Morphological and Chemical Analysis of Indoor Airborne Microplastics: Implications for Human Health in Ahvaz, Iran

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This study evaluates AMP concentration, types, and health impacts in residential, office, and commercial settings in Ahvaz, Iran, during winter and summer. The annual inhaled AMP dose was calculated based on typical occupancy patterns. Methods AMP particles were collected from 30 locations using active sampling at 5 L/min for 8 hours. Raman spectroscopy identified polymers, and SEM-EDX analysis examined surface morphology and elemental composition. The inhaled dose was estimated using MP concentrations and typical indoor exposure times. Results The highest AMP concentrations were in offices during winter (up to 48 MPs/m³), moderate in residential areas, and lowest in commercial settings. Predominant AMPs were spherules (67.2% in winter, 69.3% in summer), with black/gray particles being most common. Smaller particles (< 250 µm) were more frequent in summer. The estimated annual inhaled AMP dose was 2,952 MPs/kg/year, mainly from residential, followed by offices and commercial spaces. Conclusions Results underscore the need for policies to reduce indoor AMP pollution, improve ventilation, and manage exposure risks, especially in high-occupancy areas like offices. Future research should focus on advanced chemical analyses and size-specific dose assessments to better evaluate health risks from inhaled microplastics. Airborne microplastics Indoor air quality Seasonal variation Chemical characterization Heavy metal adsorption Human exposure assessment Environmental health Figures Figure 1 1. Background Microplastics, generally defined as plastic particles with dimensions ranging from 5 mm to 100 nm (Thompson et al., 2004 ), have become a significant environmental concern owing to their widespread distribution and potential health risks. More recently, Hartmann et al. ( 2019 ) proposed an updated definition, categorizing microplastics as plastic particles ranging from 5 mm to 1 µm, according to the International Standard Unit nomenclature(Hartmann et al., 2019 ). Microplastics can be classified into primary and secondary categories. Primary microplastics are intentionally manufactured, such as microbeads, while secondary microplastics arise from the breakdown of larger plastic debris, including synthetic textile fibers (Song, Wang, & Li, 2024 ). This distinction is particularly relevant for understanding the aerodynamic behavior of these particles, which directly affects their transport in various environments, including indoor spaces. The pervasive nature of microplastics, derived from a multitude of sources such as plastic products, textiles, and industrial processes, has raised significant concerns regarding their potential health risks(Kannankai & Devipriya, 2024 ). Recent experimental studies have demonstrated that high concentrations of microplastics can cause physical damage to both ecosystems and living organisms, including humans, leading to inflammation and other adverse health effects(Zhao et al., 2024 ). The inhalation of microplastic particles has been linked to respiratory inflammation and systemic health effects, with some evidence suggesting that these particles may even enter the bloodstream and affect various organs(Lu et al., 2022 ; Saha & Saha, 2024 ; Vasse & Melgert, 2024 ). Although epidemiological studies have associated particulate matter exposure with respiratory and cardiovascular diseases, the specific health risks posed by airborne microplastics remain poorly understood(Lei et al., 2024 ; Singh, 2024 ). Research on microplastics has predominantly focused on aquatic environments, with few studies exploring the presence and effects of airborne microplastics, particularly in indoor settings(Tan et al., 2024 ). This gap is notable, as recent studies have indicated that indoor environments may contain higher concentrations of microplastics than outdoor spaces, largely due to the presence of synthetic textiles and other indoor sources(Jahandari, 2023 ; Kek et al., 2023 ). Furthermore, the complexities associated with measuring microplastics, particularly nanoplastics, make it challenging to fully understand their distribution and potential health impacts(Jing et al., 2024 ; Ye et al., 2024 ). The difficulty in accurately measuring airborne microplastics, coupled with the variability introduced by passive versus active sampling methods, has