Indoor health risk comprehensive assessment method fusing chemical exposure and microbial pathogenicity information

Abstract

The invention discloses an indoor health risk comprehensive assessment method fusing chemical exposure and microbial pathogenicity information, and the method comprises the steps: constructing a multi-dimensional risk factor integration framework; according to the method, direct health risks of chemical pollutants, environmental behavior characteristics and potential pathogenicity risks of microorganisms induced by the chemical pollutants are systematically associated, quantified and fused for the first time, so that more comprehensive and more accurate evaluation and priority ranking of comprehensive health risks of indoor pollutants are realized. The method provided by the invention can be widely applied to health risk identification and sorting of common and emerging chemical pollutants in indoor environment media, including but not limited to pollutants in indoor dust, air and settled particles. The evaluated pollutants are wide in variety coverage and include various high-attention pollutants such as organophosphate, flame retardants, plasticizers, antibacterial agents, drug residues, polycyclic aromatic hydrocarbons and heavy metals, and the method has high practical application value and application range.

Description

Technical Field

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

[0002] As the primary place for modern people to spend their time, the quality of the indoor environment has a profound impact on human health. In particular, in modern buildings with high airtightness and low ventilation rates, various chemical pollutants and complex microbial communities are more likely to accumulate and coexist in confined spaces for long periods of time, posing a potential health threat.

[0003] Indoor environments carry a wide range of chemical pollutants that accumulate over time, including flame retardants, plasticizers, antimicrobials, pharmaceutically active compounds, and their transformation products. These pollutants originate from a wide range of sources, including building materials (paints, sealants, etc.), interior furnishings (furniture, carpets, curtains), household appliances, cleaning and care products, and human activities (such as cooking and smoking). Influenced significantly by human activity patterns, spatial functions, and physical environmental parameters (such as temperature, humidity, and ventilation), the indoor microenvironment nurtures and maintains a distinctive, highly dynamic microbial community (including bacteria, fungi, and viruses). The stability of the microbial community is crucial for maintaining host immune homeostasis. Significant perturbations in its structure or function can not only directly disrupt the balance of the human microbiome but have also been shown to be key environmental factors affecting health. Continuous exposure to specific pathogenic microorganisms in the indoor environment can disrupt normal immune development, increasing the risk of immune-mediated diseases such as asthma, allergic rhinitis, and eczema.

[0004] Significant interactions exist between chemical pollutants and microorganisms. On the one hand, some pollutants can directly kill or inhibit sensitive microbial communities, or exert selective pressure at sublethal concentrations, leading to the enrichment of tolerant and potentially pathogenic bacteria and causing imbalances in community structure. On the other hand, these chemicals can act as unconventional carbon, nitrogen, or phosphorus sources, disrupting normal microbial metabolic pathways and altering their metabolic functions and enzyme expression profiles. Typical pollutants, such as cigarette smoke residue (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 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. These factors can then facilitate lateral gene transfer via transfer media such as phage-plasmids, exacerbating the coevolution of resistance and virulence, and thereby increasing the overall pathogenic potential of the microbial community.

[0005] However, existing health risk assessment methods for indoor pollutants primarily focus on monitoring or calculating single-dimensional indicators, such as environmental monitoring of chemical pollutant concentrations; estimation of hazard quotients (HQs) or risk quotients (RQs) based on reference doses (RfDs) or reference concentrations (RfCs); and reliance on physicochemical properties (such as the octanol-water partition coefficient, Log Kow) to predict environmental behavior or bioavailability. These single- or limited-dimensional approaches overlook pollutants' disruption of microbial ecosystems and their potential to indirectly impact health through microbial mechanisms, making them incapable of reflecting the true health risks under complex exposure conditions. 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 dissemination of ARGs and VGs. Therefore, risk assessments based solely on chemical toxicity are prone to systematic biases, potentially leading to underestimation or omission of some high-risk pollutants and inaccurate and inappropriate risk ranking and control decisions. Summary of the Invention

[0006] To overcome the aforementioned shortcomings of the existing technology, the present invention provides a comprehensive indoor health risk assessment method that integrates chemical exposure and microbial pathogenicity information. The core of this method lies in constructing a multidimensional risk factor integration framework. This system systematically correlates, quantifies, and integrates the direct health risks of chemical pollutants (including exposure levels and inherent toxicity), their environmental behavior characteristics (such as physicochemical parameters and migratory capacity), and the potential pathogenicity risks induced by these pollutants. This method enables a more comprehensive and accurate assessment and prioritization of the comprehensive health risks of indoor pollutants. The method provided by this invention is widely applicable to the identification and prioritization of health risks of common and emerging chemical pollutants in indoor environmental media, including but not limited to pollutants in indoor dust, air, and particulate matter. The pollutants assessed cover a wide range of high-concern pollutants, including organophosphates, flame retardants, plasticizers, antimicrobial agents, drug residues, polycyclic aromatic hydrocarbons, heavy metals, and other types of pollutants, demonstrating strong practical application value and broad applicability.

