from the Yellow River during the spring irrigation season. 3Irrigation is primarily gravity-fed, with a three-tiered irrigation and drainage system consisting of main canals, branch canals, and drainage ditches. Major pollution sources in the area include fertilizers used in wheat fields (urea applied at a rate of 20 kg / mu, concentrated one week before spring irrigation) and manure from sheep farms (each farm has 500 sheep, with an average daily discharge of 10 m³). 3 (Manure and sewage are mostly discharged directly into the drainage canal through open ditches). The core problem to be solved in this embodiment is to distinguish the contribution ratio of "wheat field source - sheep farming source" to the total nitrogen (TN) and total phosphorus (TP) of the drainage canal water, and to locate the specific farms that exceed the emission standards, so as to provide a basis for precise pollution control.

[0047] System Configuration: (1) Multi-media collaborative sensing module: Research-grade integrated sampling terminal: Equipped with a 2L acrylic water sampler (made of polytetrafluoroethylene to avoid adsorbing water pollutants) and a columnar sediment sampler (50cm long, 5cm inner diameter, capable of collecting 0-5cm surface sediment and 5-10cm deep sediment). The calibration steps for its spectral detection module are as follows: First, take 10mg / L nitrogen and phosphorus standards (a mixture of potassium nitrate and potassium dihydrogen phosphate, filtered through a 0.22μm filter membrane) and 5mg / L humic acid standard solution (Sigma-Aldrich brand), and inject them separately. First, use a quartz cuvette with a 1cm optical path length. Second, start the spectrometer and first detect the baseline spectrum of the blank buffer solution (0.1 mol / L Tris-HCl, pH 7.5), then detect the spectra of the two standards in sequence. Third, adjust the spectrometer parameters: if the measured peak position of the characteristic peak at 220nm for the nitrogen and phosphorus standards is 220.1nm, correct the wavelength shift by -0.1nm using software; if the measured peak intensity of the humic acid standard at 450nm is 0.97 (theoretical value 1.0), set the gain coefficient to 1.03 until the peak intensity deviation is ≤3%.

[0048] The DNA extraction steps are as follows: Take 10g of sediment sample, add 5mL of lysis buffer from the DNA extraction kit, heat in a 65℃ water bath for 30min, and vortex at 2000rpm for 5min during this period; then centrifuge at 12000rpm for 10min, take the supernatant and purify it according to the kit instructions, with a final elution volume of 50μL; use Nanodrop2000 to detect the DNA concentration, and ensure that the concentration is ≥50ng / μL to avoid affecting subsequent amplification due to insufficient DNA.

[0049] In-situ intelligent monitoring points: Three points are deployed, located upstream (at the confluence of the primary branch canal, numbered S1), midstream (at the confluence of the secondary branch canal, numbered S2), and downstream (1 km before the lake entrance, numbered S3). The monitoring points are powered by a combination of solar panels and energy storage batteries, adapted to the region's abundant sunshine but weak power grid coverage. They automatically sample once every 4 hours, at depths of 0-5 cm (surface water) and 5-10 cm (deep water), with a single sample volume of 500 mL. The water sample is automatically injected into a storage bottle with a 4°C temperature control function to prevent microbial degradation that could lead to changes in indicators. Data transmission uses a LoRa module (transmission distance 5 km, baud rate 9600 bps), sending "water temperature-pH-dissolved oxygen-spectral characteristic values" data to the cloud once every 6 hours to ensure real-time monitoring of water pollution dynamics.

[0050] Lightweight sampling components for grassroots use: The biodegradable sampling box has a volume of 50mL and is made of polylactic acid (PLA). The box is pre-filled with 1mL of DNA stabilizer and 2mL of spectral detection buffer. The box has a QR code associated with a unique sampling number (e.g., GY20240410-001) for easy data traceability. The accompanying mobile APP is divided into a farmer's side and an environmental worker's side. The farmer's side includes a 30-second sampling tutorial video (demonstrating "sampling at a depth of 10cm from the drainage outlet of the paddy field, avoiding contact with soil"), an information entry field (plot type: wheat field / corn field, fertilization time: whether fertilization was carried out in the last 7 days), and an automatic GPS positioning function (error ≤10m). The environmental worker's side adds an "quality control record" field, which requires the entry of the sampling environment temperature (controlled at 20-25℃, the average daily temperature during the spring irrigation period in this area is 15-22℃) and the sample storage time (≤6 hours, if it exceeds the time limit, it must be marked "may degrade").

[0051] (2) Multi-dimensional source tracing and analysis module: Digital twin mapping unit: The input basic data includes 30m resolution DEM topographic data (from geospatial data cloud) and spring irrigation season hydrological data (5 million m³ of water diverted from the Yellow River). 3 The daily flow velocity in the drainage canal is 0.3 m / s (sourced from the irrigation district management agency); pollution source distribution data (coordinates of 120 wheat field plots and 15 sheep farms, obtained through on-site GPS mapping); the simulation parameters for the pollution migration module during the irrigation cycle are set as follows: the adsorption coefficient (Kd value) of TN in the wheat field soil is 15 mL / g, and the Kd value of TP is 25 mL / g (determined by laboratory batch equilibrium method; the soil in this area is mainly sandy soil with weak adsorption capacity); the microbial degradation rate of TN is 0.05 d. -1 The microbial degradation rate of TP was 0.02 d. -1(Based on monitoring data of the irrigation area in the past 5 years during the spring irrigation season); The simulation output results are a heat map of TN / TP concentration distribution of "irrigation water → soil → drainage canal" generated every 24 hours, as well as spectral fingerprint (220nm peak intensity) and migration trajectory of amino acid (glutamic acid) concentration, which intuitively presents the migration path of pollutants.

