A polycyclic aromatic hydrocarbon early warning system and method suitable for a coking coal catchment

Abstract

This application provides a method for early warning of polycyclic aromatic hydrocarbons (PAHs) in coking coal basins, relating to the field of environmental ecology technology. The invention includes the following steps: selecting first to third-level sub-basins along the main stream based on the topography of the coking coal basin, collecting water samples and surface sediments; determining the concentration and composition of PAHs, and qualitatively analyzing the pollution sources of PAHs through the ratio distribution between PAH isomers; selecting representative water and sediment samples for metagenomic sequencing to obtain information on the structure and functional genome composition of microbial communities; conducting ecological risk assessment of PAHs using the risk entropy method; and providing early warning of the ecological risk of PAHs in the coking coal basin. This invention organically couples the spatiotemporal distribution of PAHs, the ecological risk assessed by chemical methods, and the structural and functional responses of microbial communities to construct a spatial pollution identification system encompassing surface water, sediment, and land use information, which helps improve the ability to analyze coal-coke composite pollution.

Description

Technical Field

[0001] This invention relates to the field of environmental and ecological technology, and in particular to a polycyclic aromatic hydrocarbon (PAH) early warning system and method applicable to coking coal basins. Background Technology

[0002] Polycyclic aromatic hydrocarbons (PAHs), typical persistent organic pollutants in coal char wastewater, are highly toxic, difficult to degrade, and bioaccumulate, easily remaining in water bodies and sediments for extended periods, leading to water quality deterioration, benthic system collapse, and microecological dysfunction. Studies have shown that in coal mining areas, PAHs can be emitted through coal combustion, dry and wet deposition of atmospheric particulate matter, raw coal emissions, and the use of machinery and transportation during coal mining, resulting in higher PAH pollution levels and environmental risks in mining areas compared to other environmental functional zones.

[0003] Organic compounds, typically represented by polyphenolic compounds (PAHs), are released into the environment during coal mining and use, polluting water bodies and soil. While coking plantations are concentrating their discharge points and wastewater treatment capacity has improved, the multi-pathway discharge of PAHs and insufficient water dilution capacity mean that watershed ecological risks remain a long-term concern. Current pollution identification and risk assessment methods are largely based on single pollutant concentration thresholds and acute toxicity indicators, which fail to reflect complex pollution processes and the true responses of ecosystems. Microorganisms, as the earliest indicators of pollution response in aquatic ecosystems, possess significant early warning potential due to changes in their community structure and functional genes. However, in the context of PAH pollution, systematic research is still lacking on the perturbation mechanisms of key functional genes, the identification of ecological thresholds, and their dynamic coupling with pollutants. Summary of the Invention

[0004] In view of this, the purpose of this invention is to propose a polycyclic aromatic hydrocarbon (PAH) early warning system and method applicable to coking coal basins. Ecological disturbances in coking coal basins have significant "functional sensitivity-community lag" characteristics. This invention aims to construct a risk early warning indicator system based on response thresholds and key gene imbalance signals, starting from the dynamic correlation between pollutants, functional genes, and ecological effects, so as to achieve hierarchical management and control of ecological risks.

[0005] The technical solution of this invention is implemented as follows: A method for early warning of polycyclic aromatic hydrocarbons (PAHs) in coking coal basins, characterized by comprising the following steps: Step 1: Based on the topography of the coking coal basin, select first to third-level sub-basins along the main stream and collect water samples and surface sediments; including measuring the physicochemical indicators in the water samples and the content of organic pollutants in the sediments, and monitoring the water body including salinity indicators; Step 2: Determine the concentration and composition of PAHs, and conduct a qualitative analysis of the pollution sources of PAHs by analyzing the ratio distribution between PAH isomers; Step 3: Select representative water and sediment samples to conduct metagenomic sequencing to obtain information on the structure and functional genome composition of the microbial community, including microbial community composition analysis, environmental-microbial community structure association analysis, and species-functional contribution analysis. Step 4: Conduct an ecological risk assessment of polycyclic aromatic hydrocarbons using the risk entropy method; Step 5: Ecological risk warning of polycyclic aromatic hydrocarbons in the coking coal basin.

[0006] Furthermore, in step 2, the ratio distribution between PAH isomers is as follows:

[0007] Furthermore, in step 1, first to third-level sub-basins are selected along the main stream, and sampling points are set up upstream, inlet and downstream of the pollution source; the sampling points include upstream background reference area, typical sewage outlet adjacent area, pollution plume diffusion area, multi-source pollution confluence section, sediment accumulation area and ecologically sensitive response area.

[0008] Furthermore, in step 1, the physicochemical indicators include total organic carbon, anions, chemical oxygen demand, total nitrogen, ammonia nitrogen, and total phosphorus content, and the water body is monitored for indicators including pH value, dissolved oxygen, conductivity, temperature, and salinity.

[0009] Furthermore, in step 2, the pollution source analysis of PAHs also includes the relative abundance analysis of PAHs with different ring numbers. High molecular weight 4-6 ring PAHs mainly come from the combustion of fossil fuels under high temperature environment and can be used as a characteristic indicator of vehicle exhaust emissions or coal combustion emissions. Low molecular weight 2-3 ring PAHs mainly represent petroleum source input. By analyzing indicators of spatial distribution and source type, the pollution sources of different sampling points can be obtained, and different governance strategies can be adopted for different regions in subsequent pollution control.

[0010] Furthermore, in step 3, the microbial community composition analysis selects the microbial genera with the highest abundance (5%–15%), including constructing a Z-score normalized clustering heatmap, assessing the impact of environmental gradients on the shaping of microbial community structure, and using LDA Effect Size analysis to identify differential indicator species. The analysis of the relationship between the environment and the microbial community structure includes the use of Spearman correlation analysis and redundancy analysis to obtain the influence relationship between sediment microbial community structure and environmental factors, and to construct a PAH pollution biomonitoring system based on microbial response.

