A method for integrated assessment of indoor health risks combining chemical exposure and microbiological pathogenicity information

Abstract

The application discloses an indoor health risk comprehensive evaluation method fusing chemical exposure and microbial pathogenicity information, constructs a multi-dimensional risk factor integration framework, and for the first time systematically correlates, quantifies and fuses direct health risks of chemical pollutants, environmental behavior characteristics and potential pathogenicity risks induced by the chemical pollutants, so that more comprehensive and more accurate evaluation and priority ranking of the comprehensive health risks of indoor pollutants are realized. The method provided by the application can be widely applied to health risk identification and ranking of common and emerging chemical pollutants in indoor environmental media, including but not limited to pollutants in indoor dust, air and settled particulate matters. The evaluated pollutant types are wide, including high-concern pollutants of multiple types such as organophosphates, flame retardants, plasticizers, antibacterial agents, drug residues, polycyclic aromatic hydrocarbons and heavy metals, and have strong practical application value and wide applicability.

Description

Technical Field

[0001] This invention belongs to the field of environmental health risk assessment, specifically involving a comprehensive indoor health risk assessment method that integrates chemical exposure and microbial pathogenicity information. It is particularly suitable for identifying and prioritizing the control of high-priority pollutants that are related to indoor microbial pathogenicity and pose a complex risk to human health. Background Technology

[0002] As the primary activity space for modern people, the indoor environment has a profound impact on human health. Especially in modern buildings with high airtightness and low ventilation, various chemical pollutants and complex microbial communities are more likely to accumulate and coexist for a long time in the limited space, posing potential health threats.

[0003] Indoor environments bear a wide range of continuously accumulating chemical pollutant loads, including flame retardants, plasticizers, antibacterial agents, pharmaceutically active compounds, and their derivatives. These pollutants originate widely from building materials (coatings, sealants, etc.), interior furnishings (furniture, carpets, curtains), household appliances, cleaning and care products, and human activities (such as cooking and smoking). Significantly influenced by human activity patterns, spatial functions, and physical environmental parameters (such as temperature, humidity, and ventilation), the indoor microenvironment also fosters and maintains a characteristic, highly dynamic microbial community (including bacteria, fungi, and viruses). The stability of the microbial community is crucial for maintaining the homeostasis of the host's immune system; significant disturbances to its structure or function can not only directly disrupt the balance of the human microbiome but have also been proven to be key environmental factors affecting health. Continuous exposure to specific pathogenic microorganisms in the indoor environment may interfere with normal immune development processes, increasing an individual's risk of developing immune-mediated diseases such as asthma, allergic rhinitis, and eczema.

[0004] Significant interaction mechanisms exist between chemical pollutants and microorganisms. On the one hand, some pollutants can directly kill or inhibit sensitive bacterial communities, or exert selective pressure at sublethal concentrations, promoting the enrichment of resistant and potentially pathogenic bacteria, leading to community structure imbalance. On the other hand, these chemicals can act as unconventional carbon, nitrogen, or phosphorus sources, interfering with the normal metabolic pathways of microorganisms and altering their metabolic functions and enzyme expression profiles. Typical pollutants such as cigarette smoke residues (thirdhand smoke) have been observed to significantly reduce microbial community diversity and promote the proliferation of potentially pathogenic bacteria. Furthermore, long-term accumulation of pollutants may also promote the colonization and spread of drug-resistant and opportunistic pathogens, allowing antibiotic resistance genes (ARGs), virulence factors (VGs), and mobile genetic elements (MGEs) to coexist in dust and achieve lateral gene transfer through transfer media such as bacteriophage-plasmid transfer, exacerbating the co-evolution of resistance and virulence, thereby increasing the overall pathogenic potential of the microbial community.

[0005] However, existing indoor pollutant health risk assessment methods primarily focus on monitoring or calculating single-dimensional indicators, such as: environmental monitoring of chemical pollutant concentrations; estimation of hazard quotient (HQ) or risk quotient (RQ) based on reference dose (RfD) or reference concentration (RfC); and prediction of environmental behavior or bioavailability based on physicochemical properties (such as the octanol-water partition coefficient Log Kow). These single-dimensional or limited-dimensional methods neglect the disturbance of pollutants to the micro-ecosystem and their potential to indirectly affect health through microbial mechanisms, making it difficult to reflect the true health risks under conditions of combined exposure. Chemical pollutants can indirectly increase an individual's risk of bacterial infections and immune-related diseases by altering microbial community structure and metabolic function, and promoting the accumulation, expression, and spread of ARGs and VGs. Therefore, risk assessments based solely on chemical toxicity are prone to systematic bias, potentially leading to underestimation or underestimation of some high-risk pollutants, and resulting in a lack of precision and specificity in pollutant risk ranking and control decisions. Summary of the Invention

[0006] To overcome the aforementioned deficiencies in existing technologies, this invention provides a comprehensive indoor health risk assessment method that integrates chemical exposure and microbial pathogenicity information. The core of this invention lies in constructing a multi-dimensional risk factor integration framework. For the first time, it systematically correlates, quantifies, and integrates the direct health risks of chemical pollutants (including exposure levels and inherent toxicity), environmental behavioral characteristics (such as physicochemical parameters and tropism), and the potential microbial pathogenicity risks they induce. This allows for a more comprehensive and accurate assessment and prioritization of the overall health risks of indoor pollutants. The method provided by this invention is widely applicable to the identification and prioritization of health risks from common and emerging chemical pollutants in indoor environmental media, including but not limited to pollutants in indoor dust, air, and particulate matter. The types of pollutants assessed are broad, including organophosphates, flame retardants, plasticizers, antibacterial agents, drug residues, polycyclic aromatic hydrocarbons, heavy metals, and many other types of high-concern pollutants, possessing strong practical application value and broad applicability.