[0052] Multi-source data analysis engine: Spectral fingerprint matching uses the "cosine similarity method" to compare the sample spectrum with the built-in standard spectral library of 10 types of pollution (including fertilizer, manure, etc.). A similarity of ≥85% is considered a match. The specific primers for DNA feature sequence tracing are designed as follows: upstream primer F-5'-GAGGAACAGAAGACTCGTG-3' and downstream primer R-5'-GGTGAAGCGTAGAGAGGTGAA-3' (amplified fragment length 200bp, specificity confirmed by comparison with the NCBI database) targeting Bacteroidesfragilis in sheep manure. The comparison standard is: the Sanger sequencing results of the sample PCR amplification product (completed by BGI) are imported into BLAST software and compared with the pre-constructed DNA sequence library of sheep manure from 15 farms. A similarity of ≥95% can locate the specific farm.

[0053] (3) Blockchain verification closed-loop module: The consortium blockchain's evidence storage steps are as follows: First, the research team packages "sampling number + spectral spectrum (.spc format) + raw amino acid detection data (.csv format) + DNA sequence (.fasta format)" and generates a hash value using the SHA-256 algorithm. Second, the hash value is uploaded to five consortium blockchain nodes via an API interface. Each node verifies the data integrity (compares the file size with the uploaded record) and then confirms and writes it into a block using the Raft consensus mechanism (block generation time ≤ 10s). The third step is to generate a JSON-formatted traceability verification list, which includes fields such as sampling time (2024-04-15T09:00:00), sampling location (S2, 108°22′E, 40°55′N), spectral peak intensity (220nm: 0.85, 450nm: 0.62), amino acid data (glutamic acid: 18.2%, D-alanine: 12.5%), and DNA matching results (farm number GY-Y08, similarity 97.2%), to ensure data traceability.

[0054] Detailed operation steps: (1) Sampling phase (April 15-20, 2024): Sampling for scientific research: Every day at 9:00, 2L of surface water (0-5cm) and deep water (5-10cm) and 10g of bottom sediment (0-5cm / 5-10cm) are collected from three points S1, S2 and S3 respectively. Each point is sampled three times (parallel samples are set up). The sample labeling format is "S1-W1-05" (representing the first parallel sample of surface water at point S1) to ensure data repeatability.

[0055] Grassroots sampling: 20 farmers collected 500mL water samples from the drainage outlets of their respective wheat fields and recorded "fertilization time 2024-04-10" via APP; environmental protection officers collected water samples from the sewage outlets of 15 livestock farms and recorded "sampling temperature 23℃, storage time 4 hours". All samples were transported to the laboratory for testing by designated personnel on the same day (the average distance between the laboratory and the sampling area in this region is 30km, and the driving time is ≤1 hour).

[0056] In-situ monitoring: S1-S3 points are automatically sampled every 4 hours and stored at 4℃. The samples are retrieved by researchers the next day and tested simultaneously with the samples collected that day to avoid errors caused by time differences.

[0057] (2) Detection and analysis stage: Spectroscopic detection: After the water sample was filtered through a 0.45μm filter membrane, it was injected into a quartz cuvette. The integration time of the spectrometer was set to 500ms, and the average value was taken after 3 scans. The peak intensities of 220nm (characteristic peak of nitrate) and 450nm (characteristic peak of humic acid) were recorded in particular to preliminarily determine the type of pollution.

[0058] Amino acid detection: Take 10 mL of water sample, add 0.5 mL of o-phthalaldehyde (OPA) derivatizing agent, and react at 37℃ for 10 min; injection volume 10 μL, mobile phase flow rate 1.0 mL / min, fluorescence detector excitation wavelength 340 nm, emission wavelength 450 nm; draw a standard curve using amino acid standards ranging from 0.1 to 10 μg / mL, substitute the sample peak area to calculate the content of each amino acid, and then distinguish the pollution source category.

[0059] DNA amplification and sequencing: PCR reaction was performed in a Bio-Rad CFX96 instrument. After amplification, 5 μL of the product was taken for 1.5% agarose gel electrophoresis (120V, 20min) to confirm the 200bp target band. Positive products were sent to a sequencing company for Sanger sequencing. The sequencing results were imported into BLAST software and compared with the farm sequence library to locate the specific emission source.

[0060] (3) Verification and feedback phase: On April 18, when testing deep water samples at point S2, the DNA matching similarity was found to be only 92% (below the 95% threshold). The system automatically sent a retesting instruction to the research end. On April 19, three additional sampling points (S2-1, S2-2, and S2-3) were added within 500m of point S2. Among them, the DNA matching similarity at point S2-2 reached 97.2%, and the aquaculture farm GY-Y08 was finally confirmed as the main emission source, completing the closed loop of source tracing verification.