[0011] Furthermore, in step 3, by analyzing the relationship between microbial community structure, functional genes and environmental factors, the abundance of species in the dominant bacterial community is used as a specific biological indicator for early warning or a resource-utilizing species. Alternatively, by analyzing the relationship between microbial community structure and PAH pollution, the microbial community can be divided into three functional response types: positive response groups, sensitive response groups, and neutral groups. The decrease in the abundance of sensitive response groups can be used as a biological indicator signal of PAH pollution.

[0012] Furthermore, in step 4, the formula for calculating the risk entropy value of a monomeric PAH is as follows:

[0013]

[0014] In the formula: C i C represents the content of PAH monomer i in a certain environmental medium. NCs,i C represents the negligible risk standard value for type i PAH in the corresponding environmental medium. MPCs,i This represents the highest permissible risk standard value for type i PAH in this medium; The ecological risk level classification for individual PAHs is determined as follows: RQ i(MPCs) ≥1 indicates high risk; RQ i(NCS) ≥1 but RQ i(MPCs) <1 indicates medium risk; Total risk entropy values ​​of 16 PAHs:

[0015]

[0016] In the formula: i represents the body type of the 16 PAHs monomers; RQ NCs,∑PAHs With RQ MPCs,∑PAHs These represent the total negligible ecological risk level and the maximum permissible ecological risk level for the 16 PAHs, respectively. The ecological risk levels of 16 PAHs are classified according to RQ. NCs,ΣPAHs and RQ MPCs,ΣPAHs The following is a comprehensive assessment based on the following circumstances: RQ MPCs,ΣPAHs ≥1 and RQ NCs,ΣPAHs A value of ≥800 is considered high risk.

[0017] The 16 PAHs mentioned in this invention mainly refer to PAHs with strong carcinogenicity, which are listed as priority pollutants by the U.S. Environmental Protection Agency (EPA) and the European Union, as follows:

[0018] Furthermore, in step 5, the ecological risk of ∑PAHs is quantified by the risk quotient method to determine the impact of PAHs residues on the site; the impact of pollution on the micro-ecosystem is assessed by analyzing the response of the microbial community to PAHs pollution, and a quantitative response relationship between pollutant concentration and microbial function is constructed.

[0019] This invention provides a polycyclic aromatic hydrocarbon (PAH) early warning system suitable for coking coal basins, comprising a microbial indicator species abundance response module, a PAHs ecological risk entropy value early warning module, and an organic pollutant concentration early monitoring module, wherein the organic pollutants include dioxins.

[0020] In one embodiment of the invention, typical rivers, ditches, and artificial water bodies surrounding a coal coking industrial park in the middle reaches of the Yellow River are selected as examples. Monitoring points are set up upstream, at the confluence of pollution sources, and downstream, and water samples and surface sediments are collected. Using instrumental analysis methods such as GC-MS, the concentrations and compositions of 16 key pollutant-controlled pollutants (PAHs) are determined, and spatial distribution maps of the pollutants are drawn to identify high-pollution areas and potential risk hotspots within the region. Furthermore, by combining PAH ratio methods with land use information, the types of pollution sources and their spatial diffusion and environmental fate pathways are analyzed, providing a scientific basis for ecological risk identification and source control.

[0021] Metagenomic sequencing was conducted on representative water and sediment samples from typical pollution sites to obtain information on the microbial community structure and functional gene composition. Functional genes related to PAH degradation (such as aromatic hydroxylases) were identified, and their abundance distribution and expression characteristics were analyzed. The impact of pollution on the micro-ecosystem was assessed using community α / β diversity indices, and quantitative response relationships between pollutant concentration and microbial function were constructed using multivariate statistical methods (such as RDA and PLS-DA) to identify the ecosystem's response threshold and tolerability boundary. This study provides support for risk identification and early warning indicator screening at the functional gene level.

[0022] Compared with the prior art, the beneficial effects of the present invention are as follows: Taking the coal and char coke basin in the middle reaches of the Yellow River as an example, this invention organically couples the spatiotemporal distribution of PAHs, the ecological risks assessed by chemical methods, and the structural and functional responses of microbial communities through spatial identification of pollutants, metagenomic analysis of microorganisms, and multidimensional modeling analysis. This constructs a spatial pollution identification system covering surface water, sediment, and land use information, which helps to improve the ability to analyze coal and char coke compound pollution and can provide theoretical basis and technical support for regional water environment hierarchical management, pollution risk identification, and ecological compensation mechanisms. Attached Figure Description

[0023] Figure 1 This is a flowchart of the polycyclic aromatic hydrocarbon early warning method applicable to coking coal basins according to the present invention; Figure 2 This is a point distribution map of Example 1; Figure 3 Spatial distribution map of PAHs in sediments; Figure 4 To detect the percentage of PAHs with different ring numbers at different sites; Figure 5 Z-score normalized clustering heatmaps were constructed for the top 35 most abundant microbial genera. Figure 6 To determine the differential indicator species map for LDA Effect Size analysis in different groups; Figure 7 Spearman correlation diagram of environmental factors and sediment microbial community; Figure 8 Redundancy analysis diagram of environmental factors and sediment microbial community; Figure 9 Mantel test network diagram of environmental factors and sediment microbial community; Note: Group A (Clean Background Area, D1, D2), Group B (Industrial Core Area, D4, D7), Group C (Multi-Source Mixed Area, D8, D10, D12, D13) and Group D (River Ecological Response Area, D3, D5, D6, D9, D11, D14, D15). Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0025] Unless otherwise specified, the experimental methods used in the embodiments of this invention are all conventional methods.

[0026] Unless otherwise specified, all materials and reagents used in the embodiments of this invention are commercially available.

[0027] TOC detection method: The total organic carbon (TOC) in water is determined using a total organic carbon analyzer. A series of TC and IC standard solutions of different concentrations are prepared, and 1000 ppm of each TC and IC standard solution is used to prepare gradient solutions of different concentrations. After preheating, a standard curve template is established, and measurements are performed using 25 mL of the gradient solutions to obtain the standard curve.