[0007] The purpose of the present invention is achieved through the following technical solutions: A comprehensive indoor health risk assessment method that integrates chemical exposure and microbial pathogenicity information includes the following steps: (1) Collecting media samples from indoor environments for chemical pollutant analysis and microbial community analysis; The indoor environment includes residences, offices, schools, commercial and entertainment venues (such as shopping malls, Internet cafes, chess and card rooms and billiard rooms, etc.); The medium sample can be dust, air, settling particles, air conditioning filter sediment, or high-frequency contact surface swabs, etc.; the medium sample is preferably dust because it is a common enrichment carrier of various pollutants and microorganisms; The dust collection area is preferably behind the cabinet, the bottom of the sofa, the air outlet of the air conditioner, the carpet, etc. When the dust is used as a medium sample for chemical pollutant analysis, it should first be filtered with a mesh to remove impurities such as fibers, hair, and cotton wool; (2) Chemical contaminant analysis of media samples: extract chemical contaminants from media samples and perform quantitative analysis using instruments that match the target chemical contaminants; The chemical pollutants include, but are not limited to, one or more of organophosphates (OPEs) and their transformation products (TPs), polycyclic aromatic hydrocarbons (PAHs), phthalates (PAEs), antimicrobial agents (such as triclosan), drug residues, and heavy metals; The polycyclic aromatic hydrocarbons are preferably quantitatively analyzed by gas chromatography-mass spectrometry; the organophosphates and their conversion products, phthalates, antimicrobial agents and drug residues are preferably quantitatively analyzed by high performance liquid chromatography-triple quadrupole mass spectrometry; the heavy metals are preferably quantitatively analyzed by inductively coupled plasma mass spectrometry (ICP-MS) or atomic absorption spectrometry (AAS); The organophosphate ester includes one or more of alkyl tri-OPEs, chlorinated tri-OPEs and aromatic tri-OPEs. The alkyl triester organic phosphate includes one or more of 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); The chlorotriester organic phosphate includes one or more of tris(2-chloroethyl) phosphate (TCEP), tris(2-chloroisopropyl) phosphate (TCIPP), tris(1,3-dichloroisopropyl) phosphate (TDCIPP) and 2,2-bischloromethyl-trimethylene-bis[bis(2-chloroethyl) phosphate] (V6); The aromatic triester organic phosphate includes one or more of triphenyl phosphate (TPHP), 2-ethylhexyl diphenyl phosphate (EHDPP), tricresyl phosphate (TMPP), diphenylcresyl phosphate (MDPP), trimetyl phosphate (TMCP), tri-o-cresyl phosphate (TOTP), phenyl (di-tert-butylphenyl) phosphate (DBPP), tert-butylphenyl diphenyl phosphate (BPDP), isodecyl diphenyl phosphate (iDDPHP), resorcinol bis(diphenyl phosphate) (RDP) and bisphenol A bis(diphenyl phosphate) (BDP); The organophosphate conversion products include organophosphate diesters and / or hydroxylated organophosphates; The organic phosphate diester includes 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 ditolyl phosphate (BMPP); The hydroxylated organic phosphates include 2-hydroxyethyl bis(2-butoxyethyl) phosphate (BBOEHEP) and / or di(2-butoxyethyl) 2-(3-hydroxybutoxy)ethyl phosphate (OH-TBOEP); (3) Analysis of the microbial communities of the media samples: genomic DNA of the media samples was extracted, and the bacterial 16S rRNA gene was amplified and sequenced. The Potentially Pathogenic Index (PPI) of the obtained 16S rRNA sequencing data was predicted using BugBase software to obtain the pathogenic potential score of the microbial community of each media sample (including PPI mean and median values); (4) Matching and integrating the quantitative data of chemical pollutants obtained in step (2) with the PPI values ​​corresponding to each medium sample in step (3), and using statistical correlation analysis methods to perform pairwise correlation analysis between the concentrations of chemical pollutants and PPI indicators, and calculating the correlation coefficient; The statistical association analysis methods include but are not limited to Spearman rank correlation analysis, regression model, cluster analysis or network topology analysis; (5) Carry out predictive analysis of hormone receptor binding ability of chemical pollutants. Human nuclear receptors closely related to environmental exposure were selected. High-resolution crystal structures of each receptor were obtained from the Protein Data Bank (PDB) database. AutoDock Vina software was used to perform docking analysis between chemical pollutants and human nuclear receptors. The predicted binding free energy (ΔG) of each chemical pollutant with each receptor was recorded. The human nuclear receptors include thyroid hormone receptor α (TRα), thyroid hormone receptor β (TRβ), estrogen receptor α (ERα), estrogen receptor β (ERβ) and androgen receptor (AR); (6) Taking into account the three main exposure routes of skin contact, inhalation and hand-to-mouth ingestion, calculate the estimated daily intake (EDI) and hazard quotient (HQ) of the pollutant: Hand-to-mouth ingestion route: (1) Inhalation route: (2) Skin absorption route: (3) in EDI ing 、 EDI inh and EDI der Estimated daily intake (EDI) from hand-to-mouth ingestion, inhalation and skin absorption, in mg kg -1 day -1 ; C is the concentration of a single OPE in dust, ng·g -1 ; IR ing is the particle intake rate (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 body weight (kg); f ET is the proportion of exposure duration to the total daily time; For each compound and each exposure route, calculate HQ: (4) RfD is the safety threshold reference dose, which refers to the maximum daily intake that is expected to have no detectable adverse health effects on the general population under lifelong chronic exposure, and is expressed in mg·kg -1 ·d -1 RfDs are derived from public databases (such as US EPA, EFSA) or predicted using QSAR methods.