[0007] The objective of this invention is achieved through the following technical solution:

[0008] A comprehensive indoor health risk assessment method integrating chemical exposure and microbial pathogenicity information includes the following steps:

[0009] (1) Collect media samples from the indoor environment for analysis of chemical pollutants and analysis of microbial communities, respectively;

[0010] The indoor environment includes residences, offices, schools, commercial and entertainment venues (such as shopping malls, internet cafes, card rooms and billiard rooms, etc.);

[0011] The medium sample may be dust, air, settled particulate matter, air conditioner filter deposits, or high-frequency contact surface swabs, etc.; dust is preferred as the medium sample because it is a common enrichment carrier for various pollutants and microorganisms.

[0012] The preferred areas for dust collection are behind cabinets, under sofas, air conditioner vents, and carpets.

[0013] When the dust is used as a medium sample for chemical pollutant analysis, it should first be filtered with a mesh screen to remove impurities such as fibers, hair, and cotton wool.

[0014] (2) Chemical contaminant analysis of media samples: extract chemical contaminants from the media samples and perform quantitative analysis using instruments that match the target chemical contaminants;

[0015] The chemical contaminants include, but are not limited to, one or more of the following: organophosphates (OPEs) and their conversion products (TPs), polycyclic aromatic hydrocarbons (PAHs), phthalates (PAEs), antibacterial agents (such as triclosan), drug residues, and heavy metals;

[0016] The polycyclic aromatic hydrocarbons are preferably quantitatively analyzed using gas chromatography-mass spectrometry; the organophosphates and their conversion products, phthalates, antibacterial agents and drug residues are preferably quantitatively analyzed using high performance liquid chromatography-triple quadrupole mass spectrometry; the heavy metals are preferably quantitatively analyzed using inductively coupled plasma mass spectrometry (ICP-MS) or atomic absorption spectrometry (AAS).

[0017] The organophosphates mentioned include one or more of alkyl tri-OPEs, chlorinated tri-OPEs, and aromatic tri-OPEs.

[0018] The alkyl triester organophosphates mentioned above include one or more of the following: trimethyl phosphate (TMP), triethyl phosphate (TEP), tripropyl phosphate (TnPP), triisopropyl phosphate (TiPrP), tripropyl phosphate (TPrP), tri-n-butyl phosphate (TnBP), triisobutyl phosphate (TiBP), tri-n-pentyl phosphate (TPeP), tri-n-hexyl phosphate (THP), tris(2-butoxyethyl) phosphate (TBOEP), and tris(2-ethylhexyl) phosphate (TEHP).

[0019] The chlorotriester organophosphates include one or more of tris(2-chloroethyl) phosphate (TCEP), tris(2-chloroisopropyl) phosphate (TCIPP), tris(1,3-dichloroisopropyl) phosphate (TDCIPP), and 2,2-dichloromethyl-trimethylene-bis[bis(2-chloroethyl) phosphate] (V6);

[0020] The aromatic triester organophosphates include one or more of the following: triphenyl phosphate (TPHP), 2-ethylhexyl diphenyl phosphate (EHDPP), tricresyl phosphate (TMPP), diphenyltoluene phosphate (MDPP), tri-m-toluene phosphate (TMCP), tri-o-toluene phosphate (TOTP), phenyl (di-tert-butylphenyl) phosphate (DBPP), tert-butylbenzene diphenyl phosphate (BPDP), isodecyl diphenyl phosphate (iDDPHP), resorcinol bis(diphenyl phosphate) (RDP), and bisphenol A bis(diphenyl phosphate) (BDP).

[0021] The organophosphate conversion products include organophosphate diesters and / or hydroxylated organophosphates;

[0022] The organophosphate diesters mentioned above include one or more of dibutyl phosphate (DnBP), bis(butoxyethyl) phosphate (BBOEP), bis(2-ethylhexyl) phosphate (BEHP), bis(2-chloroethyl) phosphate (BCEP), bis(1-chloro-2-propyl) phosphate (BCIPP), bis(1,3-dichloro-2-propyl) phosphate (BDCIPP), diphenyl phosphate (DPHP), and phenyl xylene phosphate (BMPP).

[0023] The hydroxylated organophosphates include 2-hydroxyethyl bis(2-butoxyethyl) phosphate (BBOEHEP) and / or di(2-butoxyethyl)2-(3-hydroxybutoxyethyl) phosphate (OH-TBOEP).

[0024] (3) Microbial community analysis of media samples: Genomic DNA was extracted from the media samples, bacterial 16S rRNA genes were amplified and sequenced; BugBase software was used to predict the potential pathogenic index (PPI) of the obtained 16S rRNA sequencing data, and the pathogenicity potential score of the microbial community of each media sample (including mean PPI, median, etc.) was obtained.

[0025] (4) Match and integrate the quantitative data of chemical pollutants obtained in step (2) with the PPI values ​​corresponding to each medium sample in step (3), and use statistical correlation analysis to perform pairwise correlation analysis on the concentration of chemical pollutants and PPI index, and calculate the correlation coefficient;

[0026] The statistical correlation analysis methods mentioned include, but are not limited to, Spearman rank correlation analysis, regression models, cluster analysis, or network topology analysis;

[0027] (5) Conduct hormone receptor binding ability prediction analysis on chemical pollutants, select human nuclear receptors that are closely related to environmental exposure, obtain high-resolution crystal structures of each receptor from the Protein Data Bank (PDB) database, use AutoDock Vina software to perform docking analysis between chemical pollutants and human nuclear receptors, and record the predicted binding free energy (ΔG) of each chemical pollutant and each receptor.