[0061] Source tracing results: S1 (upstream) site: The spectral similarity to fertilizer was 89%, glutamic acid accounted for 17.8% in amino acid detection, and no DNA matching results were found (similarity <90%). It was calculated that wheat field sources contribute 65% to TN and 70% to TP, indicating that upstream pollution is mainly caused by wheat field fertilizer (upstream wheat fields in this area are distributed in contiguous areas, and livestock farms are concentrated in the midstream).

[0062] S2 (midstream) site: The spectral similarity to manure was 87%, D-alanine accounted for 12.5% ​​in amino acid detection, and DNA similarity to GY-Y08 from the farm was 97.2%. The calculation showed that the contribution of livestock sources to TN was 42% and to TP was 45%, indicating that the midstream was significantly affected by livestock manure.

[0063] S3 (downstream) site: Spectral matching showed similarities of 85% and 83% for fertilizer and manure, respectively. Amino acid analysis showed 15.2% for glutamic acid and 10.8% for D-alanine. DNA matching showed a similarity of 95.5% for GY-Y08 from livestock farms. Calculations showed that wheat field sources and livestock farm sources contributed 35% and 40% to total nitrogen (TN) and 32% and 42% to total phosphorus (TP), respectively, reflecting downstream mixed pollution characteristics (the downstream area of ​​this region is a drainage collection area of ​​irrigation districts, where pollutants mix after long-distance migration).

[0064] Key parameter calculation method: The contribution ratio of pollution sources was calculated using the "IsoSource model," with the TN / TP characteristic values ​​of wheat field sources and livestock farming sources (TNδ for wheat field sources) as input. 15 N=5‰, TNδ from aquaculture 15 N=15‰ (detected by a stable isotope analyzer), and the contribution percentage is finally obtained by combining the weights of spectral peak intensity and amino acid proportion (each accounting for 50%).

[0065] Spectral detection error calculation: The average value of the 220nm peak intensity of the three parallel samples is 0.85, the standard deviation is 0.02, and the error = (standard deviation / average value) × 100% ≈ 2.35%, which meets the technical indicator of "spectral detection error ≤ ±3%".

[0066] Reproduction instructions: Those skilled in the art can reproduce this by following these steps: First, purchase the same equipment and reagents according to the "core equipment and reagent specifications" and apply them to a large-scale gravity-flow irrigation area in the Yellow River Basin (with a spring irrigation water diversion volume of 5 million m³). 3 / day, sandy soil) Select sampling points within the same latitude and longitude range as in this embodiment; second, strictly follow the "spectral calibration steps" (standard concentration, parameter adjustment method) and "DNA extraction-amplification steps" (reagent dosage, PCR program); third, use the IsoSource model, input the same TN / TP feature values ​​and weights as in the text, and you can get consistent pollution source contribution percentage results.

[0067] Example 3 This embodiment proposes a pollution source tracing and verification system combining spectral fingerprinting, amino acid analysis, and DNA analysis for emergency source tracing after torrential rains in hilly areas of subtropical monsoon climate zones. Specific implementation details include: Implementation Background (Supplementary Meteorological and Hydrological Data): The study area of ​​this embodiment is a hilly watershed in a subtropical monsoon climate zone, with a watershed area of ​​727 km². 2 The terrain is mainly low mountains and hills (slope 15-25°), with concentrated rainfall during the rainy season (June-August), often accompanied by short-duration heavy rainfall. On June 15, 2024, the area experienced a rainstorm, with meteorological station monitoring data showing a rainfall of 80 mm / 24h, a maximum rainfall intensity of 15 mm / h, and a rainfall duration of 10 hours. After the rainstorm, the COD concentration in the main river of the basin rose from 15 mg / L to 45 mg / L (exceeding the Class IV water standard). Emergency source tracing needed to be completed within 4 hours to identify the pollution sources of "farmland pesticides - urban domestic sewage" and locate the discharge points, providing a basis for emergency interception of sewage (the area has contiguous hilly and sloping farmland and towns distributed along the riverbanks, making it easy for rainstorms to cause mixed discharge of slope runoff and pipe networks).

[0068] System Configuration: Multi-media collaborative sensing module: A handheld electric water sampler (2-hour battery life, sampling depth 0-2m) is used to adapt to hilly and steep terrain (avoiding the safety risks of manual sampling). The sampling bottle is pre-filled with 1mL of sulfuric acid (1:5, v / v) to fix the COD water sample. Because the turbidity of the water sample reached 50NTU after the rainstorm (the soil clay content in the hilly area is 30%, and the silt content is high due to the rainstorm), centrifugation pretreatment (3000rpm, 10min) is required before spectral detection. The integration time of the spectrometer is set to 800ms to enhance the signal and ensure accurate detection of spectral peak intensity.

[0069] Multi-dimensional source tracing and analysis module: The rainfall-runoff pollution simulation module of the digital twin mapping unit takes into account rainstorm data (80 mm of rainfall, 15 mm / h of rainfall intensity) and soil data (30% clay content and 45% porosity of hilly slope soil) to simulate a slope runoff velocity of 0.5 m / s and a pollution migration distance of 2 km / h (the slope runoff in this area is fast and the pollution migrates rapidly). The amino acid determination criteria of the multi-source data analysis engine are: through a large number of sample tests in advance, it is determined that the proportion of glutamic acid in the farmland source samples in this area is 15%-20% (mainly from paddy field fertilization), and the proportion of L-leucine in the domestic source samples is 8%-12% (from protein degradation in urban domestic sewage), which serves as the basis for distinguishing pollution source categories.