[0008] (7) Obtain the logD value of each chemical pollutant in the chemical property prediction software; The chemical property prediction software includes ADMETlab, EPI Suite, and ACD / Labs; (8) Multidimensional data integration and risk prioritization, using the HQ obtained in step (6) to characterize health risks, the predicted binding free energy obtained in step (5) to characterize endocrine disruption potential, the logD value obtained in step (7) to characterize physicochemical behavior characteristics, and the correlation coefficient obtained in step (4) to characterize microbial association; The above four index values ​​are subjected to minimum-maximum normalization processing, and the calculation method is as follows: (5) where χ Norm is the normalized value obtained from the different dimension indicators, which is a dimensionless number with a range of [0,1]; χ is the original indicator value, and its unit is consistent with the indicator itself; χ Max and χ Min It is the maximum and minimum value of this indicator for all target compounds involved in the evaluation. The unit is consistent with the indicator itself.

[0009] According to the contribution of each dimension to health risk, the weighted coefficient is determined as follows: Health risk ( W H ) is 2; endocrine disruption potential ( W E ) is 2; physical and chemical behavior characteristics ( W C ) is 1; microbial association ( W M ) is 1; The final comprehensive risk score ToxPi of chemical pollutants is calculated according to the following formula: (6) ToxPi i Represents the final comprehensive risk score of chemical pollutant i; HealthRisk i 、EndoEffect i 、ChemProp i and MicroAssoc i For each domain,Norm ; ToxPi scores range from 0 to 1, with higher values ​​indicating a higher priority for health risk management.

[0010] Compared to existing single-dimensional risk analysis methods based solely on chemical pollutant concentration detection, toxicity prediction, or Health Hazard Quotient (HQ) assessment, this invention offers significant technological advancement and innovation in terms of assessment dimensions, analytical depth, and breadth of application. The beneficial effects of this invention are reflected in the following aspects: (1) This method expands the dimension of health risk identification by introducing microbial functional factors such as the potential pathogenicity index, reveals the indirect impact of pollutants on the microecological system in indoor environments, and fills the gap in the existing assessment system's consideration of the health consequences of microecological disturbances. By constructing statistical associations between chemical exposure parameters and microbial community functional characteristics, this method quantifies the potential mechanisms by which pollutants induce health risks through indirect toxicity pathways (such as induction of resistance genes and expression of virulence genes).

[0011] (2) The proposed method integrates multiple indicators, including the health risks, endocrine disruption potential, physicochemical behavior, and microbial associations of chemical pollutants, to form a comprehensive and logically closed-loop risk assessment chain. Compared with traditional methods that rely on a single toxicological parameter or concentration threshold to determine risk, the proposed method provides a more accurate and comprehensive risk characterization method.

[0012] (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 very likely to miss pollutants with low direct exposure risks, but some pollutants may cause higher health hazards through their impact on the microecological system. However, this invention uses a comprehensive assessment strategy to identify highly pathogenic drivers that are difficult to capture with traditional methods, providing decision support for the hierarchical management of pollutants, high-risk screening, and the formulation of intervention strategies.

[0013] (4) The assessment framework of the present invention is highly versatile and extensible, and is adaptable to a variety of pollutants and environmental media. The assessment method of the present invention is applicable to various indoor media (dust, air, sediment, etc.) and multiple types of pollutants of high concern (including OPEs, PAEs, antimicrobial agents, heavy metals, etc.). The assessment indicator structure is flexible and can be expanded to more complex microbial risk factors such as ARGs, VGs, MGEs, or multiple toxicity endpoints according to project requirements, showing the potential for broad application. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1are the concentrations and compositions of organophosphorus triesters (tri-OPEs) and their transformation products (TPs) in dusts of different indoor environments, where: A is the concentration of alkyl, chlorinated, and aryl tri-OPEs and TPs in six indoor environments; B1 is the relative abundance of a single tri-OPE to the ∑tri-OPE concentration; B2 is the relative abundance of a single TP to the ∑TP concentration; the boxes represent the 25th and 75th percentiles; the black line represents the median; and the whiskers represent the 10th and 90th percentiles.