[0028] The human nuclear receptors mentioned include thyroid hormone receptor α (TRα), thyroid hormone receptor β (TRβ), estrogen receptor α (ERα), estrogen receptor β (ERβ), and androgen receptor (AR).

[0029] (6) Taking into account the three main exposure routes—skin contact, inhalation, and hand-to-mouth ingestion—calculate the estimated daily intake (EDI) and hazard quotient (HQ) of the pollutant:

[0030] Hand-to-mouth route of ingestion: (1)

[0031] Inhalation route: (2)

[0032] Skin absorption pathways: (3)

[0033] in EDI ing , EDI inh and EDI der These are the estimated daily intakes (EDI) for hand-to-mouth, inhalation, and skin absorption, in mg. kg -1 ·day -1 ;

[0034] C It is the concentration of a single OPE in the dust, in ng·g -1 ; IR ing It is the particulate matter intake rate (mg / day) -1 ); IR inh Inhalation rate (m 3 ·day -1 ); PEFParticulate matter emission factor (m 3 ·kg -1 ); SA Skin surface area (cm²) 2 ·day -1 ); AF It is skin adhesion factor (mg·cm) -2 ); ABS der It is the absorption coefficient; BW Weight (kg); f ET It is the proportion of exposure time to the total daily time;

[0035] Calculate the HQ for each compound and each exposure pathway:

[0036] (4)

[0037] RfD is the safety threshold reference dose, which refers to the maximum daily intake that, under lifetime chronic exposure, is not expected to produce detectable harmful health effects in the general population, and is expressed in mg / kg. -1 ·d -1 RfD is derived from public databases (such as US EPA, EFSA) or predicted using the QSAR method.

[0038] (7) Obtain the logD value of each chemical pollutant in the chemical property prediction software;

[0039] The chemical property prediction software includes ADMETlab, EPI Suite, and ACD / Labs;

[0040] (8) Multidimensional data integration and risk priority ranking: the HQ obtained in step (6) represents health risk, the prediction combined with free energy obtained in step (5) represents endocrine interference potential, the logD value in step (7) represents physicochemical behavior characteristics, and the correlation coefficient obtained in step (4) represents microbial association.

[0041] The above four index values ​​are subjected to minimum-maximum standardization, and the calculation method is as follows:

[0042] (5)

[0043] Where χ Norm χ² represents the normalized values ​​obtained from different dimension indicators, which are dimensionless numbers ranging from [0,1]; χ² represents the original indicator value, with units consistent with the indicator itself; χ² Max and χ Min The maximum and minimum values ​​for this index are for all target compounds participating in the evaluation, with units consistent with the index itself.

[0044] Based on the contribution of each dimension to health risk, the weighting coefficients are determined as follows: Health Risk ( W H The endocrine disruption potential is 2; W E The value is 2; physicochemical behavior characteristics ( W C The value is 1; the microbial association ( W M The value is 1;

[0045] The final composite risk score (ToxPi) for chemical pollutants is calculated using the following formula:

[0046] (6)

[0047] Among them ToxPi i The final comprehensive risk score representing chemical pollutant i; HealthRisk i EndoEffect i ChemProp i and MicroAssoc i χ for each domain Norm The ToxPi score ranges from 0 to 1, with a higher value indicating a higher priority for health risk management.

[0048] Compared with existing single-dimensional risk analysis methods that rely solely on chemical pollutant concentration detection, toxicity prediction, or health hazard quotient (HQ) assessment, this invention demonstrates significant technological advancements and innovations in terms of assessment dimensions, analytical depth, and application breadth. The beneficial effects of this invention are reflected in the following aspects:

[0049] (1) This invention expands the dimensions of health risk assessment by introducing microbial functional factors such as potential pathogenicity indices, reveals the indirect impact of pollutants on the micro-ecosystem in indoor environments, and fills the gap in the existing assessment system's consideration of the health consequences of micro-ecological disturbances. This method quantifies the potential mechanisms by which pollutants cause health risks through indirect toxic pathways (such as inducing the expression of drug resistance genes and virulence genes) by constructing statistical associations between chemical exposure parameters and the functional characteristics of microbial communities.

[0050] (2) The method proposed in this invention integrates multi-dimensional indicators such as the health risks of chemical pollutants, endocrine disruption potential, physicochemical behavior characteristics, and microbial correlations, forming a complete and logically closed-loop risk assessment chain. Compared with traditional methods that rely on a single toxicological parameter or concentration threshold to judge risk, this invention provides a more accurate and comprehensive means of risk characterization.

[0051] (3) This invention can help identify non-traditional high-risk pollutants and improve the accuracy of priority prevention and control of indoor pollution. Traditional methods are prone to missing pollutants with low direct exposure risk, but some pollutants may cause higher health hazards through their impact on the micro-ecosystem. However, this invention, through a comprehensive assessment strategy, identifies highly pathogenic driving factors that are difficult to capture by traditional methods, providing decision support for the graded management of pollutants, high-risk screening and intervention strategy formulation.