led to the underreporting of actual microplastic concentrations(Chen et al., 2020 ). To address these gaps, it is crucial to develop comprehensive sampling and analysis methods that can provide a more accurate assessment of airborne microplastics, particularly in indoor environments where human exposure is likely to be more significant. The current study aimed to investigate the presence, distribution, and potential health risks of indoor airborne microplastics in a variety of urban settings to capture a wide range of particle sizes and morphologies. By advancing our understanding of indoor microplastics, this study seeks to contribute to the broader discourse on environmental health and to inform strategies for mitigating the risks associated with microplastic exposure. 2. Materials and Methods 2.1 Study Area and Sampling Plan This study was conducted in Ahvaz, a city known for its industrial activities and unique climatic conditions. This study focused on indoor environments within four distinct urban areas: residential, commercial, office, and industrial. Thirty sampling sites were selected using ArcGIS to ensure broad representation across these different urban settings. Sampling was conducted in summer (July) and winter (December) to account for potential seasonal variations in microplastic pollution (Kaydi et al., 2024 ). 2.2 Sampling Methodology Active sampling was conducted in four key indoor environments representative of residential, commercial, office, and industrial settings, complementing passive sampling to specifically capture airborne microplastics. An SKC pump equipped with quartz microfilters was employed for the collection of airborne particles. The pump was positioned at a height of 1.2 to 1.8 meters to correspond with the average human breathing zone. The device operated at a flow rate of 5 L/min, continuously collecting air samples over an 8-hour period. Sampling was conducted when occupants were present, particularly in offices and commercial locations, during typical working hours. Active sampling took place in both the summer and winter seasons, resulting in a total of 60 samples collected across all locations. Following the collection, samples underwent rigorous laboratory analysis to identify the types and concentrations of microplastics present 2.3 Laboratory Analysis 2.3.1 Sample Preparation Upon arrival at the laboratory, the samples were pretreated to isolate microplastics from other particulate matter. This process included washing with hydrogen peroxide, incubation, and subsequent filtration through a vacuum filtration system equipped with mixed cellulose membrane filters. The filters were then dried, and the microplastic particles were identified and counted using a stereomicroscope. 2.3.2 Visual Analysis and Quantification Microplastics were examined and evaluated using a Digital Stereomicroscope (DSM3000) at 80× magnification. The process of identifying and counting microplastics followed specific guidelines, which included assessing uniform thickness, checking for the lack of visible organic structures, evaluating color consistency, and testing structural integrity when pressure was applied.(Liu et al., 2019 ). To differentiate microplastics from organic materials, researchers have conducted tactile examinations using sterilized needles and forceps. Any particles that broke apart when subjected to mild pressure were not categorized as microplastics.(Prata et al., 2020 ) To prevent sample contamination, all analyses were conducted under controlled conditions. Microplastics were characterized morphologically using four main criteria: shape (categorized as fibers, films, fragments, and spherules), size distribution (grouped into < 250 µm, = 250 µm, 251–500 µm, 501–1000 µm, and 1001–5000 µm), color (classified as White/Transparent, Yellow/Orange, Red/Pink, Black/Gray, and Blue/Green), and thickness (categorized as thin, medium, or thick). This thorough methodology allowed for the precise classification and quantification of microplastic particles while maintaining consistency with established protocols. 