[0015] Figure 2 The results are the prediction results of potential pathogenicity index of microorganisms in dust in different indoor environments.

[0016] Figure 3 Predicted binding free energies between different organophosphates and hormone receptors.

[0017] Figure 4 is the hazard factor (HQ) of organophosphates in indoor dust for different exposed populations.

[0018] Figure 5 Radar chart and cluster analysis of four-dimensional risk indicators for organophosphates and their transformation products.

[0019] Figure 6 This is a bar chart and ranking results of the ToxPi multidimensional risk assessment scores for each chemical pollutant. DETAILED DESCRIPTION

[0020] The present invention will be described in further detail below with reference to embodiments and drawings, but the embodiments of the present invention are not limited thereto. Example

[0021] A comprehensive indoor health risk assessment method that integrates chemical exposure and microbial pathogenicity information includes the following steps: 1. Dust Sample Collection: Sampling was conducted in a variety of typical indoor environments, including residences, offices, internet cafes, chess and card rooms, shopping malls, and billiard rooms, encompassing diverse venue types and population activity patterns. Dust was collected using a natural-bristle, metal-free brush, focusing on areas prone to dust accumulation, such as behind cabinets, under sofas, air conditioning vents, and carpets. The collected dust was transferred using a stainless steel shovel into pre-cleaned aluminum foil bags to ensure the absence of external contamination. The bags were immediately placed in a portable cooler at 4°C to ensure sample stability and were delivered to the laboratory the same day. In the laboratory, each dust sample was homogenized and divided into two parts: ① For chemical contaminant analysis: the dust was filtered through a 100-mesh stainless steel sieve to remove impurities such as fibers, hair, and lint. The resulting dust was then sealed in aluminum foil and stored at -20°C. ② For microbial community analysis: the unsieved sample was directly aliquoted into sterile centrifuge tubes and stored in a -80°C ultra-low temperature freezer to prevent DNA degradation.

[0022] 2. Sample Pretreatment and Organophosphate Analysis: For samples used for chemical pollutant analysis, the target pollutants were extracted using a modified QuEChERS method as follows: approximately 500 mg of sieved dust was weighed into a 15 mL glass centrifuge tube; 5 mL of HPLC-grade acetonitrile was added, vortexed for 1 min, and then sonicated for 20 min; 500 mg of pre-prepared QuEChERS extraction salts (trisodium citrate, disodium hydrogen citrate sesquihydrate, anhydrous magnesium sulfate, and sodium chloride, mass ratio 1:0.5:4:1) were added, vortexed for 5 min, and centrifuged at 12,000 rpm for 5 min; the supernatant was transferred to a clean glass tube, and the above extraction steps were repeated twice for the residue, and all supernatants were combined; 150 mg of anhydrous MgSO4 and 25 mg of N-propylethylenediamine (PSA) were added to the combined extract for cleanup, vortexed for 30 seconds, and centrifuged for 5 min; the cleaned extract was concentrated to near dryness in a 35°C water bath under nitrogen purge; it was reconstituted with 0.5 mL of HPLC-grade methanol and filtered through 0.22 The samples were filtered through a μm nylon syringe filter and finally transferred to an injection vial for testing. Blank controls and spike recovery were set up for each batch of samples to verify the accuracy and stability of the method.

[0023] Seven alkyl tri-OPEs, four chlorinated tri-OPEs, six aromatic tri-OPEs, and nine organophosphate conversion products (TPs), including diesters and hydroxylated organophosphates, were analyzed using a high-performance liquid chromatograph (Nexera LC-40D, Shimadzu) coupled with a triple quadrupole mass spectrometer (API 6500, AB Sciex). Chromatographic separation was performed on an Agilent InfinityLab Poroshell 120 EC-C18 column (4.6 mm × 100 mm, 2.7 μm). The mobile phase consisted of 0.1% formic acid and 2 mM ammonium acetate in water (aqueous phase) and 0.1% formic acid and 2 mM ammonium acetate in methanol (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; and 16.1–20.0 min, 35% B. Electrospray ionization was performed in positive ion mode for tri-OPEs and negative ion mode for TPs, with a 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.