[0052] (4) The assessment framework of this invention has high versatility and scalability, and is adaptable to a variety of pollutants and environmental media. The assessment method of this invention is applicable to various indoor media (dust, air, sediment, etc.) and a variety of pollutants of high concern (including OPEs, PAEs, antibacterial agents, heavy metals, etc.). The assessment index structure is flexible and can be expanded to more complex microbial risk factors or multiple toxicity endpoints such as ARGs, VGs, and MGEs according to project needs, and has broad application potential. Attached Figure Description

[0053] Figure 1 This represents the concentration and composition of organophosphorus triesters (tri-OPEs) and their conversion products (TPs) in dust from different indoor environments. Specifically: A represents the concentrations of alkyl, chlorinated, aryl tri-OPEs and TPs in six indoor environments; B1 represents the relative abundance of a single tri-OPE to the total tri-OPE concentration; B2 represents the relative abundance of a single TP to the total TP concentration; boxes indicate the 25th and 75th percentiles; black lines represent the median; and whiskers represent the 10th and 90th percentiles.

[0054] Figure 2 Predicted results of potential pathogenicity indices of microorganisms in dust from different indoor environments.

[0055] Figure 3 Predicted binding free energies between different organophosphates and hormone receptors.

[0056] Figure 4 The hazard factor (HQ) of organophosphates in indoor dust for different exposed populations.

[0057] Figure 5 Radar chart and cluster analysis of four-dimensional risk indicators for organophosphates and their conversion products.

[0058] Figure 6 The bar chart and ranking results of the ToxPi multidimensional risk assessment scores for each chemical pollutant are presented. Detailed Implementation

[0059] The present invention will be further described in detail below with reference to the embodiments and accompanying drawings, but the embodiments of the present invention are not limited thereto. Example

[0060] A comprehensive indoor health risk assessment method integrating chemical exposure and microbial pathogenicity information includes the following steps:

[0061] 1. Dust Sample Collection: Sampling was conducted in various typical indoor environments, including residences, offices, internet cafes, card rooms, shopping malls, and billiard rooms, covering different types of venues and activity patterns of people. Dust was collected by gently sweeping with a metal-free brush made of natural bristles, focusing on areas prone to dust accumulation, such as behind cabinets, under sofas, air conditioner vents, and carpets. The collected dust was transferred to pre-cleaned aluminum foil bags using a stainless steel shovel to ensure no external contamination, and immediately placed in a 4°C portable refrigerator to ensure sample stability and delivery to the laboratory on the same day. In the laboratory, each dust sample was homogenized and divided into two parts: ① For chemical contaminant analysis: filtered through a 100-mesh stainless steel sieve to remove impurities such as fibers, hair, and lint; the resulting dust was sealed in aluminum foil and stored at -20°C; ② For microbial community analysis: unscreened samples were directly aliquoted into sterile centrifuge tubes and stored in an ultra-low temperature freezer at -80°C to prevent DNA degradation.

[0062] 2. Sample Pretreatment and Organophosphate Analysis: For samples used in chemical pollutant analysis, the modified QuEChERS method was used to extract the target pollutants. The steps were as follows: Weigh approximately 500 mg of sieved dust into a 15 mL glass centrifuge tube; add 5 mL of HPLC-grade acetonitrile, vortex for 1 min, followed by sonication for 20 min; add 500 mg of pre-prepared QuEChERS extraction salt (trisodium citrate, disodium hydrogen citrate sesquihydrate, anhydrous magnesium sulfate, sodium chloride, mass ratio 1:0.5:4:1), vortex for 5 min, and centrifuge at 12000 rpm for 5 min; transfer the supernatant to a clean glass tube, repeat the above extraction steps twice for the residue, and combine all supernatants; add 150 mg of anhydrous MgSO4 and 25 mg of N-propylethylenediamine (PSA) to the combined extract for purification, vortex for 30 seconds, and centrifuge for 5 min; concentrate the purified extract to near dryness under a 35℃ water bath and nitrogen blowing conditions; redissolve in 0.5 mL of HPLC-grade methanol, and filter through 0.22 mL of HPLC-grade methanol. The sample was filtered through a μm nylon needle and then transferred to a vial for testing. Blank controls and spiked recovery were included for each batch of samples to verify the accuracy and stability of the method.

[0063] Seven alkyl tri-OPEs, four chlorotri-OPEs, six aryl tri-OPEs, and nine organophosphate conversion products (TPs), including organophosphate diesters and hydroxylated organophosphates, were determined using high-performance liquid chromatography (Nexera LC-40D, Shimadzu) and triple quadrupole mass spectrometry (API 6500, AB Sciex). Chromatographic separations were performed on an Agilent InfinityLab Poroshell 120 EC-C18 column (4.6 mm × 100 mm, 2.7 μm). The mobile phase consisted of an aqueous solution of 0.1% formic acid and 2 mM ammonium acetate and a methanol solution of 0.1% formic acid and 2 mM ammonium acetate (organic phase), with a total flow rate of 350 μL / min. -1 The injection volume was 3 μL. The column was maintained at 40°C. The mobile phase gradient was as follows: 0–2.0 min, 35% B; 2.0–9.0 min, 35%–90% B; 9.0–13.5 min, 90%–100% B; 13.5–16.0 min, 100% B; 16.0–16.1 min, 100%–35% B; 16.1–20.0 min, 35% B. Electrospray ionization of tri-OPEs was performed in positive ion mode, and TPs in negative ion mode, with an ion source temperature of 400°C. The ion spray voltage was set to 4.5 kV. Multiple reaction monitoring (MRM) mode was used for quantification of all target compounds.

[0064] The results are as follows Figure 1 As shown, a total of 16 tri-OPEs were detected, with TPrP not detected in any samples. Except for TiPrP, TMPP, and V6, whose detection rates in different scenarios were 56%-80%, 92%-100%, and 72%-100%, respectively, all other tri-OPEs were detected in all samples. DnBP, BEHP, BCEP, DPHP, and BMPP were detected in all dust samples, while the detection frequencies of BBOEP, BBOEHEP, OH-TBOEP, and BDCIPP were 24%-100%, 8%-100%, 0-22%, and 68%-100%, respectively.