[0070] Blockchain verification closed-loop module: Set the "Rainstorm Emergency" data priority label to increase the data upload and consensus confirmation speed by 50%, block generation time ≤ 5s, and ensure that parsing and verification are completed within 4 hours to meet the timeliness requirements of emergency traceability (pollution spreads rapidly after rainstorms in this area, and pollution interception measures need to be taken quickly).

[0071] Operation steps and results: Emergency sampling (June 15, 14:00-15:00): Five sampling points (L1-L5) will be set up at 1km intervals along the main stream of the basin (avoiding steep slopes and gullies to ensure sampling safety). Two surface water samples will be collected from each point (one for spectral and amino acid detection, and one for COD detection). The "sampling time-point-GPS coordinates" will be uploaded in real time via the APP to ensure data traceability.

[0072] Rapid analysis (15:00-17:00): Spectroscopic detection showed a 90% similarity between the characteristic peaks (230nm) of organophosphorus pesticides at points L3 and L4 (the hilly farmland in this area mainly grows rice and commonly uses organophosphorus pesticides), and an 87% similarity between the characteristic peaks (280nm) of protein in domestic sewage at points L1 and L2; amino acid detection showed that L-leucine accounted for 10.2% at point L1 (identified as a domestic source), and glutamic acid accounted for 16.8% at point L3 (identified as an agricultural source), thus preliminarily clarifying the distribution of pollution sources.

[0073] Retesting and Confirmation (17:00-18:00): The pesticide peak intensity deviation at point L4 was 16% (>15% of the preset threshold), and the system automatically started retesting; at 17:30, an investigation was conducted within 200m of point L4, and residual methamidophos (concentration 0.15mg / L, a commonly used pesticide in this area) was found in the paddy field; after investigation, point L1 was confirmed to be a mixed discharge of rainwater from a community's drainage network (domestic sewage accounts for 60% of COD, and the rainstorm caused the network to overflow due to overload). At 18:00, emergency source tracing was completed, and the environmental protection department was guided to take the "temporary interception dam for rainwater inlets" measure. 24 hours later, the COD of the water body dropped to 25mg / L.

[0074] Example 4 This embodiment proposes a pollution source tracing and verification system combining spectral fingerprinting, amino acids, and DNA for source tracing in the grassroots sampling of river networks in subtropical humid climate zones. Specific implementation details include: Implementation Background: The study area is a plain river network basin in a subtropical humid climate zone, with a river network density of 0.8 km / km. 2 The water flow is slow (0.1-0.2 m / s), and the surrounding area is scattered with paddy fields and vegetable fields (farmers operate separately, with each plot ranging from 0.5 to 2 mu in size). It is necessary to rely on farmers and township environmental protection officers to complete the sampling of the entire area, verify the effectiveness of the grassroots sampling data and the quality control methods, and solve the problem of "insufficient sampling coverage at the research end" (there are many tributaries in the river network in this area, and the research end can only cover the main stream, and pollution in the tributaries is easily missed).

[0075] System configuration and operation: Validation of lightweight sampling components at the grassroots level: 100 biodegradable sampling boxes were randomly selected. The pre-filled reagent volume error was ≤ ±0.05mL (meeting the "matching single test requirements"). The material degradation rate reached 80% after 6 months at 25℃ (the climate in this area is humid, the degradation rate is moderate, and it meets environmental protection requirements). The illustrated guide on the farmer's mobile APP includes key tips such as "avoid sampling in paddy fields within 24 hours after fertilization" and "keep vegetable field sampling away from pesticide spraying areas (pyrethroid pesticides are commonly used in vegetable fields in this area)". The quality control record on the environmental protection officer's end must include "sampling temperature (20-28℃, the average annual temperature in this area is 16-18℃, and the sampling period is in summer)" and "sample storage time (≤6 hours, high temperature can easily lead to amino acid degradation)".

[0076] Sampling and Analysis: 50 sets of grassroots sampling kits were distributed. 20 farmers collected runoff samples from paddy fields (mainly from paddy field drainage gates), and 30 environmental protection workers collected samples of vegetable garden drainage (open ditches around vegetable gardens) and domestic sewage (effluent from village-level sewage treatment plants). A total of 46 sets of qualified data were collected (qualification rate 92%). Of the 4 sets that failed, 2 sets were due to DNA degradation caused by storage time exceeding 8 hours (concentration <30ng / μL), and 2 sets were due to a 5% deviation in spectral peak intensity caused by sampling temperature exceeding 30℃ (affected by high afternoon temperatures in summer). AP P automatically marked "unqualified" and removed them to ensure data quality; the spectral matching results showed that the fertilizer characteristic peak (220nm, nitrate) of 22 paddy field water samples had a similarity of ≥85%, and the pesticide characteristic peak (260nm, pyrethroid) of 15 vegetable field water samples had a similarity of ≥86%, which met the indicator of "spectral matching accuracy rate not less than 85%", proving that the grassroots sampling data can be used for source tracing (after the grassroots sampling in this area covered the tributaries, the number of source tracing points increased from the original 10 to 60, and the coverage increased by 500%).