2.3.3 SEM-EDX Analysis A scanning electron microscope (TESCAN VEGA3) with energy dispersive X-ray spectroscopy (SEM-EDX) capabilities was employed to examine the surface morphology and elemental composition. For in-depth characterization, representative microplastic particles were randomly chosen from each sample group. The SEM technique utilizes a concentrated electron beam to produce high-resolution images of the particle surface topography. Using sterile metal tweezers and microscopic guidance, individual microplastic particles were extracted and placed on specimen stubs. To improve the conductivity, the mounted samples were coated with a metallic layer before analysis. The prepared specimens then underwent SEM-EDX examination, during which the surface morphological features were recorded at various magnifications. Simultaneously, EDX analysis yielded spectral data of the elemental makeup, with distinctive peaks corresponding to the elements present in the microplastic particles. This integrated SEM-EDX approach allowed for a thorough characterization of both the structural attributes and chemical composition of the isolated microplastic particles. 2.3.4 µ-Raman Spectroscopy Chemical characterization of the microplastic particles was performed using a µ-Raman spectroscopy system (Renishaw InVia, Horiba LabRAM HR Evolution). The system was equipped with a 633 nm near-infrared laser as the excitation source, operating at a power of 10 mW. Raman spectra were collected across a Raman shift range of 400–1800 cm⁻¹ with a spectral resolution of approximately 1 cm⁻¹. Representative particles encompassing diverse morphological characteristics and colorations were randomly selected from various sampling locations for spectroscopic analysis. Spectral acquisition and subsequent data processing were performed using Origin Lab software( https://www.originlab.com ) for peak identification and spectral visualization. Polymer identification was accomplished through comparative analysis of the characteristic Raman peaks with established reference spectra from peer-reviewed literature(Dong et al., 2020 ). 2.3.5 Quality Control Sample collection procedures adhered to standardized protocols, following either the ISO 16000-1:2004 guidelines for indoor air sampling (ISO, 2004 ) or the European Committee for Standardization (CEN/TC 264/WG 40) protocols for indoor air particulate matter measurements (Stacey, Mathe, & Amodeo, 2023 ). Comprehensive quality assurance measures were implemented throughout all the experimental phases, including sample collection, preparation, and analytical procedures. Laboratory glassware underwent rigorous cleaning protocols using ultrapure water (18.2 MΩ·cm at 25°C). Sample handling was conducted exclusively using metal tweezers to prevent cross-contamination. After collection, the samples were preserved in sterile petri dishes sealed with aluminum foil to maintain sample integrity. All solution preparations and washing procedures used HPLC-grade distilled water. Laboratory personnel have maintained stringent contamination control measures, including the use of powder-free latex gloves and clean laboratory attires. Sample exposure was minimized and restricted to essential analytical procedures to preserve sample integrity and prevent environmental contamination. 2.4 Statistical Analysis Statistical analyses were performed to evaluate the spatial and temporal distribution patterns of airborne microplastics across indoor environments in Ahvaz. The normality of the microplastic concentration data was confirmed using the Shapiro-Wilk test. Seasonal variations (summer versus winter) in microplastic concentrations were assessed using independent sample t-tests, whereas differences among indoor environments (residential, commercial, and office spaces) were evaluated using one-way analysis of variance (ANOVA). Multivariate linear regression models were constructed to identify significant predictors of microplastic concentrations, incorporating building characteristics and occupancy parameters as independent variables. Data management and initial processing were carried out using Microsoft Excel, while statistical analyses were performed using Stata/MP 17.0 (StataCorp). Statistical significance was established at p < 0.05 for all analyses. 