[0065] The total concentration of OPEs varied significantly across different microenvironments: the highest concentration was found in dust samples from office spaces, and the lowest in residential spaces. Chlorinated and aromatic OPEs were significantly higher in public environments than in residential spaces. Emerging OPEs such as BDP, RDP, and iDDPHP were detected at higher levels in office and internet cafe environments. The total concentration of TPs was lowest in residential spaces. Figure 1 -A), with a median of 580 ng g -¹, significantly lower than other microenvironments (Kruskal-Wallis test, p <0.05), the median concentration of ∑TP in other microenvironments ranged from 2550 to 5560 ngg. -1 .

[0066] Tri-OPE composition analysis showed that TEP accounted for more than 50% in residential environments, while TCIPP, BDP, and other compounds accounted for a higher proportion in public environments. Figure 1 -B1) reflects different usage sources and release characteristics. For TPs, DnBP, BEHP, BCEP, and DPHP dominate in all environments, accounting for more than 90% of the total TP concentration ( Figure 1 -B2). The concentrations of some TPs (such as BEHP) even exceed those of their precursors OPEs, suggesting that they may originate directly from emissions from commercial products rather than from simple environmental conversion.

[0067] This step yielded multi-dimensional and quantifiable OPE pollution characteristic data, providing crucial data support for subsequent exposure assessment, health risk calculation, and prioritization.

[0068] 3. Dust gene sequencing and potential pathogenicity index prediction: Genomic DNA was isolated using the OMEGA Soil DNA Kit (M5635-02), and its quality and quantity were assessed using a NanoDrop NC2000 spectrophotometer and agarose gel electrophoresis. The V3-V4 region of the bacterial 16S rRNA gene was amplified using universal bacterial primers 338F and 806R. All 52 samples were successfully amplified and subsequently sequenced using the SP kit on the Illumina NovaSeq 6000 platform (500 cycles), yielding approximately 60,000 high-quality sequences per sample. Bioinformatics processing was performed using QIIME2 version 2022.11. The original sequences were demultiplexed, and primer sequences were removed. The DADA2 plugin was used for quality filtering, noise reduction, paired-end merging, and chimera removal. Amplicon sequence variants (ASVs) were classified using the SILVA Release 138.1 database and a Naive Bayes classifier. Sequencing saturation was confirmed by sparse curve analysis, confirming data reliability. The entire process includes negative and positive mock controls, and clearly identifies sample batches and sequencing numbers to ensure traceability and reproducibility.

[0069] After completing the microbial community structure analysis, parallel annotation was performed on the same ASV table based on Greengenes 13_8. The ASV feature table and GG annotations were then converted to the OTU / abundance input format required by BugBase. Subsequently, BugBase software was used to predict the Potentially Pathogenic Index (PPI) of the obtained 16S rRNA sequencing data to further assess the impact of pollutants on the pathogenicity potential of microorganisms. The feature table (ASV table) output by QIIME2 and the species annotation results were converted to the input format required by BugBase. Using the BugBase online tool or local version, PPI prediction was run based on the Greengenes 13_8 database to obtain the pathogenicity potential score of the microbial community for each sample. The PPI results were standardized, and the mean, median, and significance analysis of PPI were calculated for various microenvironments.

[0070] The results are as follows Figure 2 As shown, the PPI values ​​in residential environments ranged from 0.051 to 0.92, with the median significantly lower than those in public places. Public places (offices, shopping malls, internet cafes) had higher PPI values ​​(0.69 to 0.97, p<0.05), indicating a stronger potential for microbial pathogenicity in public environments.

[0071] This step systematically quantifies information on the potential pathogenicity of microorganisms and incorporates it into the indoor pollution risk assessment system for the first time, providing a new dimension for identifying complex risks.

[0072] 4. Chemical-Microbial Correlation Identification: To systematically reveal the potential impact pathways of indoor chemical pollutants on the pathogenicity of microorganisms and further enhance the scientific rigor and comprehensiveness of risk assessment, this embodiment quantitatively analyzes the correlation between the concentration of chemical pollutants in indoor dust and the PPI of microorganisms. The obtained chemical pollutant concentration data were matched and integrated with the corresponding PPI values ​​for each dust sample to form a complete dataset. To ensure analytical consistency, samples with missing values ​​were removed, and the pollutant concentration data underwent a logarithmic transformation to improve analytical stability. Spearman's rank correlation coefficient was used to perform pairwise correlation analysis between pollutant concentration and PPI indicators, and the correlation coefficient was calculated.

[0073] The results showed that 13 pollutants were significantly positively correlated with PPI. p <0.01), the r values, in descending order, are EHDPP, DPHP, iDDPHP, TBOEP, TEHP, TPHP, BDP, BEHP, BBOEP, RDP, DnBP, BBOEHEP, and TCIPP; Additionally, five compounds also showed a positive correlation ( pThe correlation coefficients for the pollutants were <0.05, namely TCEP, TDCIPP, BCEP, BDCIPP, and BMPP; the correlation coefficients for the remaining compounds ranged from -0.33 to 0.27. These results indicate that some OPEs may indirectly increase health risks by promoting the accumulation of pathogenic bacteria or causing microecological imbalance; the pollutant-microbe correlation was more significant in residential environments, presumably related to the persistence of pollutant exposure and the stability of the microenvironment.