[0077] Example 5 This embodiment proposes a pollution source tracing and verification system combining spectral fingerprinting, amino acids, and DNA for the analysis of multi-source mixed pollution in watersheds. Specific implementation details include: Implementation Background: A mixed watershed containing contiguous farmland, large-scale livestock farms, and industrial wastewater treatment plants was selected, with a watershed area of ​​approximately 500 km². 2 The farmland is mainly planted with wheat and corn (fertilizer application rate of 15-20 kg / mu), the livestock farms are large-scale pig farms (each farm has more than 1,000 pigs), and the industrial wastewater treatment plant has a treatment capacity of 10,000 m³. 3 / day (the effluent standard is Class A, mainly receiving mixed wastewater from three surrounding industrial parks); it is necessary to verify the effect of "spectral fingerprint + amino acid + DNA" synergistic analysis on the separation of multi-source mixed pollution, and solve the problem of "large separation error of single technology" (the three types of pollution sources are concentrated in this area, and the pollutants are difficult to distinguish after being mixed by runoff).

[0078] System configuration and operation: Mixed sample preparation: Mixed samples were prepared according to the volume ratio of "agricultural source: livestock source: industrial wastewater treatment plant source = 3:4:3". Different concentrations of TN standard solution (5 mg / L for agricultural source, 20 mg / L for livestock source, and 12 mg / L for industrial wastewater treatment plant source) were added to simulate the actual mixed pollution scenario in the region (agricultural drainage, livestock wastewater, and industrial wastewater treatment plant effluent converge in the downstream of the watershed).

[0079] Collaborative analysis: Spectroscopic detection initially distinguished three types of pollution through characteristic peak matching (220nm nitrate peak from farmland, 450nm humic acid peak from livestock farming, and 280nm protein peak from industrial wastewater treatment plants, with matching accuracies of 88%, 90%, and 91%, respectively). Amino acid detection further subdivided the pollutants by identifying D-alanine (13.2% from livestock farming, a characteristic amino acid of pig manure), glutamic acid (17.5% from farmland, a degradation product of crop residues), and L-leucine (10.8% from industrial wastewater treatment plants, a protein degradation product in mixed domestic and industrial wastewater). Category; DNA detection locates emission sources by amplifying farm-specific genes (mitochondrial cytochrome b gene, matching similarity 97%) and characteristic bacterial genes (denitrifying denitrifying bacteria, matching similarity 96%) from farms (pigs); the final analysis shows a mixing ratio of "3.2:3.9:2.9", with an error of |(analyzed value - true value) / true value|×100%=7.2% (≤8%), while the analysis error of single spectral technology reaches 25% (it can only distinguish mixed pollution types, but cannot separate proportions), fully demonstrating the advantages of synergistic analysis.

[0080] Example 6 This embodiment proposes a pollution source tracing verification system combining spectral fingerprinting, amino acids, and DNA for blockchain-based source tracing guided by pollution liability determination. Specific implementation details include: Implementation Background: Taking a certain watershed exceeding the standard (a certain region, with a TN concentration of 15 mg / L, exceeding the Class III water standard, and the main pollution sources being large-scale livestock farms and rural domestic sewage, as well as two water-related industrial enterprises in the surrounding area) as an example, the environmental protection department of this region has incorporated pollution responsibility into the assessment system of township officials and enterprises. It is necessary to verify the connection effect between the blockchain-based evidence data and the local environmental protection assessment system to solve the problem of "no basis for pollution responsibility determination and easy to shirk responsibility" (previously, due to the lack of credible traceability data, livestock farms, industrial enterprises and townships often denied each other's pollution responsibility).

[0081] System configuration and operation: Data Integration: The consortium blockchain connects with the local environmental protection department's "Environmental Protection Assessment System" via a RESTful API interface. The data transmission format is JSON, and the field mapping relationship is as follows: "Responsible Entity" corresponds to "Enterprise / Township Number" (e.g., industrial enterprise number is ZW-GY01, livestock farm number is ZW-Y01, and township number is ZW-X03), and "Pollution Source Contribution Ratio" corresponds to "Pollution Reduction Assessment Weight" (5 points are deducted for every 10% increase in contribution ratio).

[0082] Responsibility determination process: Environmental personnel log into the assessment system, enter the source tracing verification list number (e.g., ZW20240601-001), and view the blockchain-stored "Sampling Data - Analysis Results - Contribution Ratio" (unalterable and traceable to specific samplers and testing equipment); based on the results in the list, such as "Industrial enterprise ZW-GY01 contributes 22% to TN, livestock farm ZW-Y01 contributes 42% to TN, and a certain township's responsibility for domestic sewage supervision accounts for 35%", an assessment score sheet is generated (industrial enterprise deducts 10 points, livestock farm deducts 20 points, and township deducts 15 points), which is included in the quarterly assessment; the entire process takes 1 day, which is 85% more efficient than the traditional paper record method (7 days), and the data can be used as a basis for punishment (an industrial enterprise and a livestock farm in the region took the initiative to assume pollution control responsibility due to the conclusive source tracing data, and invested 1.2 million yuan and 500,000 yuan respectively to upgrade wastewater treatment facilities), which meets the technical characteristics of "source tracing data connected to the environmental assessment system".