[0028] Anion detection method: The main anions detected include chloride ions and nitrate ions. The instrument used is an ion chromatograph from Qingdao Shenghan Chromatography Co., Ltd.

[0029] COD detection method: Chemical Oxygen Demand (COD) is measured using an ultraviolet spectrophotometer. When testing water quality, special consumables are required. Here, we are using COD consumables from Lianhua Technology. The consumable solutions D and E are prepared according to the instructions.

[0030] Total nitrogen detection method: Total nitrogen is measured using an ultraviolet spectrophotometer with total nitrogen consumables from Lianhua Technology.

[0031] Ammonia nitrogen detection method: Ammonia nitrogen is measured using an ultraviolet spectrophotometer and ammonia nitrogen consumables from Lianhua Technology.

[0032] Total phosphorus detection method: Total phosphorus is measured using an ultraviolet spectrophotometer with total phosphorus consumables from Lianhua Technology.

[0033] A method for early warning of polycyclic aromatic hydrocarbons (PAHs) in coking coal basins comprises the following steps: Step 1: Sampling Point Deployment: Based on the topography of the coking coal basin, select first to third-level sub-basins along the main stream and deploy sampling points. Points will be set up in non-coal and coke-affected areas for background reference, potential emission points for typical pollutants, multi-source confluence areas and tributary inlets, and sediment enrichment sections. Water samples and surface sediments will be collected, including measurements of total organic carbon, anions, chemical oxygen demand, total nitrogen, ammonia nitrogen, and total phosphorus in the water samples, and organic pollutant content in the sediments. Water pH, dissolved oxygen, conductivity, temperature, and salinity will also be monitored. Step 2: Source analysis of polycyclic aromatic hydrocarbons (PAHs): Determine the concentration and composition of PAHs and obtain a spatial distribution map of PAHs; The pollution sources of PAHs are qualitatively analyzed using the characteristic ratio method. The characteristic ratio method is a method for qualitative analysis of the pollution sources of PAHs. By analyzing the ratio distribution between specific PAH isomers, the sources of PAHs can be classified as rock, liquid fuel combustion (gasoline, diesel), and coal and biomass combustion. The source of PAHs can also be obtained from the relative abundance analysis of PAHs with different ring numbers. High molecular weight 4-6 ring PAHs mainly come from the combustion of fossil fuels under high temperature environment and can be used as a characteristic indicator of vehicle exhaust emissions or coal combustion emissions. Low molecular weight 2-3 ring PAHs mainly represent petroleum source input. The spatial distribution and source type indicators together reveal the pollution sources at different locations. Based on the analysis results, different governance strategies should be adopted for different regions in future pollution control. For example, in one embodiment of the present invention, the sources of PAHs are spatially distributed in the north and single in the south. Source reduction strategies should be adopted for combustion hotspots in the south, while for estuarine mixed areas, watershed management and air pollution prevention and control should be coordinated and multi-media collaborative governance should be implemented.

[0034] Step 3: Analysis of microbial response mechanisms: Metagenomic sequencing was conducted on representative water and sediment samples from typical pollution sites to obtain information on the structure and functional genome composition of the microbial community; α-diversity analysis of the microbial community was also performed.

[0035] Based on the relative abundance analysis of species at the phylum and genus levels, the overall skeletal characteristics of sediment microbial communities were obtained.

[0036] Z-score-normalized clustering heatmaps were constructed by selecting the top 5%–15% of microbial genera in abundance to analyze the shaping effect of environmental gradients on micro-community structure.

[0037] LDA Effect Size analysis was used to identify differential indicator species in different groups.

[0038] By combining Spearman correlation analysis and redundancy analysis, the relationship between sediment microbial community structure and environmental factors was obtained.

[0039] Based on the Spearman correlation between microbial taxa and PAH abundance, the community was divided into three functional response types: positive responders, sensitive responders, and neutral taxa, and a PAH pollution biomonitoring system based on microbial response was constructed.

[0040] Step 4: Ecological risk assessment of polycyclic aromatic hydrocarbons (PAHs): The risk entropy method characterizes risk by calculating the ratio of the measured environmental concentration to the predicted ineffective concentration. This method is simple to calculate and provides intuitive results, making it commonly used for water body risk assessment. For sedimentary media, the sediment quality benchmark method is considered the gold standard for risk assessment. Commonly used benchmark values ​​include the low and middle values ​​of the effective range. If the concentration is below the ERL, negative biological effects are generally considered rare; if it is above the ERM, they are considered frequent.

[0041] In recent years, GC-MS, PAHs ratio methods, and pollution spectrum analysis have been used to identify pollutant distribution and sources. However, given the complex pollution background of multiple discharge outlets, multiple time periods, and multiple components in the coal and char coke basin of the middle reaches of the Yellow River, the accuracy and explanatory power of pollution identification remain insufficient. Furthermore, integrated spatial identification studies on the environmental fate processes of PAHs in sediments are still lacking. Therefore, constructing a spatial pollution identification system encompassing surface water, sediment, and land use information is a key technological breakthrough direction for improving the ability to analyze complex coal and char coke pollution.

[0042] The calculation formula is as follows: Monomer PAH risk entropy value:

[0043]

[0044] In the formula: C i C represents the content of PAH monomer i in a certain environmental medium. NCs,i C represents the negligible risk standard value for type i PAH in the corresponding environmental medium. MPCs,i Let i be the highest permissible risk standard value for PAHs in this medium, where C is the C value of each individual PAH. NCs and C MPCs The values ​​are shown in Table 1.

[0045] Table 1. C content of 16 PAHs in soil medium NCs and C MPCs Value (mg / kg)

[0046] Total risk entropy values ​​of 16 PAHs:

[0047]

[0048] In the formula: i represents the body type of the 16 PAHs monomers; RQ NCs,∑PAHs With RQ MPCs,∑PAHs These represent the total negligible ecological risk level and the maximum permissible ecological risk level for the 16 PAHs, respectively.