[0074] This step enables the quantitative identification of the correlation between pollutants and the pathogenic potential of the microecology, systematically reveals potential indirect health risk pathways, and improves the accuracy and foresight of pollutant screening, hierarchical management, and the formulation of scientific intervention measures.

[0075] 5. Hormone Receptor Binding Capacity Prediction: Many organophosphates (OPEs) have been reported to have endocrine-disrupting capabilities. To further assess the potential disruptive effects of indoor chemical pollutants on the human endocrine system, this embodiment conducted a hormone receptor binding capacity prediction analysis on OPE compounds. This step, as an important dimension in assessing the potential toxicity of pollutants, can effectively improve the scientific rigor and systematic nature of the comprehensive risk assessment.

[0076] This patent application selects human nuclear receptors closely related to environmental exposure, including thyroid hormone receptor α (TRα), thyroid hormone receptor β (TRβ), estrogen receptor α (ERα), estrogen receptor β (ERβ), and androgen receptor (AR), as docking targets. These receptors play important roles in regulating key physiological processes such as reproduction, development, and metabolism, and are highly sensitive to endocrine disruptors.

[0077] Target OPEs compounds were selected, and their molecular structures in SDF or PDB format were downloaded. Preprocessing and format conversion were performed using Open Babel. High-resolution crystal structures of each receptor were obtained from the Protein Data Bank (PDB) database. Water molecule removal, hydrogen atom addition, Gasteiger charge calculation, and binding sites were defined using PyMOL or AutoDock Tools. Docking analysis was performed using AutoDock Vina software, with a grid frame covering the receptor active sites. The predicted binding free energy ΔG between each contaminant and each receptor was recorded. To ensure the reliability of the results, each compound underwent at least three independent docking trials with each receptor.

[0078] To assess the potential for endocrine disruption, molecular docking was performed on OPEs and five human nuclear receptors (AR, ERα, ERβ, TRα, and TRβ). The results are as follows: Figure 3 As shown, the binding affinity of Tri-OPEs ranges from -9.3 to -2.8 kcal / mol. -1The binding affinity of TP is -9.1 to -4.0 kcal / mol. -1 Aryl tri-OPEs, including the emerging iDDPHP, RDP, and BDP, exhibit the strongest binding affinity, suggesting greater endocrine-disrupting potential than Alkyl-OPEs or Chlorinated tri-OPEs. Among receptors, TRβ consistently demonstrates the strongest binding affinity.

[0079] This step allows for the rapid prediction of potential endocrine disruption effects of pollutants without relying on cell or animal experiments, significantly reducing experimental costs and time, while avoiding ethical issues and enhancing the flexibility and high-throughput screening capabilities of the assessment system.

[0080] 6. Multi-path exposure levels and health risk estimation: In order to scientifically assess the potential health risks of human exposure to OPEs in different indoor microenvironments, the three main exposure routes of skin contact, inhalation, and hand-to-mouth ingestion are comprehensively considered. The estimated daily intake (EDI) and hazard quotient (HQ) of pollutants are calculated and used as important indicators for comprehensive health risk assessment.

[0081] Hand-to-mouth route of ingestion: (1)

[0082] Inhalation route: (2)

[0083] Skin absorption pathways: (3)

[0084] in EDI ing , EDI inh and EDI der These are the daily intakes (EDI) from hand-to-mouth, inhalation, and skin absorption, respectively, in mg. kg -1 ·day -1 ;

[0085] C It is the concentration of a single OPE in the dust, in ng·g -1 ; IR ing It is the intake rate of particulate matter (100 mg / day) -1 ); IR inh It is the inhalation rate (15.7 m). 3 ·day -1 ); PEF The particulate matter emission factor is 1.36 × 10⁻⁶. 9 m 3 ·kg -1 ); SA It is the skin surface area (2000 cm²) 2 ·day -1 ); AF It is a skin adhesion factor (0.07 mg·cm⁻¹). -2 ); ABS der It is the absorption coefficient (0.1%). BW The weight was (59.3±2.72kg). f ET It is the proportion of exposure time to the total daily time.

[0086] The aforementioned f ET It is allocated based on a hypothetical daily activity pattern, in which individuals are assumed to have one-third (of their daily activities) f ET One-third of the time is spent at work (in the office or entertainment venues), and there are 14 hours a day. f ET For 14 / 24) at home, one hour ( f ET Engage in leisure activities for 30 minutes (1 / 24). f ET Shopping for 1 / 48.

[0087] Calculate the HQ for each compound and each exposure pathway:

[0088] (4)

[0089] RfD is the safety threshold reference dose, which refers to the maximum daily intake that, under lifetime chronic exposure, is not expected to produce detectable harmful health effects in the general population, and is expressed in mg / kg. -1 ·d -1 RfD is derived from public databases (such as US EPA, EFSA) or predicted using the QSAR method on the ToxValue platform.

[0090] The results showed that all HQs were below 1, indicating a low current non-carcinogenic health risk; the ΣHQ of public place workers (especially office workers and shopping mall workers) was significantly higher than that of residential workers; some TPs contributed more to HQ than the original tri-OPEs (such as BMPP), highlighting the importance of including TPs in risk assessment.

[0091] This step enables a quantitative assessment of the entire OPE exposure-dose-risk chain. Combined with multi-dimensional risk integration methods such as ToxPi, it can effectively support the systematic identification and priority determination of pollutant health risks.