[0083] Example 7 This embodiment proposes a pollution source tracing and verification system combining spectral fingerprinting, amino acid analysis, and DNA analysis for energy consumption optimization at in-situ monitoring points in areas without power grid coverage. Specific implementation details include: Implementation Background: In an area on the edge of a large irrigation district without power grid coverage, the area has abundant sunshine (an average of more than 3,000 hours of sunshine per year) but is far from towns and cities. The cost of building a power grid is high. It is necessary to verify the stability of solar power + energy storage power supply at the in-situ monitoring point and the effect of stratified sampling to solve the problems of "difficult power supply to monitoring points in remote areas and inaccurate analysis of sediment pollution" (previously, the area relied on manual sampling once a month, which resulted in poor data timeliness and the inability to analyze sediment pollution in a stratified manner).

[0084] System configuration and operation: Energy consumption monitoring: 30 consecutive days of monitoring showed that the energy consumption at the monitoring point was 1.2 kWh / day on sunny days (sunlight ≥ 8h) (90% solar power supply, 70% in this area on sunny days), 0.8 kWh / day on cloudy days (sunlight 4-6h) (60% energy storage battery power supply), and 0.6 kWh / day on rainy days (sunlight < 4h) (100% energy storage battery power supply; when the battery is low, the sampling frequency is automatically reduced from 4h / time to 8h / time to avoid power outages). There were no power outages in 30 days, and the operation and maintenance costs were reduced by 40% compared to grid power supply (saving about 2,000 yuan in electricity costs per year).

[0085] Stratified sampling results: Stratified sampling devices were used to collect sediment samples from the 0-5cm surface layer and the 5-10cm deep layer. Tests showed that the DNA abundance in the surface sediment (120 ng / g) was 2.4 times that of the deep sediment (50 ng / g) (the surface sediment was significantly affected by recent pollution), and the spectral humic acid peak intensity (0.75 in the surface layer) was 2.3 times that in the deep layer (0.32), proving that the surface sediment is the main pollution storage layer. Based on this, the local environmental protection department formulated a "surface sediment dredging plan" (dredging depth 5cm). After dredging, the TN concentration in the drainage canal decreased from 12 mg / L to 8 mg / L, demonstrating significant treatment effects and meeting the technical characteristics of "stratified sediment sampling and pollution analysis".

[0086] Comparative Example 1 This comparative example provides a traditional solution that only uses spectral fingerprinting and amino acid tracing, including the following details: Technical solution: This comparative example uses the same spectral detection module and amino acid detection component as Example 2 (same model and parameters), but lacks DNA extraction and amplification device, and does not disclose the "weight calculation method for mixed source splitting" (only subjectively assigning weights based on spectral peak intensity). The study area is also a large gravity irrigation area in the Yellow River Basin (a certain region, with spring irrigation characteristics consistent with Example 2).

[0087] Comparison results: Insufficient analytical precision: It can only determine that "the contribution of livestock breeding sources to TN is 42%", but it cannot locate specific farms (there are 15 sheep farms in the middle reaches of the region), and each one needs to be investigated (which takes 7 days); the error in separating mixed sources is 18% (6.7% error in Example 2), which cannot be corrected due to the lack of DNA data, so the traceability results can only be used for "major category differentiation" and cannot guide precise law enforcement.

[0088] Application limitations: Environmental protection departments cannot specifically require farms to rectify their practices. From April 15th to 22nd, the TP concentration in the drainage ditch remained at 0.3 mg / L (exceeding the Class III water standard of 0.2 mg / L), and the pollution problem was not resolved in a timely manner. Due to the lack of specific steps in DNA extraction and amplification (such as primer sequences and amplification procedures), those skilled in the art cannot achieve the goal of "tracing responsibility to specific emission sources".

[0089] Comparative Example 2 This comparison provides a traditional solution with three types of data but no blockchain verification, specifically including: Technical solution: This comparative example uses the same spectral, amino acid, and DNA detection technologies as Example 6 (equipment model and reagent specifications are the same), but records "sampling data - analysis results" in paper forms, without hash storage and blockchain nodes, and does not disclose "data query and public supervision interface". The study area is the same super-standard watershed (a certain region, with assessment requirements the same as Example 6).

[0090] Comparison results: Low data credibility: Both an industrial enterprise and a farm ZW-Y01 denied emissions, claiming that "the sampling data was modified" (the paper records have no anti-counterfeiting marks and may be tampered with). Because there was no tamper-proof evidence, the environmental protection department could not verify the authenticity of the data, and the determination of responsibility was stalled for 3 days.

[0091] Insufficient efficiency and credibility: It took 7 days from sampling to issuing the assessment results (only 1 day in Example 6), of which 3 days were spent organizing three agencies to verify the paper data (sampler, tester, enterprise representative, and farm representative jointly reconciled the accounts); surrounding farmers filed 3 complaints because they could not find the traceability results, and the environmental protection department had to invest additional manpower to explain, which damaged its credibility; those skilled in the art could not achieve a closed-loop control system of "data immutability + public supervision", which did not meet the requirements and had defects.

[0092] Compared with Examples 1-7 and Comparative Examples 1-2, Examples 1-7 construct a full-chain technology system of "spectral fingerprint + amino acid + DNA + blockchain". Comparative Example 1 lacks the DNA extraction and analysis link, and Comparative Example 2 lacks the blockchain evidence storage module. The three examples differ significantly in terms of traceability accuracy, emission source location, data credibility and scenario adaptability. The technical integrity of Examples 1-7 is significantly better than that of the two control groups.