[0049] The standards for classifying ecological risk levels are shown in Table 2.

[0050] Table 2. Ecological Risk Classification of PAHs Using the Risk Entropy Value Method

[0051] The ecological risk of ∑PAHs is quantified by evaluating the individual ecological risks of PAHs.

[0052] Individual ecosystem risk is determined by its risk quotient (RQ).i = C i / C) Judgment: RQ i(MPCs) ≥1 indicates high risk; RQ i(NCS) ≥1 but RQ i(MPCs) A score less than 1 indicates a medium risk.

[0053] Overall ecological risk is based on RQ NCs,ΣPAHs and RQ MPCs,ΣPAHs The overall situation is considered for judgment: when RQ MPCs,ΣPAHs ≥1 and RQ NCs,ΣPAHs A value of ≥800 is considered high risk.

[0054] Step 5: Ecological risk warning of polycyclic aromatic hydrocarbons in the coking coal basin: Based on the topography, the coking coal basin was divided into upper, middle and lower reaches. Water and sediment samples were collected from the upper background reference area, the area near typical sewage outlets, the pollution plume diffusion area, the multi-source pollution confluence section, the sediment accumulation area and the ecologically sensitive response area, respectively.

[0055] The concentration and composition of PAHs in the watershed were determined, a spatial distribution map of pollutants was drawn, and high-pollution areas and potential risk hotspots in the region were identified. Furthermore, by combining the PAHs ratio method with land use information, the source categories of PAH pollution and their spatial diffusion and environmental fate pathways were analyzed.

[0056] Based on the response analysis of microbial communities to PAH pollution, the impact of pollution on micro-ecosystems is assessed, a quantitative response relationship between pollutant concentration and microbial function is constructed, and the response threshold and tolerance boundary of the ecosystem are identified, providing early biological indicators for ecological risk assessment.

[0057] The risk quotient method is used to quantify the ecological risk of ∑PAHs (polycyclic aromatic hydrocarbons) and determine the extent of the impact of PAH residues on the site. Differential management based on risk assessment has significant scientific guiding significance for balancing regional resource development and public health protection.

[0058] Example 1 In a specific embodiment of the present invention, a method for early warning of polycyclic aromatic hydrocarbons (PAHs) in the coking coal industrial park in the middle reaches of the Yellow River is discussed, using typical rivers, ditches, and artificial water bodies around the coking coal basin as sample areas. Step 1: Sampling Point Deployment. Given the numerous tributaries of the Yellow River, 22 sub-basins (levels one to three) were selected along the main stream from each of the upper, middle, and lower reaches. In the coal and coke industry cluster area of ​​the middle reaches of the Yellow River, 15 monitoring points were established. These points, based on spatial function differences, are located in the upstream background reference area, the area adjacent to typical discharge outlets, the pollution plume diffusion area, the multi-source pollution confluence section, the sediment accumulation area, and the ecologically sensitive response area. The system comprehensively covers the entire process of pollution generation, migration, deposition, and ecological impact, possessing advantages such as good spatial representativeness, comprehensive coverage of pollution processes, and obvious gradient characteristics. See Table 3 for the specific point selection table, and see the point diagram below. Figure 2 .

[0059] Table 3 Location Selection Table

[0060] A 1 L sample of water from the middle layer of a 0.5 m aquifer was collected and placed in a pre-washed brown glass bottle. The pH of the water sample was adjusted to 2.0 by acidification and stored in the dark at low temperature. Pretreatment was completed within 48 h. The specific pretreatment procedure was as follows: Activation was performed using a 5 mL methanol and ultrapure water equilibrium column. 100 mL of water sample was slowly loaded at a flow rate of 5 mL / min. The column was washed with 10 mL of ultrapure water to remove irrelevant impurities. The column was evacuated under vacuum for 2 h. The solid-phase extraction column was rinsed with 10 mL of methanol to obtain the eluent. The solvent was then blown down to 1.0 mL with stable and dry nitrogen gas to obtain the sample to be tested.

[0061] Top-layer sediment samples (0-10 cm) were collected using a stainless steel handheld mud sampler and transported under refrigeration on-site. In the laboratory, the samples were freeze-dried, ground, and sieved through a 100-mesh sieve. 50 g of the dried sample was weighed and extracted using 40 kHz ultrasonic-assisted extraction for 30 min, followed by centrifugation at 6000 rpm for 10 min. The supernatant was then filtered through a 0.22 μm organic filter membrane, and the solvent was purged to 1.0 mL with stable, dry nitrogen gas to obtain the sample for testing.

[0062] The system measures total organic carbon, anions, chemical oxygen demand, total nitrogen, ammonia nitrogen, and total phosphorus content, and monitors water pH, dissolved oxygen, conductivity, temperature, and salinity.

[0063] After systematic analysis of 15 sediment samples (SD1-SD15, corresponding to the adjacent riparian zones of water sites D1-D15 respectively), dozens of semi-volatile organic pollutants covering 8 major categories were detected. PAHs were mainly distributed at four sites: SD7, SD9, SD13 and SD14, while they were not detected at the other sites.

[0064] In addition, dibenzofurans, which have dioxin-like structural characteristics, were detected in some samples. Dibenzofurans are usually associated with incomplete combustion and the co-generation of dioxin-like substances during high-temperature combustion, coking, or waste incineration. The co-detection of dibenzofurans with PAHs in SD7 and SD9 indicates that coal and coke industry activities, while producing PAHs, also generate small amounts of dioxin-like organic pollutants. This substance can serve as an "early warning" indicator of soil organic pollution in coal and coke industrial areas for subsequent ecological risk assessment and long-term environmental trend monitoring.

[0065] Step 2: Analysis of the sources of polycyclic aromatic hydrocarbons (PAHs): 1) The characteristic ratio method is used to conduct a qualitative analysis of the pollution sources of PAHs. By the ratio distribution between specific PAH isomers, the sources of PAHs can be divided into rock, liquid fuel combustion (gasoline, diesel) and coal and biomass combustion, as shown in Table 4.