[0024] The results are as follows Figure 1 As shown: A total of 16 tri-OPEs were detected, of which TPrP was not detected in any samples. Except for TiPrP, TMPP, and V6, which had detection rates of 56%-80%, 92%-100%, and 72%-100% in different scenarios, the remaining tri-OPEs were detected in all samples. DnBP, BEHP, BCEP, DPHP, and BMPP were detected in all dust samples, while BBOEP, BBOEHEP, OH-TBOEP, and BDCIPP had detection frequencies of 24%-100%, 8%-100%, 0-22%, and 68%-100%, respectively.

[0025] The total concentration of OPEs varied significantly across different microenvironments: highest in office dust samples and lowest in residential areas. Chlorinated and aromatic OPEs were significantly higher in public environments than in residential areas. Emerging OPEs such as BDP, RDP, and iDDPHP were detected at higher levels in offices and internet cafes. Residential areas had the lowest total concentration of TPs ( Figure 1 -A), with a median of 580 ng g - ¹, significantly lower than other microenvironments (Kruskal-Wallis test, p <0.05), and the median concentration of ∑TP in other microenvironments ranged from 2550 to 5560 ng / g. -1 .

[0026] Analysis of Tri-OPEs composition showed that TEP accounted for more than 50% in residential environments, while compounds such as TCIPP and BDP accounted for a high proportion in public environments ( Figure 1 -B1), reflecting different sources of use and release characteristics. For TPs, DnBP, BEHP, BCEP, and DPHP dominated in all environments, accounting for more than 90% of the total TP concentration ( Figure 1 The concentrations of some TPs (such as BEHP) even exceeded those of their precursors, OPEs, suggesting that they may be directly emitted from commercial products rather than simply generated through environmental transformation.

[0027] Through this step, multi-dimensional and quantifiable OPEs pollution characteristic data were obtained, providing key data support for subsequent exposure assessment, health risk calculation and prioritization.

[0028] 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 bacterial universal primers 338F and 806R. All 52 samples were successfully amplified and subsequently sequenced on an Illumina NovaSeq 6000 platform using the SP kit (500 cycles), generating approximately 60,000 high-quality reads per sample. Bioinformatics processing was performed using QIIME2 version 2022.11. Raw sequences were demultiplexed, and primer sequences were removed. Quality filtering, denoising, paired-end merging, and chimera removal were performed using the DADA2 plugin. Amplicon sequence variants (ASVs) were classified using the SILVA Release 138.1 database and a naive Bayesian classifier. Rarefaction curve analysis confirmed sequencing saturation and data reliability. Negative and positive mock controls are set up throughout the entire process, and sample batches and sequencing numbers are clearly defined to ensure traceability and reproducibility.

[0029] After completing the microbial community structure analysis, the same ASV table was annotated in parallel using Greengenes 13_8. The ASV feature table and GG annotations were converted to the OTU / abundance input format required by BugBase. The resulting 16S rRNA sequencing data were then used to predict the Potentially Pathogenic Index (PPI) using BugBase software to further assess the impact of pollutants on microbial pathogenicity. The feature table (ASV table) and species annotation output from QIIME2 were converted to the BugBase input format. PPI prediction was performed using the BugBase online tool or a local version based on the Greengenes 13_8 database to obtain pathogenicity scores for each sample's microbial community. PPI results were normalized, and the mean, median, and significance of differences were calculated for each microenvironment.

[0030] The results are as follows Figure 2 As shown: The PPI value of residential environment ranged from 0.051 to 0.92, and the median was significantly lower than that of public places; the PPI value of public places (offices, shopping malls, and Internet cafes) was higher (0.69~0.97, p<0.05), suggesting that the pathogenic potential of microorganisms in public environments is stronger.

[0031] Through this step, the potential pathogenicity information of microorganisms was systematically quantified and incorporated into the indoor pollution risk assessment system for the first time, providing a new dimension for complex risk identification.

[0032] 4. Identification of chemical-microbial correlation: In order to systematically reveal the potential impact path of indoor chemical pollutants on the pathogenic potential of microorganisms and further improve the scientificity and comprehensiveness of risk assessment, this embodiment quantitatively analyzes the correlation between the concentration of chemical pollutants in indoor dust and the microbial PPI. The obtained chemical pollutant concentration data are matched and integrated with the PPI value corresponding to each dust sample to form a complete data set. To ensure the consistency of the analysis, the missing value samples are eliminated, and the pollutant concentration data are logarithmically transformed to improve the stability of the analysis. The Spearman rank correlation coefficient is used to perform pairwise correlation analysis on the pollutant concentration and PPI indicators, and the correlation coefficient is calculated.

[0033] The results showed that 13 pollutants were significantly positively correlated with PPI ( p <0.01), the order of r values ​​was EHDPP, DPHP, iDDPHP, TBOEP, TEHP, TPHP, BDP, BEHP, BBOEP, RDP, DnBP, BBOEHEP and TCIPP; another five compounds also showed positive correlation ( p <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 suggest that some OPEs may indirectly increase health risks by promoting the accumulation of pathogens or microbial imbalance. Pollutant-microbial associations are more pronounced in residential environments, presumably related to persistent pollutant exposure and microenvironmental stability.