2.5 Human Exposure Assessment Potential human exposure to airborne microplastics (AMPs) was quantified through inhalation exposure assessment calculations for occupants across the studied indoor environments. The exposure assessment considered the concentration of airborne microplastics, average inhalation rates, and duration of exposure based on typical indoor activities (Berry, 1991 ; Danilov & Benuzh, 2020 ; Diffey, 2011 ). The inhaled dose for active sampling of airborne microplastics (AMPs) was calculated using the Exposure Factor Equation and Inhalation Exposure Equation, as recommended by the Agency for Toxic Substances and Disease Registry (ATSDR) (ATSDR, 2016 ): Inhaled Dose (MPs/kg/year) = (EPC × EF) / BW × Days per Year where EPC is Exposure concentration of airborne microplastics (MPs/m³), EF is Exposure factor (percentage of time spent indoors), BW is Body weight (kg), and days per year are assumed to be 365 for annual calculations. This assessment considered the average indoor concentration of microplastics across different settings, as well as the duration of time adults typically spend indoors (home, office, and commercial environments). These findings provide insights into the potential health risks associated with microplastic exposure in indoor environments. 3. Results 3.1 Abundance and Distribution of AMPs Indoor microplastic concentrations (MPs/m³) across the different sampling locations in Ahvaz varied significantly, depending on several environmental and structural factors. Samples were collected from 30 indoor sites in residential, office, and commercial settings. As shown in Table 1 , microplastic concentrations ranged from 3 MPs/m³ to 48 MPs/m³ during the summer and from 2 MPs/m³ to 48 MPs/m³ during the winter. The highest concentration was observed in office buildings, whereas residential areas exhibited a moderate level of MPs. The minimum concentrations were predominantly observed in residential settings with fewer residents and adequate ventilation systems. Table 1 Descriptive Characteristics of Indoor Sampling Locations in Ahvaz for Microplastic Pollution Analysis No. Loc. Type of Setting Area of Building (m²) Building age Ventilation No of Residents Building Type MPs/m³ Summer Winter 1 A10 Residential < 100 5 Apartment 6 28 2 A12 Residential 10 Yes > 5 Apartment 13 23 3 A4 Offices > 100 > 10 No > 5 Villa 10 23 4 A8 Residential > 100 > 10 No > 5 Villa 14 48 5 A7 Offices > 100 < 10 Yes 100 > 10 No > 5 Villa 5 45 7 B9 Residential > 100 > 10 No > 5 Villa 13 18 8 B10 Residential > 100 5 Apartment 20 15 9 B6 Residential > 100 > 10 No > 5 Villa 7 10 B7 Residential > 100 < 10 Yes 100 5 Villa 8 10 12 C6 Residential > 100 < 10 Yes 100 > 10 Yes 100 > 10 No > 5 Villa 17 9 15 C11 Residential < 100 5 Villa 23 23 16 D1 Commercial > 100 > 10 No 100 > 10 No > 5 Villa 22 25 18 D7 Commercial 10 Yes > 5 Villa 17 21 19 D10 Residential > 100 > 10 Yes 100 5 Villa 7 9 21 E3 Offices 10 No > 5 Apartment 12 19 22 E5 Residential > 100 < 10 Yes 100 > 10 No > 5 Apartment 7 27 24 E10 Residential 10 Yes > 5 Apartment 8 23 25 F3 Residential > 100 < 10 Yes 100 < 10 Yes 100 < 10 Yes 100 > 10 Yes 100 > 10 Yes 100 < 10 No < 5 Villa 9 35 Regression analysis (Table 2 ) identified several significant factors influencing microplastic concentrations. The type of sampling site, building area, and season were all found to have a significant positive impact on MPs/m³ concentrations, with offices and larger building areas demonstrating the highest levels of microplastic contamination. The concentration of MPs was 4.703 MPs/m³ higher in winter than in summer (p < 0.001), suggesting that seasonal indoor activities and ventilation conditions play a pivotal role in microplastic accumulation. Table 2 Key Determinants of Microplastic Concentration in Indoor Environments of Ahvaz Variable Unstandardized Coefficients (B) Standardized Coefficients (Beta) 95% Confidence Interval Type Sampling Site 3.624 0.223 2.974–4.274 Year of Building -3.284 -0.118 -4.360 – -2.208 Type of Building 6.423 0.230 5.362–7.484 Area of Building 7.422 0.205 6.152–8.692 Number of Residents -7.557 -0.266 -8.700 – -6.414 Ventilation System -18.496 -0.158 -22.591 – -14.400 Conversely, older buildings and the presence of a ventilation system significantly reduced the MPs concentrations by 3.284 and 18.496 MPs/m³, respectively. Additionally, the number of residents had a negative impact on MPs concentration, indicating that higher occupancy was associated with fewer MPs, likely due to increased air movement and cleaning activities. 