[0066] Table 4. Ratios of PAH source characteristics

[0067] Note: HMW represents high molecular weight PAHs (4-6 rings), and LMW represents low molecular weight PAHs (2-3 rings).

[0068] In this embodiment, the Fla / (Fla+Pyr) ratios of SD7 and SD13 are 0.70 and 0.67, respectively, and the BaA / (BaA+Chr) ratios are 0.40 and 0.33, respectively; the Fla / (Fla+Pyr) ratio of SD9 is 0.67, and the BaA / (BaA+Chr) ratio was not calculated because it was not detected. The Fla / (Fla+Pyr) ratios of SD7 and SD13 strongly indicate that their polycyclic aromatic hydrocarbon (PAH) pollution mainly originates from the combustion process; while the BaA / (BaA+Chr) ratio further confirms that the combustion is of a high-temperature combustion type (such as coal combustion). Therefore, the pollution source of these two sites can be clearly attributed to a high-intensity combustion source.

[0069] Table 5 Calculation results of effective points for characteristic ratios

[0070] In summary, in this embodiment, the sources of PAHs in the study area exhibit a spatial pattern of "dispersed in the north and singular in the south." Sites SD7 and SD13 show a singular pollution source, primarily combustion, directly reflecting local industrial or energy utilization activities. Site SD9, however, as an ecologically sensitive area where land and water meet, exhibits significant mixed and imported pollution sources, simultaneously influenced by river transport and atmospheric deposition. This analysis suggests that future pollution control should employ source reduction strategies for southern combustion hotspots, while for estuarine mixed pollution areas, a comprehensive approach combining watershed management and air pollution control is necessary, implementing multi-media collaborative governance.

[0071] 2) The relative abundance analysis of PAHs with different ring numbers can reflect the source of PAHs. High molecular weight (4~6 rings) PAHs mainly come from the combustion of fossil fuels in a high-temperature environment and can be used as a characteristic indicator of vehicle exhaust emissions or coal combustion emissions, while low molecular weight (2~3 rings) PAHs mainly represent petroleum-derived inputs.

[0072] Analysis of 15 soil samples (SD1-SD15) showed that PAHs were detected only at four sites: SD7, SD9, SD13, and SD14. The percentage of PAHs with different ring numbers at each detection site is shown in the table below. Figure 4 .

[0073] The total PAH concentration at SD7 was 2.73 mg / kg. This site exhibited a clear dominance of 4-ring PAHs, primarily related to coal combustion. The presence of 5-ring PAHs indicates a contribution from high-temperature petroleum fuel combustion. 3-ring and 2-ring PAHs combined accounted for approximately 30.40%, possibly originating from the volatilization of petroleum products or low-temperature incomplete combustion. In summary, the primary potential source of PAHs at SD7 is coal combustion pollution, mixed with petroleum combustion and volatilization sources.

[0074] The total PAHs concentration at SD9 was 1.47 mg / kg. Low molecular weight PAHs, including 2- and 3-ring PAHs, dominated this site, accounting for a high 72.79%. Low-ring PAHs exhibit high volatility and water solubility, and are typically associated with direct pollution from petroleum products (such as crude oil spills and light oil volatilization) or recent emissions. While 4-ring PAHs accounted for a similar proportion, high molecular weight compounds with 5 rings or higher were lacking. Overall, the primary potential source of PAHs at SD9 is petroleum combustion and volatilization, mixed with coal combustion pollution.

[0075] The total PAHs concentration at SD13 was 1.49 mg / kg. At this site, 4-ring PAHs accounted for over 60%, indicating the strongest coal combustion origin. The detection of 5-ring PAHs also suggests associated high-temperature combustion. The relatively low proportions of 2-ring and 3-ring PAHs indicate a smaller contribution from petroleum volatilization. Therefore, the PAHs at SD13 primarily originated from coal combustion.

[0076] Only one compound, phenanthrene (0.1 mg / kg), was detected at SD14, belonging to the tricyclic PAHs, accounting for 100%. The detection of a single low-ring PAH usually indicates deposition from long-distance atmospheric transport or mild, non-specific petroleum pollution, with a relatively weak source indicativeness.

[0077] Based on the analysis of the ring number distribution characteristics of polycyclic aromatic hydrocarbons (PAHs) in the soil at the detection sites (SD7, SD9, SD13, SD14), PAH pollution in the study area presents a multi-source complex characteristic. The main sources can be ranked in order of contribution as follows: (1) Coal combustion is the dominant source, and coal-related combustion activities are the most important input pathway for PAH accumulation in the area; (2) Petroleum pollution sources are an important mixed source, with the superimposed contribution of direct petroleum pollution or volatilization, as well as mobile sources such as transportation fuel combustion; (3) Background / long-distance transport sources.

[0078] In summary, regional soil PAHs are mainly caused by historical or current coal combustion emissions, superimposed with significant petroleum pollution and local industrial point source impacts, exhibiting obvious spatial heterogeneity.

[0079] Step 3: Analysis of microbial response mechanisms: Metagenomic sequencing was performed on representative water and sediment samples to obtain information on the structure and functional genome composition of the microbial community. Alpha diversity analysis was then conducted on the microbial community.

[0080] Based on the relative abundance analysis of species at the phylum and genus levels, a Z-score normalized clustering heatmap was constructed using the top 35 most abundant genera. Figure 5 To determine the differential indicator species among different groups from a statistical perspective, LDA Effect Size analysis (LDA threshold > 2.0) was performed. Figure 6 .

[0081] Intensive coking pollution stress led to a significant decrease in alpha diversity of sediment microbial communities (SD7 Shannon index dropped to 1.84), disrupting the stability of the original micro-ecosystem. In terms of species composition, indigenous sensitive microbial communities were eliminated due to their inability to adapt to the polluted environment, replaced by an explosive enrichment of the pollution-tolerant genus Thaurera. This genus had a relative abundance of 25.2% at SD7, becoming the absolutely dominant species.