[0034] This step achieves the quantitative identification of the association between pollutants and microecological pathogenic potential, systematically reveals potential indirect health risk pathways, and improves the accuracy and foresight of pollutant screening, graded management, and the formulation of scientific intervention measures.

[0035] 5. Hormone Receptor Binding Ability Prediction: Many organophosphates have been reported to have endocrine-disrupting properties. To further assess the potential disruption of indoor chemical pollutants on the human endocrine system, this example conducted a hormone receptor binding prediction analysis for OPEs. This step, as a key dimension in assessing the potential toxicity of pollutants, effectively enhances the scientific and systematic nature of comprehensive risk assessments.

[0036] 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 an important role in regulating key physiological processes such as reproduction, development, and metabolism, and are highly sensitive to endocrine disruptors.

[0037] Target OPE compounds were selected, their molecular structures downloaded in SDF or PDB format, and preprocessed and converted using Open Babel. High-resolution crystal structures of each receptor were obtained from the Protein Data Bank (PDB) database. Water molecules were removed, hydrogen atoms were added, Gasteiger charge calculations were performed, and binding sites were defined using PyMOL or AutoDock Tools. Docking analysis was performed using AutoDock Vina software, with a grid frame set to cover the receptor active site. The predicted binding free energy ΔG for each contaminant was recorded. To ensure the reliability of the results, each compound was independently docked with each receptor at least three times.

[0038] To evaluate the endocrine disrupting potential, molecular docking of OPEs with five human nuclear receptors (i.e., AR, ERα, ERβ, TRα, and TRβ) was performed. Figure 3 As shown: The binding affinity of Tri-OPEs ranges from -9.3 to -2.8 kcal mol -1 The binding affinity of TP is -9.1 to -4.0 kcal mol -1 Aryl tri-OPEs, including the emerging iDDPHP, RDP, and BDP, showed the strongest binding affinity, suggesting a greater endocrine disrupting potential than alkyl-OPEs or chlorinated tri-OPEs. Among the receptors, TRβ consistently exhibited the strongest binding affinity.

[0039] Through this step, the potential endocrine disrupting effects of pollutants can be quickly predicted without relying on cell or animal experiments, significantly reducing experimental costs and cycles, while avoiding ethical issues and improving the flexibility and high-throughput screening capabilities of the evaluation system.

[0040] 6. Multi-path exposure level 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 pathways of skin contact, respiratory inhalation, and hand-to-mouth ingestion are comprehensively considered, and the estimated daily intake (EDI) and hazard quotient (HQ) of pollutants are calculated and used as important indicators for comprehensive health risk assessment.

[0041] Hand-to-mouth ingestion route: (1) Inhalation route: (2) Skin absorption route: (3) in EDIing 、 EDI inh and EDI der They are the daily intake (EDI) from hand-to-mouth ingestion, inhalation and skin absorption, in mg kg -1 day -1 ; C is the concentration of a single OPE in dust, ng·g -1 ; IR ing is the particle intake rate (100 mg·day -1 ); IR inh is the inhalation rate (15.7 m 3 day -1 ); PEF is the particulate matter emission factor (1.36×10 9 m 3 kg -1 ); SA is the skin surface area (2000 cm 2 day -1 ); AF is the skin adhesion factor (0.07 mg·cm -2 ); ABS der is the absorption coefficient (0.1%); BW is body weight (59.3 ± 2.72 kg); f ET It is the proportion of exposure time to the total daily time.

[0042] The f ET was allocated based on an assumed daily activity pattern, where individuals were assumed to spend one-third of their day ( f ET 1 / 3) of the time is spent at work (in the office or in entertainment venues), and there are 14 hours a day ( f ET For 14 / 24) at home, one hour ( f ET 1 / 24) engage in leisure activities, 30 minutes ( f ET Shopping for 1 / 48).

[0043] For each compound and each exposure route, calculate HQ: (4) RfD is the safety threshold reference dose, which refers to the maximum daily intake that is expected to have no detectable adverse health effects on the general population under lifelong chronic exposure, and is expressed in mg·kg -1 ·d -1 RfDs are derived from public databases (such as US EPA, EFSA) or predicted using the QSAR method using the ToxValue platform.

[0044] The results showed that all HQs were lower than 1, indicating that the current non-carcinogenic health risks were low; the ΣHQ of workers in public places (especially offices and shopping malls) was significantly higher than that of residential residents; and the contribution of some TPs to HQ even exceeded that of the original tri-OPEs (such as BMPP), emphasizing the importance of incorporating TPs into risk assessment.