[0093] Regarding the accuracy of source tracing, Example 1 (industrial park) used three technologies in synergy to pinpoint a single enterprise's discharge outlet within 14 hours, achieving 100% accuracy in locating the source of excessive nitrochlorobenzene. Example 2 (large irrigation area) showed a 6.7% error in the separation of mixed sources and a 2.35% error in spectral detection. Example 5 (multi-source mixed watershed) showed a 7.2% separation error, while Comparative Example 1, relying solely on spectroscopy and amino acids, showed an error of 18%-25% for similar sources, as the lack of DNA correction prevented the correction of deviations. Example 3 (hilly area rainstorm emergency response) relied on the synergy of amino acids and DNA to identify the pollution type and location within 4 hours, while Comparative Example 1 could only distinguish between the broad categories of farmland and domestic sources, failing to refine the pollution proportion.

[0094] In terms of locating emission sources, Example 1 used specific DNA of halophilic thermophilic bacteria (matching similarity 98.2%) to locate the emission outlet of workshop A of enterprise; Example 2 used specific DNA of sheep manure (matching similarity 97.2%) to locate the farm; Example 8 was accurate to a single farmland area; Comparative Example 1, due to the lack of DNA technology, required the investigation of 15 farms in the irrigation area (taking 7 days), and in the hilly scene, it was impossible to locate the specific emission point, missing the opportunity for emergency interception of pollution.

[0095] Regarding data credibility, Example 6 (responsibility determination) uses blockchain-based evidence data to connect with the assessment system, completing the responsibility allocation within one day, and the data is tamper-proof; Example 7 (areas without power grids) uploads monitoring data hashes to ensure the credibility of the traceability results. Comparative Example 2 uses paper records, resulting in situations where industrial enterprises and farms denied emissions, and farmers filed three complaints. The responsibility determination stalled for three days, and there was no public oversight interface, leading to insufficient public credibility.

[0096] In terms of scenario adaptability, Examples 1-7 cover seven types of scenarios, including industrial parks, irrigation areas, hills, river networks, and areas without power grids. For example, Example 7 uses solar energy and energy storage to achieve monitoring in remote areas, and Example 4 uses a grassroots sampling box to cover tributaries of plain river networks. Comparative Example 1 cannot separate the three types of pollution in multi-source mixed watersheds, and Comparative Example 2 is difficult to implement in the scenario of liability determination due to unreliable data. Both have limitations in scenario adaptability.

[0097] In summary, Comparative Example 1 suffers from "difficulty in location and low accuracy" due to the lack of DNA, while Comparative Example 2 suffers from "fake data and slow identification" due to the lack of blockchain. Examples 1-7, through multi-technology collaboration and closed-loop design, completely solve the above pain points, fully demonstrating that this system provides a complete technical solution for accurate source tracing of industrial and agricultural non-point source pollution.

[0098] The above embodiments can be implemented in whole or in part by software, hardware, firmware or other arbitrary combinations. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product.

[0099] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0100] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0101] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of protection of the claims.

[0102] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A pollution source tracing and verification system combining spectral fingerprinting, amino acid analysis, and DNA analysis, characterized in that, It includes a multi-media collaborative sensing module, a multi-dimensional traceability and analysis module, and a blockchain verification closed-loop module. Each module is linked together through data transmission and edge processing technologies. The multi-media collaborative sensing module is used to collect spectral fingerprint, amino acid and DNA feature data in water bodies. The spectral fingerprint data is obtained through in-situ online detection or rapid on-site detection, while the amino acid and DNA feature data are obtained through laboratory analysis. The multi-dimensional source tracing and analysis module is used to integrate spectral fingerprint, amino acid and DNA data to achieve pollution source tracing and verification. The spectral fingerprint is obtained and matched to identify the pollution type through in-situ online detection. The amino acid composition characteristics are obtained through laboratory analysis to distinguish the pollution source category. The DNA feature sequence is obtained through laboratory analysis to locate the specific pollution emission point. Combined with the migration simulation results, a pollution source tracing report is output. The blockchain verification closed-loop module is used to realize the trusted storage of traceability data and the closed-loop application of verification results, generate an immutable traceability verification list, and verify and optimize the traceability results through a retesting and comparison mechanism. This technology utilizes a progressive synergy of spectral fingerprinting, amino acids, and DNA to achieve three levels of source tracing, from identifying pollution types to distinguishing pollution source categories and locating specific emission points.

2. The pollution source tracing and verification system combining spectral fingerprinting, amino acids, and DNA as described in claim 1, characterized in that, The multi-media collaborative sensing module includes a scientific research-grade integrated sampling terminal, a lightweight sampling component at the grassroots level, an in-situ intelligent monitoring point, an industrial wastewater sampler, and a laboratory analysis unit. The research-grade integrated sampling terminal is equipped with a portable spectral detection module and a sample collection and preservation component. The sample collection and preservation component is equipped with a spectral detection buffer and a DNA stabilizer, which can acquire water spectral fingerprints on site and collect and preserve water samples for amino acid analysis and DNA extraction for laboratory testing. The spectral detection module includes a miniature calibration component that integrates nitrogen and phosphorus standards, humic acid characteristic fluorescent substances, and industrial characteristic pollutant standards. It is used to calibrate the peak position and intensity of the spectrometer on-site, so that the spectral fingerprint detection error is controlled within ±3%.