[0082] The differential shaping effect of sediment microbial community structure, combined with Spearman correlation heatmaps, Figure 7 With redundancy analysis (RDA), Figure 8 Chemical oxygen demand (COD) is the most significant environmental factor explaining the spatial variability of the community in this watershed. COD is significantly positively correlated with predatory myxobacteria Corallococcus and Desulfurivibrio (P<0.01).

[0083] Geobacter, Denitrosoma, and Sphingopyxis are not distributed directly along the PAHs vector, but rather cluster closely around the Salinity vector.

[0084] The heatmap revealed a highly significant positive correlation between the abundance of Thermonema and PAH concentration (P<0.001). The fungal genus Coprinellus also showed a significant positive response to PAHs (P<0.05). The strong cross-genus synergistic correlation suggests that under extreme polycyclic aromatic hydrocarbon stress, a "fungal-bacterial consortium" network may have spontaneously assembled in the in-situ habitat of sediment.

[0085] Based on the Spearman correlation between microbial taxa and PAH abundance, the community was divided into three functional response types: positive responders, sensitive responders, and neutral taxa.

[0086] Spearman correlation analysis showed that, Figure 9Polycyclic aromatic hydrocarbons (PAHs) showed significant common correlations with various physicochemical indicators. Specifically, the data showed that PAHs exhibited moderately strong positive correlations with chemical oxygen demand (COD, r = 0.54), salinity (r = 0.53), and ammonia nitrogen (NH3-N, r = 0.46), while showing negative correlations with water pH (r = -0.46) and dissolved oxygen (DO, r = -0.47). This result indicates that PAH pollution in the study area is not a single toxicity variable input; the enrichment of PAHs is inevitably accompanied by the input of high concentrations of mixed organic carbon (COD) and the formation of a high osmotic pressure (salinity) environment. Therefore, when coping with PAH pollution, the microbial community actually faces a complex habitat pressure of "toxicity screening and substrate nutrient enrichment."

[0087] In the Mantel test network diagram, the distance matrix of the positive response group showed the highest positive correlation with the macroscopic carbon source indicator COD (Mantel r = 0.157, P = 0.072, marginally significant), while no significant linear driving relationship was shown with the single PAH concentration gradient (Mantel r = -0.04, P>0.05). This result indicates that polycyclic aromatic hydrocarbons (PAHs) play a decisive "threshold screening" role, and their high toxicity directly defines the survival boundary and enrichment qualification of the positive response group; while the high concentration of COD as an associated input acts as a key energy substrate, further driving the structural reorganization within this pollution-tolerant group. The two constitute an inseparable "toxicity stress-substrate driven" mechanism.

[0088] In contrast, the "neutral group," which was not screened by strong pollution, showed a significant positive correlation with the natural physical factor—temperature (Mantel r = 0.315, P = 0.014); while the "sensitive response group," due to toxicity suppression, showed no significant association with any of the measured factors (P>0.05). This result indicates that high-intensity PAHs compound pollution completely disrupts the conventional regulatory pathways of natural physical factors (such as temperature) on microbial communities, forcing the construction of micro-ecological networks to shift entirely onto a forced succession track dominated by anthropogenic pollution.

[0089] Metagenomic analysis based on the KEGG functional gene library revealed that at locus D7, the abundance of benzoyl-CoA reductase (K04113-K04115), a key enzyme involved in the anaerobic degradation of aromatic compounds, was as high as 151.4, significantly higher than the clean control (77.4, P<0.05). In particular, the bcrA and bamC genes showed specific high expression in D7, and these genes are the core functional markers driving benzene ring cleavage in Thaurera microorganisms under hypoxic conditions. Furthermore, functional annotation results showed that the abundance of the betaine / carnitine transporter gene (BCCT family, K03451) at locus D7 (27.7) was more than 30 times higher than that at the background locus D2 (0.8). This indicates that the dominant bacterial community, led by Thaurea, has adopted a highly energy-efficient "uptake strategy," which involves directly uptake of organic osmotic regulators from the environment to resist salinity stress, rather than energy-intensive de novo synthesis.

[0090] In summary, polycyclic aromatic hydrocarbons (PAHs) are key environmental factors driving microbial community differentiation, and their pollution levels directly determine the distribution patterns of different functional taxa. The enrichment of positively responding taxa reflects the microbial adaptation and utilization strategies of PAHs, while the decline of sensitive responding taxa reveals the potential threat of PAHs to ecosystem function.

[0091] Despite the significant reduction in biodiversity and the high environmental risks posed by coking pollution, the sediment micro-ecosystems in the study area have not completely collapsed, but rather exhibit remarkable in-situ self-purification potential. Functional microbial communities centered around Thaurera are continuously degrading aromatic pollutants in the sediments through anoxic or denitrification pathways. Thaurera abundance can serve as a specific bioindicator for the degree of coking pollution and early warning in this area, used to monitor the spread of pollution plumes. In terms of resource utilization, the identified indigenous Thaurera strains have extremely high application value and can be isolated, cultured, and developed into salt-tolerant bioremediation agents.

[0092] Step 4: Ecological risk assessment of polycyclic aromatic hydrocarbons: This embodiment uses the risk entropy method for ecological risk assessment.

[0093] An ecological risk assessment of 16 predominantly controlled polycyclic aromatic hydrocarbons (PAHs) in regional soils revealed significant spatial differentiation characteristics of the risks. Sites SD7 and SD9 were classified as "high-risk" based on the risk entropy method. This is because both sites not only have ∑RQ MPCs Values ​​(5.00 and 3.74 respectively) greater than 1 indicate the presence of pollutants exceeding the standard; their RQ NCSThe ΣPAH values ​​(1111.5 and 807.1 respectively) far exceed the 800 threshold, revealing severe background accumulation of various PAHs.