[0045] This step realizes the quantitative assessment of the entire chain of OPEs exposure-dose-risk. Combined with multi-dimensional risk integration methods such as ToxPi, it can effectively support the systematic identification of pollutant health risks and the determination of control priorities.

[0046] 7. In this application, the log D values ​​of target compounds are primarily predicted using the ADMETlab 2.0 platform (https: / / admetmesh.scbdd.com / ). Structures are retrieved from the public database PubChem or drawn using the chemical drawing software ChemDraw, and the compound's SMILES expression is derived. This expression is then entered into the platform, and the platform's built-in prediction algorithm, based on QSAR and machine learning models, is used to determine the log D value at pH 7.4. This predicted value simulates the lipid-water partitioning behavior of pollutants under human physiological conditions, thereby scientifically assessing their bioaccumulation and environmental transport potential in indoor environments.

[0047] Table 1 SMILES expression and distribution coefficient (log D) of target compounds

[0048] 8. Multidimensional data integration and risk prioritization: To quantitatively identify and scientifically prioritize the comprehensive health risks of indoor organophosphate pollutants (OPEs), this embodiment develops and applies a multidimensional risk assessment and prioritization method.

[0049] This example selects the following four risk information domains as risk assessment dimensions and develops a scoring framework based on the Toxicology Priority Index (ToxPi): (1) Health risk: HQ calculated based on estimated EDI and RfD, reflecting direct exposure risk; (2) Endocrine disruption potential: molecular docking is used to predict the binding affinity of pollutants to nuclear hormone receptors (ERα, ERβ, AR, TRα, and TRβ), and the binding free energy ΔG value is used to quantify the potential endocrine disruption risk; (3) Physical and chemical behavior characteristics: The physical and chemical behavior is reflected by the distribution coefficient (log D).

[0050] (4) Microbial association: 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 microbiome-related health effects into the consideration of chemical risks.

[0051] In order to achieve the unified integration of indicators of different dimensions, the minimum-maximum standardization processing of each indicator value is first performed. The calculation method is as follows: (5) where χ Norm is the normalized value obtained from the different dimension indicators, which is a dimensionless number with a range of [0,1]; χ is the original indicator value, and its unit is consistent with the indicator itself; χ Max and χ Min It is the maximum and minimum value of this indicator for all target compounds involved in the evaluation. The unit is consistent with the indicator itself.

[0052] According to the contribution of each dimension to health risk, the weighted coefficient is determined as follows: Health risk ( W H ) is 2; endocrine disruption ( W E ) is 2; physical and chemical behavior ( W C ) is 1; microbial association ( W M ) is 1.

[0053] The final comprehensive risk score ToxPi of a pollutant is calculated according to the following formula: (6) Where ToxPi represents the final comprehensive risk score of chemical pollutant i; HealthRisk i 、EndoEffect i 、ChemProp i and MicroAssoc i For each domain, Norm The ToxPi score ranges from 0 to 1, with higher values ​​indicating a higher priority for health risk management.

[0054] Results showed that ToxPi scores ranged from 0.19 (OH-TBOEP) to 0.93 (BDP), indicating significant differences in risk levels among different pollutants. Aromatic tri-OPEs (OPEs) such as BDP, iDDPHP, and RDP scored high across all four dimensions, with overall scores significantly higher than those of alkyl tri-OPEs and chlorinated tri-OPEs, forming a high-risk 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 effects on microbial pathogenicity. Furthermore, substances such as OH-TBOEP, TEP, and TiPrP generally had lower scores and were ranked lower in the risk ranking. Transformation products of several OPEs, such as BMPP, DPHP, and BEHP, also exhibited high ToxPi scores (0.82–0.92). These have not been adequately addressed by current risk management systems and should be considered in future risk management systems.

[0055] The above embodiments are preferred implementation modes of the present invention, but the implementation modes of the present invention are not limited to the above embodiments. Any other changes, modifications, substitutions, combinations, and simplifications that do not deviate from the spirit and principles of the present invention should be considered as equivalent replacement methods and are included in the scope of protection of the present invention.