3. The pollution source tracing and verification system combining spectral fingerprinting, amino acids, and DNA as described in claim 2, characterized in that, The lightweight sampling component for basic applications includes a biodegradable sampling box pre-loaded with protective reagents and a mobile application with QR code recognition functionality. The protective reagents include a DNA stabilizer, and the reagent dosage in the biodegradable sampling box is matched to the needs of a single test. The mobile application is divided into three types based on the sampling subject: farmer's end, enterprise environmental protection officer's end, and supervisor's end. The enterprise environmental protection officer's end adds a function to record industrial wastewater characteristic parameters, and the supervisor's end adds a function to record data quality control parameters. The data quality control parameters include sampling environment temperature and sample storage time.

4. The pollution source tracing and verification system combining spectral fingerprinting, amino acids, and DNA as described in claim 2, characterized in that, The in-situ intelligent monitoring point is equipped with a stratified sampling device, a multi-parameter sensor, and an in-situ spectral detection unit. The stratified sampling device collects surface water samples from a depth of 0 to 5 cm and deep water samples from a depth of 5 to 10 cm, respectively. The multi-parameter sensor simultaneously detects water temperature, pH value, and dissolved oxygen content. The in-situ spectral detection unit collects water spectral fingerprint data in real time to assist in correcting spectral fingerprint detection values. The in-situ intelligent monitoring points are deployed at industrial discharge outlets, river sections, or lake / reservoir locations, powered by solar energy, and automatically collect water samples every four hours.

5. The pollution source tracing and verification system combining spectral fingerprinting, amino acids, and DNA as described in claim 1, characterized in that, The multi-dimensional source tracing and analysis module includes a digital twin mapping unit and a multi-source data analysis engine; The digital twin mapping unit is constructed according to the actual scale of the watershed, and maps the data collected by the multi-media collaborative sensing module in real time to simulate the migration characteristics and paths of spectral fingerprints, amino acids and DNA in the aquatic environment. The digital twin mapping unit is configured with corresponding simulation modules for four types of scenarios: industrial parks, irrigation areas, hilly areas and plain river networks. The industrial park corresponds to the park drainage pipe network simulation module, which is used to simulate the transmission time delay and mixing characteristics of industrial wastewater in the enterprise's internal pipe network and the park's main pipe network. The multi-source data analysis engine is equipped with an industrial characteristic pollutant spectral library, an industrial source amino acid fingerprint library, and an industrial microbial DNA fingerprint library.

6. The pollution source tracing and verification system combining spectral fingerprinting, amino acids, and DNA as described in claim 5, characterized in that, The spectral fingerprint matching function of the multi-source data analysis engine identifies typical pollution types, including industrial characteristic pollutants, pesticides, fertilizers, livestock and poultry manure, and domestic sewage, by comparing the peak position and peak intensity of the sample spectrum with the characteristic pollutant spectral library, with a matching accuracy of no less than 85%.

7. The pollution source tracing and verification system combining spectral fingerprinting, amino acids, and DNA as described in claim 5, characterized in that, The amino acid composition feature analysis function of the multi-source data parsing engine detects the relative content of characteristic amino acids in the sample, analyzes the relative content and ratio of characteristic amino acids through a preset amino acid discrimination model, and outputs the pollution source category determination result.

8. The pollution source tracing and verification system combining spectral fingerprinting, amino acids, and DNA as described in claim 5, characterized in that, The DNA feature sequence tracing function of the multi-source data analysis engine amplifies microbial-specific gene fragments in the sample and compares them with the DNA fingerprint database of known emission sources, including industrial enterprises, livestock and poultry farms, domestic sewage treatment plants and farmland areas. When the matching similarity is higher than 95%, the specific pollution emission point is located, and the source tracing accuracy is down to a single enterprise discharge outlet, a single breeding farm, or a single farmland area.

9. The pollution source tracing and verification system combining spectral fingerprinting, amino acids, and DNA as described in claim 1, characterized in that, The blockchain verification closed-loop module includes a two-layer evidence storage chain and a verification-feedback unit; The dual-layer evidence storage chain includes a consortium chain and a public supervision chain. The consortium chain stores spectral fingerprints, amino acid detection results, DNA sequence data, and hash values ​​of the parsing process, generating an immutable traceability verification list. The public oversight chain is used to upload the anonymized verification results and supports the public in querying traceability information and reporting suspected contamination points through the application; The source tracing verification list includes sampling time, sampling location, key parameters of spectral fingerprint, raw data of amino acid detection, DNA sequence comparison results, analysis basis, and information on responsible entities. The responsible entities include industrial enterprises, farmland management entities, and aquaculture enterprises. This list is connected to the local environmental protection supervision system as the basis for assessing the effectiveness of pollution control and determining pollution responsibility.

10. The pollution source tracing and verification system combining spectral fingerprinting, amino acids, and DNA as described in claim 1, characterized in that, The preset threshold of the verification-feedback unit is 15%; When the deviation between the retested spectral characteristic value and the source tracing report exceeds 15%, or the amino acid concentration deviation exceeds 15%, the scientific research-grade integrated sampling terminal will be automatically activated to retest and sample key areas, additional water samples will be sent to the laboratory for DNA characteristic sequence comparison and verification, and the multi-source data analysis engine will be called to regenerate the pollution source tracing verification results.

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