[0094] Analysis of the dominant pollutants at high-risk sites revealed that the key risk-driving monomers for SD7 were pyrene (Pyr), naphthalene (Nap), and fluorene (Flu). Specifically, the concentration ratio of fluoranthene to pyrene (Fla / (Fla+Pyr)) at SD7 was 0.7, which is greater than the empirical source apportionment threshold of 0.5, effectively indicating that the pollution source is coal combustion. Spatially, the distribution of high-risk site SD7 closely coincides with the location of the coking industrial park within the region.

[0095] Therefore, the results of the ecological risk assessment—the spatial clustering of high-risk sites, the composition of characteristic pollutants, and key molecular indicators—constitute a coherent chain of evidence. This chain of evidence objectively supports the scientific inference that "the PAHs pollution in the study area mainly originates from industrial activities involving the high-temperature pyrolysis and combustion of coal, with the coking industrial park being the primary source of influence," thus achieving a logical closed loop from risk characterization to pollution source tracing.

[0096] Step 5: Ecological risk warning of polycyclic aromatic hydrocarbons in the coking coal basin: In this embodiment, the PAH pollution in the study area mainly originated from industrial activities involving high-temperature pyrolysis and combustion of coal, with the coking industrial park being the primary source. The PAH composition in the study area exhibited significant concentration differences and spatial heterogeneity. Overall, the average concentration of each monomeric PAH spanned multiple orders of magnitude, with standard deviations generally close to or higher than the mean, and coefficients of variation (CV) ranging from 0.56 to 2.00, indicating that the pollutants were extremely unevenly distributed spatially and exhibited obvious local enrichment characteristics. In particular, the concentration of high-ring PAHs was significantly higher in some sampling points, while low-molecular-weight PAHs were undetectable or at extremely low levels in most sampling points, reflecting that PAH residues in this site have been affected by both environmental processes and human activities.

[0097] The main sources of PAH pollution in the coking coal basin are industrial activities involving high-temperature pyrolysis and combustion of coal. The ecological risk warning for PAHs is as follows: the coking coal basin is divided into upper, middle and lower reaches according to the topography, and water and sediment samples are collected in the upstream background reference area, the area adjacent to typical sewage outlets, the pollution plume diffusion area, the multi-source pollution confluence section, the sediment accumulation area and the ecologically sensitive response area.

[0098] The concentration and composition of PAHs in the watershed were determined, a spatial distribution map of pollutants was drawn, and high-pollution areas and potential risk hotspots in the region were identified. Furthermore, by combining the PAHs ratio method with land use information, the source categories of PAH pollution and their spatial diffusion and environmental fate pathways were analyzed.

[0099] Based on the response analysis of microbial communities to PAH pollution, the impact of pollution on micro-ecosystems is assessed, a quantitative response relationship between pollutant concentration and microbial function is constructed, and the response threshold and tolerance boundary of the ecosystem are identified, providing early biological indicators for ecological risk assessment.

[0100] The ecological risk of ∑PAHs was quantified using the risk entropy method to determine the extent of the impact of PAH residues in the site. Differential management based on risk assessment has significant scientific guiding significance for balancing regional resource development and public health protection.

[0101] In summary, this embodiment presents a series of studies on water and sediment indicators, polycyclic aromatic hydrocarbon (PAH) pollution characteristics, PAH source apportionment, PAH risk assessment, and microbial community structure response characteristics in the coking coal industrial belt of the middle reaches of the Yellow River. Through spatial identification of pollutants, microbial metagenomic analysis, and multidimensional modeling analysis, a method for early warning of PAH ecological risks in the coking coal basin can be applied.

[0102] Regarding the characteristics of polycyclic aromatic hydrocarbon (PAH) pollution, PAHs are the only type of organic pollutant that is widely detected in sediment analysis. They are mainly distributed in potential emission points of typical pollutants in industrial parks, before tributary confluence, multi-source convergence areas, and sediment enrichment sections.

[0103] Regarding the source apportionment of polycyclic aromatic hydrocarbons (PAHs), the characteristic ratio method was used to conduct a qualitative analysis of the pollution sources of PAHs, revealing that the sources of PAHs in this study area exhibit a spatial pattern of "dispersed in the north and single in the south." In future pollution control, based on the spatial pattern of PAH sources, management strategies for different regions will be adopted to implement multi-media collaborative governance.

[0104] In terms of ecological risk assessment, the risk entropy method was used to evaluate the ecological risks of 16 prioritized polycyclic aromatic hydrocarbons (PAHs) in the regional soil. The spatial clustering of high-risk sites, the composition of characteristic pollutants, and key molecular indicators together constitute a coherent chain of evidence. The PAH composition in the study area showed significant concentration differences and spatial heterogeneity. Based on the overall risk assessment, it is suggested that relevant management departments should implement precise control measures for the downstream river section of the coking industrial park.

[0105] Regarding the response characteristics of biological community structure, this embodiment reveals the directional reshaping mechanism of sedimentary microbial communities in coking coal industrial belts under the dual gradients of organic load and industrial characteristic pollutants (PAHs, salinity). At the level of ecological function response, extreme PAHs and high osmotic pressure stress not only specifically enriched specialized bacterial communities with the potential for "denitrification-aromatic hydrocarbon anaerobic degradation," but also induced a potential "fungus-bacteria" cross-species synergistic degradation network in the in-situ habitat. Complex pollution centered on polycyclic aromatic hydrocarbons drove the microbial community towards specialized degradation succession. The underlying micro-ecosystem, through the assembly of specific functional modules and the evolution of elemental cycling coupling mechanisms (carbon-sulfur, iron-nitrogen coupling), achieved deep adaptation to extreme industrial stress and in-situ self-purification. For example, functional bacterial communities centered around Thaurea are continuously degrading aromatic pollutants in sediments through anoxic or denitrification pathways, suggesting that Thaurea abundance can be used as a specific bioindicator for the degree of coking pollution and early warning in the region, and can be used to monitor the spread of pollution plumes. In terms of resource utilization, the identified indigenous Thaurea strains have extremely high application value and can be isolated, cultured and developed into salt-tolerant bioremediation agents.