3.2 Morphological Characteristics Analysis of the microplastic characteristics revealed notable variations across different seasons and sampling settings in Ahvaz. Table 3 presents the seasonal characteristics of indoor microplastics, indicating that the average concentration of AMPs was higher during the winter months (28.25 ± 12.01 MPs/m³) compared to the summer months (26.27 ± 16.11 MPs/m³). The predominant type of microplastics identified was spherules, which constituted a significant portion of the total microplastics in both seasons, accounting for 69.3% in summer and 67.2% in winter. Table 3 Seasonal Characteristics of Indoor Microplastics in Ahvaz (Overall, Summer, and Winter) MP Characteristic Summer (n = 1156) Winter (n = 1520) MP Type Fiber 230 a (19.9%) 280 a (18.4%) 510 (19.1%) Film 42 a (3.6%) 23 b (1.5%) 65 (2.4%) Fragment 83 a (7.2%) 196 b (12.9%) 279 (10.4%) Spherule 801 a (69.3%) 1021 a (67.2%) 1822 (68.1%) MP Color White/Transparent 16 a (1.4%) 19 a (1.3%) 35 (1.3%) Yellow/Orange 108 a (9.3%) 138 a (9.1%) 246 (9.2%) Red/Pink 24 a (2.1%) 38 a (2.5%) 62 (2.3%) Black/Gray 965 a (83.5%) 1291 a (84.9%) 2256 (84.7%) Blue/Green 43 a (3.7%) 34 b (2.2%) 77 (2.9%) MP Size (µm) < 250 731 a (63.2%) 532 b (35.0%) 1263 (47.2%) = 250 206 a (17.8%) 733 b (48.2%) 939 (35.1%) 251–500 85 a (7.4%) 99 a (6.5%) 184 (6.9%) 501–1000 64 a (5.5%) 85 a (5.6%) 149 (5.6%) 1001–5000 70 a (6.1%) 71 a (4.7%) 141 (5.3%) MP Thickness Thick 9 a (0.8%) 1 b (0.1%) 10 (0.4%) Medium 33 a (2.9%) 23 b (1.5%) 56 (2.0%) Thin 1114 a (96.4%) 1496 b (98.4%) 2610 (97.6%) Concentration MP /m³ 26.27 ± 16.11 28.25 ± 12.01 27.39 ± 13.97 Each subscript letter denotes a subset of Type of Sampling Site categories whose column proportions do not differ significantly from each other at the .05 level. * Denote the highest concentrations among the sampling settings, indicating statistically significant differences. In terms of microplastic color, black and gray particles represented the majority in both seasons, comprising 83.5% of the total in summer and 84.9% in winter. This suggests that darker microplastics are a common feature of indoor environments. Conversely, the proportion of blue/green microplastics was significantly lower, at 3.7% in summer and 2.2% in winter, highlighting less common color variations. Analysis of microplastic size also revealed significant seasonal variations. As presented in Table 3 , microplastics less than 250 µm were more prevalent during summer (63.2% of the total concentration) than during winter (35.0%). In contrast, larger microplastics (≥ 250 µm) showed an increase in concentration during the winter months, particularly in the 250 µm size category, which increased to 48.2%. This variation suggests that seasonal factors may influence the distribution of microplastic sizes in the indoor air. The majority of microplastics identified were thin, accounting for 96.4% in summer and 98.4% in winter. The minimal presence of thick and medium microplastics emphasizes that finer particles dominate indoor air quality in the study area. Table 4 further illustrates the distribution of microplastic attributes across different sampling settings. The data indicated that residential areas had the highest concentration of fibers (21.4%), whereas commercial settings had the highest concentration of fragments (21.9%). In contrast, the proportion of spherules remained consistently high across all settings, underscoring their prevalence in indoor environments. Table 4 Distribution of Indoor Microplastic Attributes Across Different Sampling Settings in Ahvaz Attributes Residential (n = 19) Commercial (n = 4) Offices (n = 7) Total (n = 30) MP Type Fiber 333 a (21.4%) 78 a,b (17.5%) 99 b (14.7%) 510 (17.3%) Film 50 a (3.2%) 6 a,b (1.3%) 9 b (1.3%) 65 (2.2%) Fragment 93 a (6.0%) 38 a (8.5%) 148 b (21.9%) 279 (9.3%) Spherule 1079 a (69.4%) 324 a (72.6%) 419 b (62.1%) 1822 (61.2%) MP Color White/Transparent 28 a (1.8%) 1 b (0.2%) 6 a,b (0.9%) 35 (1.3%) Yellow/Orange 155 a (10.0%) 24 b (5.4%) 67a (9.9%) 246 (9.2%) Red/Pink 37 a (2.4%) 12 a (2.7%) 13 a (1.9%) 62 (2.3%) Black/Gray 1279 a (82.4%) 402 b (90.1%) 575 a (85.2%) 2256 (84.7%) Blue/Green 56 a (3.6%) 7 a (1.6%) 14 a (2.1%) 77 (2.9%) MP Size (µm) < 250 648 a (41.7%) 170 a (38.1%) 445 b (65.9%) 1263 (45.3%) = 250 593 a (38.1%) 205 b (46.0%) 141 c (20.9%) 939 (33.5%) 251–500 123 a (7.9%) 28 a,b (6.3%) 33 b (4.9%) 184 (6.6%) 501–1000 110 a (7.1%) 21 a,b (4.7%) 18 b (2.7%) 149 (5.4%) 1001–5000 81 a (5.2%) 22 a (4.9%) 38 a (5.6%) 141 (5.1%) MP Thickness Thick 7 a (0.5%) 1 a (0.2%) 2 a (0.3%) 10 (0.4%) Medium 43 a (2.8%) 5 a (1.1%) 8 a (1.2%) 56 (2.0%) Thin 1505 a (96.8%) 440 a (98.7%) 665 a (98.5%) 2610 (97.6%) Concentration MP/m³ 25.15 ± 13.32 28.84 ± 11.88 32.06 ± 14.45 * 27.39 ± 13.97 Each subscript letter denotes a subset of Type of Sampling Site categories whose column proportions do not differ significantly from each other at the .05 level. * Denote the highest concentrations among the sampling settings, indicating statistically significant differences. Statistical analyses confirmed significant differences in microplastic characteristics between summer and winter, as well as across different sampling settings. These variations emphasize the importance of understanding the sources and dynamics of microplastic pollution in indoor environments, which can inform targeted mitigation strategies. 