Claims

1. A comprehensive indoor health risk assessment method that integrates chemical exposure and microbial pathogenicity information, characterized by The following steps are involved: (1) Collecting media samples from indoor environments for chemical pollutant analysis and microbial community analysis; (2) Chemical contaminant analysis of media samples: extract chemical contaminants from media samples and perform quantitative analysis using instruments that match the target chemical contaminants; The chemical pollutants include, but are not limited to, one or more of organophosphates and their conversion products, polycyclic aromatic hydrocarbons, phthalates, antimicrobial agents, drug residues, and heavy metals; The organic phosphate ester includes one or more of an alkyl triester organic phosphate ester, a chloro triester organic phosphate ester and an aromatic triester organic phosphate ester; The organophosphate conversion products include organophosphate diesters and / or hydroxylated organophosphates; (3) Analysis of the microbial communities of the media samples: genomic DNA of the media samples was extracted, and the bacterial 16S rRNA gene was amplified and sequenced. The potential pathogenicity index of the obtained 16S rRNA sequencing data was predicted using BugBase software to obtain the pathogenicity potential score of the microbial community of each media sample; (4) Matching and integrating the quantitative data of chemical pollutants obtained in step (2) with the PPI values ​​corresponding to each medium sample in step (3), and using statistical correlation analysis methods to perform pairwise correlation analysis between the concentrations of chemical pollutants and PPI indicators, and calculating the correlation coefficient; (5) Carry out prediction analysis of nuclear receptor binding ability of chemical pollutants. Select human nuclear receptors closely related to environmental exposure, obtain high-resolution crystal structures of each receptor from the Protein Data Bank 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 with each receptor; (6) Taking into account the three main exposure pathways of skin contact, inhalation and hand-to-mouth ingestion, calculate the estimated daily intake (EDI) and hazard quotient (HQ) of the pollutant: Hand-to-mouth ingestion route: (1) Inhalation route: (2) Skin absorption route: (3) in EDI ing 、 EDI inh and EDI der EDI is the estimated daily intake (EDI) from hand-to-mouth ingestion, inhalation and skin absorption, in mg / kg. -1 day -1 ; C is the concentration of a single OPE in dust, ng·g -1 ; IR ing is the particle intake rate (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 body weight (kg); f ET is the proportion of exposure duration to the total daily time; For each compound and each exposure route, calculate HQ: （4） RfD is the safety threshold reference dose, in mg·kg -1 ·d -1 ; (7) Obtain the logD value of each chemical pollutant in the chemical property prediction software; (8) Multidimensional data integration and risk prioritization, using the HQ obtained in step (6) to characterize health risks, the predicted binding free energy obtained in step (5) to characterize endocrine disruption potential, the logD value obtained in step (7) to characterize physicochemical behavior characteristics, and the correlation coefficient obtained in step (4) to characterize microbial association; The above four index values ​​are subjected to minimum-maximum normalization processing, and the calculation method is as follows: （5） where χ Norm is the normalized value obtained from the indicators of different dimensions; χ is the original indicator value; χ Max and χ Min The maximum and minimum values ​​of this indicator for all target compounds involved in the evaluation; According to the contribution of each dimension to health risk, the weighted coefficient is determined as follows: Health risk W H 2; endocrine disrupting potential W E 2; physical and chemical behavior characteristics W C 1; microbial association W M is 1; The final comprehensive risk score ToxPi of chemical pollutants is calculated according to the following formula: （6） ToxPi i Represents the final comprehensive risk score of chemical pollutant i; HealthRisk i 、EndoEffect i 、ChemProp i and MicroAssoc i For each domain, Norm ; ToxPi scores range from 0 to 1, with higher values ​​indicating a higher priority for health risk management.

2. The evaluation method according to claim 1, wherein: The alkyl triester organic phosphate described 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, tris(2-butoxyethyl) phosphate and tris(2-ethylhexyl) phosphate.

3. The evaluation method according to claim 1, wherein: The chlorotriester organic phosphate described in step (2) includes one or more of tris(2-chloroethyl) phosphate, tris(2-chloroisopropyl) phosphate, tris(1,3-dichloroisopropyl) phosphate and 2,2-bischloromethyl-trimethylene-bis[bis(2-chloroethyl) phosphate].

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

5. The evaluation method according to claim 1, wherein: The organic phosphoric acid diester described in 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 ditolyl phosphate.

6. The evaluation method according to claim 1, wherein: The hydroxylated organic phosphate in step (2) includes 2-hydroxyethyl bis(2-butoxyethyl) phosphate and / or di(2-butoxyethyl) 2-(3-hydroxybutoxy)ethyl phosphate.

7. The evaluation method according to claim 1, wherein: The human nuclear receptors described in step (5) include thyroid hormone receptor α, thyroid hormone receptor β, estrogen receptor α, estrogen receptor β and androgen receptor.

8. The evaluation method according to claim 1, wherein: The medium sample in step (1) is dust, air, settling particles, air conditioning filter sediment or high-frequency contact surface swab.

9. The evaluation method according to claim 1, wherein: The indoor environment described in step (1) includes residences, offices, schools, businesses and entertainment venues.

10. The evaluation method according to claim 1, wherein: The polycyclic aromatic hydrocarbons described in step (2) are quantitatively analyzed by gas chromatography-mass spectrometry; the organophosphates and their conversion products, phthalates, antimicrobial agents and drug residues are quantitatively analyzed by high performance liquid chromatography-triple quadrupole mass spectrometry; and the heavy metals are quantitatively analyzed by inductively coupled plasma mass spectrometry or atomic absorption spectroscopy.

Images