[0106] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for early warning of polycyclic aromatic hydrocarbons (PAHs) in coking coal basins, characterized in that, Includes the following steps: Step 1: Based on the topography of the coking coal basin, select first to third-level sub-basins along the main stream and collect water samples and surface sediments; including measuring the physicochemical indicators in the water samples and the content of organic pollutants in the sediments, and monitoring the water body including salinity indicators; Step 2: Determine the concentration and composition of PAHs, and conduct a qualitative analysis of the pollution sources of PAHs by analyzing the ratio distribution between PAH isomers; Step 3: Select representative water and sediment samples to conduct metagenomic sequencing to obtain information on the structure and functional genome composition of the microbial community, including microbial community composition analysis, environmental-microbial community structure association analysis, and species-functional contribution analysis. Step 4: Conduct an ecological risk assessment of polycyclic aromatic hydrocarbons using the risk entropy method; Step 5: Ecological risk warning of polycyclic aromatic hydrocarbons in the coking coal basin.

2. The method for early warning of polycyclic aromatic hydrocarbons (PAHs) in coking coal basins according to claim 1, characterized in that, In step 2, the ratio distribution between PAH isomers is as follows: 。 3. The method for early warning of polycyclic aromatic hydrocarbons (PAHs) in coking coal basins according to claim 1, characterized in that, In step 1, first to third-level sub-basins are selected along the main stream, and sampling points are set up upstream, inlet and downstream of pollution sources; the sampling points include upstream background reference area, typical sewage outlet adjacent area, pollution plume diffusion area, multi-source pollution confluence section, sediment accumulation area and ecologically sensitive response area.

4. The method for early warning of polycyclic aromatic hydrocarbons (PAHs) in coking coal basins according to claim 1, characterized in that, In step 1, the physicochemical indicators include total organic carbon, anions, chemical oxygen demand, total nitrogen, ammonia nitrogen, and total phosphorus content. The water body is monitored for indicators including pH, dissolved oxygen, conductivity, temperature, and salinity.

5. The method for early warning of polycyclic aromatic hydrocarbons (PAHs) in coking coal basins according to claim 1, characterized in that, In step 2, the pollution source analysis of PAHs also includes the relative abundance analysis of PAHs with different ring numbers. High molecular weight 4-6 ring PAHs mainly come from the combustion of fossil fuels under high temperature environment and can be used as a characteristic indicator of vehicle exhaust emissions or coal combustion emissions. Low molecular weight 2-3 ring PAHs mainly represent petroleum source input. By analyzing indicators of spatial distribution and source type, the pollution sources of different sampling points can be obtained, and different governance strategies can be adopted for different regions in subsequent pollution control.

6. The method for early warning of polycyclic aromatic hydrocarbons (PAHs) in coking coal basins according to claim 1, characterized in that, In step 3, the microbial community composition analysis selects the top 5% to 15% of microbial genera in abundance, including constructing a Z-score normalized clustering heatmap, assessing the impact of environmental gradients on the shaping of microbial community structure, and using LDA Effect Size analysis to identify differential indicator species. The analysis of the relationship between the environment and the microbial community structure includes the use of Spearman correlation analysis and redundancy analysis to obtain the influence relationship between sediment microbial community structure and environmental factors, and to construct a PAH pollution biomonitoring system based on microbial response.

7. The method for early warning of polycyclic aromatic hydrocarbons (PAHs) in coking coal basins according to claim 1, characterized in that, In step 3, by analyzing the relationship between microbial community structure, functional genes and environmental factors, the abundance of species in the dominant bacterial community is used as a specific biological indicator for early warning or a resource-utilizing species. Alternatively, by analyzing the relationship between microbial community structure and PAH pollution, the microbial community can be divided into three functional response types: positive response groups, sensitive response groups, and neutral groups. The decrease in the abundance of sensitive response groups can be used as a biological indicator signal of PAH pollution.

8. The method for early warning of polycyclic aromatic hydrocarbons (PAHs) in coking coal basins according to claim 1, characterized in that, In step 4, the formula for calculating the risk entropy value of a monomeric PAH is as follows: In the formula: C i C represents the content of PAH monomer i in a certain environmental medium. NCs,i C represents the negligible risk standard value for type i PAH in the corresponding environmental medium. MPCs,i This represents the highest permissible risk standard value for type i PAH in this medium; The ecological risk level classification for individual PAHs is determined as follows: RQ i(MPCs) ≥1 indicates high risk; RQ i(NCS) ≥1 and RQ i(MPCs) <1 indicates medium risk; Total risk entropy values ​​of 16 PAHs: In the formula: i represents the body type of the 16 PAHs monomers; RQ NCs,∑PAHs With RQ MPCs,∑PAHs These represent the total negligible ecological risk level and the maximum permissible ecological risk level for the 16 PAHs, respectively. The ecological risk levels of 16 PAHs are classified according to RQ. NCs,ΣPAHs and RQ MPCs,ΣPAHs The following is a comprehensive assessment based on the following circumstances: RQ MPCs,ΣPAHs ≥1 and RQ NCs,ΣPAHs A value of ≥800 is considered high risk.

9. The method for early warning of polycyclic aromatic hydrocarbons (PAHs) in coking coal basins according to claim 1, characterized in that, In step 5, the ecological risk of ∑PAHs is quantified by the risk quotient method to determine the impact of PAHs residues on the site; the impact of pollution on the micro-ecosystem is assessed by analyzing the response of the microbial community to PAHs pollution, and a quantitative response relationship between pollutant concentration and microbial function is constructed.

10. A polycyclic aromatic hydrocarbon (PAH) early warning system suitable for coking coal basins, characterized in that, It includes a module for deploying microbial indicator species abundance response, a module for early warning of PAHs ecological risk entropy, and a module for early monitoring of organic pollutant concentrations, wherein the organic pollutants include dioxins.

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