[0092] 7. In this invention, the log D value of the target compound is mainly predicted using the ADMETlab 2.0 platform (https: / / admetmesh.scbdd.com / ). The structure is retrieved from the public database PubChem or drawn using the chemical drawing software ChemDraw, and the SMILES expression of the compound is exported. After inputting the expression into the platform, its built-in prediction algorithm based on QSAR and machine learning models is used to obtain the log D value under pH 7.4 conditions. This predicted value can simulate the lipid-water partitioning behavior of pollutants under human physiological conditions, thereby scientifically assessing their bioaccumulation and environmental migration capacity in indoor environments.

[0093] Table 1. SMILES expression and partition coefficient (log D) of the target compounds

[0094]

[0095] 8. Multidimensional data integration and risk prioritization: In order to achieve comprehensive quantitative identification and scientific ranking of the health risks of indoor organophosphate pollutants (OPEs), this embodiment develops and applies a multidimensional risk assessment and prioritization method.

[0096] This embodiment selects the following four risk information domains as risk assessment dimensions and develops a scoring framework based on the Toxicology Priority Index (ToxPi):

[0097] (1) Health risk: HQ calculated based on estimated EDI and RfD, reflecting the risk of direct exposure;

[0098] (2) Endocrine disruption potential: The binding affinity of pollutants to nuclear hormone receptors (ERα, ERβ, AR, TRα and TRβ) is predicted by molecular docking to quantify the binding free energy ΔG value and indicate the potential endocrine disruption risk.

[0099] (3) Physicochemical behavior characteristics: The distribution coefficient (log D) reflects the physicochemical behavior.

[0100] (4) Microbial correlation: Based on the absolute value of the Spearman rank correlation coefficient between pollutant concentration and microbial PPI, the possible impact of pollutants on the pathogenic potential of microorganisms is quantified, thereby incorporating the health impacts related to the microbiome into the consideration of chemical risks.

[0101] To achieve unified integration of indicators from different dimensions, the values ​​of each indicator are first subjected to minimum-maximum standardization, calculated as follows:

[0102] (5)

[0103] Where χ Norm χ² represents the normalized values ​​obtained from different dimension indicators, which are dimensionless numbers ranging from [0,1]; χ² represents the original indicator value, with units consistent with the indicator itself; χ² Max and χ Min The maximum and minimum values ​​for this index are for all target compounds participating in the evaluation, with units consistent with the index itself.

[0104] Based on the contribution of each dimension to health risk, the weighting coefficients are determined as follows: Health Risk ( W H The value is 2; endocrine interference ( W E The value is 2; the physical and chemical behavior is ( W C The value is 1; the microbial association ( W M The value is 1.

[0105] The final composite risk score (ToxPi) for pollutants is calculated using the following formula:

[0106] (6)

[0107] Where ToxPi represents the final overall risk score of chemical pollutant i; HealthRisk i EndoEffect i ChemProp i and MicroAssoc i χ for each domain Norm The ToxPi score ranges from 0 to 1, with a higher value indicating a higher priority for health risk management.

[0108] The results showed that the ToxPi scores ranged from 0.19 (OH-TBOEP) to 0.93 (BDP), indicating significant differences in risk levels among different pollutants. Aryl tri-OPEs, such as BDP, iDDPHP, and RDP, scored highly across all four dimensions, with overall scores significantly higher than alkyl and chlorinated tri-OPEs, forming a high-risk pollutant cluster. BDP and iDDPHP ranked first and second, respectively, with ToxPi scores of 0.93 and 0.91, reflecting their high exposure risk, strong hormone receptor binding activity, high physicochemical mobility, and positive promoting effect on microbial pathogenicity. Furthermore, substances such as OH-TBOEP, TEP, and TiPrP generally scored lower, ranking them lower in risk. Several OPE transformation products, such as BMPP, DPHP, and BEHP, also showed high ToxPi scores (0.82–0.92), which are not currently adequately addressed in the risk monitoring system and should be considered in future risk management systems.

[0109] The above embodiments are preferred embodiments of the present invention, but the embodiments of the present invention are not limited to the above embodiments. Any changes, modifications, substitutions, combinations, or simplifications made without departing from the spirit and principle of the present invention shall be considered equivalent substitutions and shall be included within the protection scope of the present invention.

Claims

1. A method of integrated assessment of indoor health risks fusing chemical exposure with microbiological pathogenic information, characterized in that It comprises the following steps: (1) Collecting medium samples of indoor environment for chemical contaminant analysis and microbial community analysis; (2) Chemical contaminant analysis of the medium sample, extracting chemical contaminants in the medium sample, and using instruments matched with target chemical contaminants for quantitative analysis; The chemical contaminants include one or more of organophosphate and its conversion product, polycyclic aromatic hydrocarbon, phthalate, antibacterial agent, drug residue and heavy metal; The organophosphate includes one or more of alkyl triester organophosphate, chlorinated triester organophosphate and aromatic triester organophosphate; The organophosphate conversion product includes organophosphate diester and / or hydroxylated organophosphate; (3) Microbial community analysis of the medium sample, extracting genomic DNA of the medium sample, amplifying bacterial 16S rRNA gene and sequencing; using BugBase software to predict the potential pathogenicity index PPI of the obtained 16S rRNA sequencing data, and obtaining the pathogenicity potential score of the microbial community of each medium sample; (4) Matching and integrating the quantitative data of chemical contaminants obtained in step (2) with the PPI value corresponding to each medium sample in step (3), and using statistical correlation analysis method to analyze the correlation between chemical contaminant concentration and PPI index, and calculating the correlation coefficient; (5) Carrying out nuclear receptor binding ability prediction analysis on the chemical contaminants, selecting human nuclear receptors closely related to environmental exposure, obtaining high-resolution crystal structures of each receptor from Protein Data Bank database, and using AutoDock Vina software to carry out chemical contaminant and human nuclear receptor docking analysis, and recording the predicted binding free energy ΔG of each chemical contaminant and each receptor; (6) Considering skin contact, respiratory inhalation and hand-mouth intake as the three main exposure pathways, calculating the estimated daily intake EDI and hazard quotient HQ of the pollutants: Hand-mouth intake pathway: (1) Respiratory inhalation pathway: (2) Skin absorption pathway: (3) wherein EDI ing , EDI inh and EDI der are the estimated daily intake EDI for hand-mouth ingestion, inhalation via respiration and dermal absorption, respectively, in mg kg -1 day -1 ; C is the concentration of individual OPEs in dust, ng·g -1 ; IR ing is the intake rate of particulate matter (mg·day -1 ); IR inh is the inhalation rate (m 3 ·day -1 ); PEF is the particulate matter emission factor (m 3 ·kg -1 ); SA is the skin surface area (cm 2 ·day -1 ); AF is the skin adhesion factor (mg·cm -2 ); ABS der is the absorption coefficient; BW is the body weight (kg); f ET is the proportion of exposure time to total daily time; Calculate HQ for each compound and each exposure pathway: wherein RfDis a safe threshold reference dose in mg-kg -1 ·d -1 ; (7) Obtain the logD value of each chemical contaminant in the chemical property prediction software; (8) Multi-dimensional data integration and risk priority ranking, using the HQ obtained in step (6) to represent health risk, using the predicted binding free energy obtained in step (5) to represent endocrine disrupting potential, using the logD value in step (7) to represent physicochemical behavior characteristics, and using the correlation coefficient obtained in step (4) to represent microbial correlation; The minimum-maximum standardization processing is carried out on the above four index values, and the calculation method is as follows: where χ Norm is the normalized value obtained for the different dimension indicators; χ is the original indicator value; χ Max and χ Min are the maximum and minimum values of the indicator for all target compounds participating in the evaluation; The weighting coefficients are determined according to the contribution of each dimension to the health risk as follows: Health risk W H Endocrine disruptor potential W E Physico-chemical behavior W C Microbial relevance W M ​ The final comprehensive risk score ToxPi of the chemical contaminant is calculated according to the following formula: where ToxPi i represents the final composite risk score for chemical contaminant i; HealthRisk i , EndoEffect i , ChemProp i , and MicroAssoc i χ Norm for each domain; ToxPi scores range from 0 to 1, with higher values indicating higher priority for health risk management.

2. The evaluation method according to claim 1, characterized in that: The alkyl triester organophosphate in step (2) includes one or more of trimethyl phosphate, triethyl phosphate, tripropyl phosphate, triisopropyl phosphate, tripropyl phosphate, tri-n-butyl phosphate, triisobutyl phosphate, tri-n-pentyl phosphate, tri-n-hexyl phosphate, tri(2-butoxyethyl) phosphate and tri(2-ethylhexyl) phosphate.

3. The evaluation method according to claim 1, characterized in that: The chloro triester organophosphate ester of step (2) includes one or more of tris (2-chloroethyl) phosphate, tris (2-chloroisopropyl) phosphate, tris (1,3-dichloroisopropyl) phosphate, and 2,2-bis (chloromethyl) -trimethylene-bis [bis (2-chloroethyl) phosphate].

4. The evaluation method according to claim 1, characterized in that: The aromatic triester organophosphate ester of step (2) includes one or more of triphenyl phosphate, 2-ethylhexyl diphenyl phosphate, tricresyl phosphate, cresyl diphenyl phosphate, tri-m-tolyl phosphate, tri-o-tolyl phosphate, phenyl (di-tert-butylphenyl) phosphate, t-butylphenyl diphenyl phosphate, isodecyl diphenyl phosphate, resorcinol bis (diphenyl phosphate), and bisphenol A bis (diphenyl phosphate).

5. The evaluation method of claim 1, characterized in that: The organophosphate diester of step (2) includes one or more of dibutyl phosphate, bis (butoxyethyl) phosphate, bis (2-ethylhexyl) phosphate, bis (2-chloroethyl) phosphate, bis (1-chloro-2-propyl) phosphate, bis (1,3-dichloro-2-propyl) phosphate, diphenyl phosphate, and phenyl xylenyl phosphate.

6. The evaluation method of claim 1, wherein: The hydroxylated organophosphate ester of step (2) includes 2-hydroxyethyl bis (2-butoxyethyl) phosphate and / or phosphoric acid di (2-butoxyethyl) 2- (3-hydroxybutoxy) ethyl ester.

7. The evaluation method of claim 1, wherein: The human nuclear receptor of step (5) includes thyroid hormone receptor alpha, thyroid hormone receptor beta, estrogen receptor alpha, estrogen receptor beta, and androgen receptor.

8. The evaluation method of claim 1, wherein: The medium sample of step (1) is dust, air, settled particulate matter, air conditioner filter deposits, or high frequency contact surface swabs.

9. The evaluation method of claim 1, wherein: The indoor environment of step (1) includes a residence, an office, a school, a commercial, and an entertainment venue.

10. The evaluation method of claim 1, characterized in that: The polycyclic aromatic hydrocarbon of step (2) is quantitatively analyzed using gas chromatography-mass spectrometry; the organophosphate ester and its conversion product, phthalate, antimicrobial agent, and drug residue are quantitatively analyzed using high performance liquid chromatography-triple quadrupole mass spectrometry; and the heavy metal is quantitatively analyzed using inductively coupled plasma mass spectrometry or atomic absorption spectrometry.

Images