3.3 µ-Raman spectroscopy In order to identify the polymer types present in suspected microplastic particles, µ-Raman spectroscopy was employed (Z. Li et al., 2023 ). The selected particles were sampled from various indoor settings, including residential, commercial, and office environments. The samples were handled using microneedles to minimize contamination during spectroscopic analysis. The µ-Raman spectra were processed and visualized using the Origin Lab software to interpret the characteristic polymer peaks. The resulting data are summarized in Table 6 , which details the Raman shifts and corresponding polymers identified for each particle across different settings. Table 6 Results from Micro Raman Spectra for All Indoor Particle Samples Sample ID Type of Setting Raman Shift (cm⁻¹) Identified Polymers E5 Residential 1061.03, 1327.66 PE, PP A12 Residential 1061.61, 1328.20, 1528.93 PE, PP, PVC A7 Offices 1063.91, 1326.56, 1359.24, 1455.91, 1528.93, 1572.19 PE, PP, PS, PVC, PET B8 Offices 488.64, 1155.13, 1179.22, 1213.98, 1283.13, 1341.57, 1573.75 PE, PP D6 Commercial 1342.12, 1572.97 PP, PE D7 Commercial 1283.41, 1573.49 PP, PE Raman shifts represent the characteristic peaks detected for each sample, allowing polymer identification based on known reference spectra. PE: Polyethylene, PP: Polypropylene, PS: Polystyrene, PVC: Polyvinyl Chloride, PET: Polyethylene Terephthalate. The analysis revealed the presence of common polymers such as polyethylene (PE), polypropylene (PP), polystyrene (PS), polyvinyl chloride (PVC), and polyethylene terephthalate (PET). For instance, a sample from an office setting (A7) displayed prominent Raman shifts at 1063.91, 1326.56, 1359.24, 1455.91, 1528.93, and 1572.19 cm⁻¹, indicating the presence of PE, PP, PS, PVC, and PET. Similarly, a residential sample (E5) exhibited shifts at 1061.03 and 1327.66 cm⁻¹, corresponding to PE and PP, respectively. Commercial settings have also shown diverse polymer types. For example, sample D6 from a commercial environment had peaks at 1342.12 and 1572.97 cm⁻¹, indicating PP and PE, while sample D7 demonstrated shifts at 1283.41 and 1573.49 cm⁻¹, also representing PP and PE. 3.5. Surface morphology and elemental composition SEM analysis revealed distinctive surface characteristics of the microplastic particles, exhibiting varied morphological features indicative of environmental degradation (Fig. 1 ). EDX spectroscopy identified predominant elemental compositions of C 40.80%) and O 36.78%), consistent with the organic polymer composition of common plastics such as polyethylene (PE), polypropylene (PP), polystyrene (PS), and polyethylene terephthalate (PET). Additional elements detected included silicon (Si; 7.23%), mercury (Hg; 4.89%), barium (Ba; 3.31%), and calcium (Ca; 1.84%), alongside trace concentrations of sodium (Na), aluminum (Al), magnesium (Mg), and lead (Pb). The presence of these inorganic elements, particularly heavy metals, indicates significant environmental interactions and pollutant adsorption on microplastic surfaces (Liu et al., 2020 ). 3.4 Human Exposure Estimates Human exposure to airborne microplastics (AMPs) through inhalation was assessed using the indoor concentration of MPs and typical exposure factors. The annual inhaled dose of MPs was estimated for adults based on the time spent in different indoor environments, including home, office, and commercial spaces (see Table 5 ). Table 5 Annual Exposure Assessment of Airborne Microplastics in Indoor Settings Among Adults in Ahvaz Setting Average Indoor Concentration (MPs/m³) Time Spent (%) EF EF-adjusted Air Concentration (MPs/m³) Inhaled Dose (MPs/kg/year) Home 25.15 60 0.60 15.09 1,808.08 Office 32.06* 30 0.30 9.62 1,152.27 Commercial 28.84 5 0.05 1.44 172.23 Overall 27.39 90 0.90 24.65 2,952.05 EF = Exposure Factor, calculated based on time spent in each setting; The inhaled dose is calculated using body weight (70 kg); *Denotes the highest concentration setting among the evaluated environments.; The overall average concentration (27.39 MPs/m³) is a weighted average, calculated based on the time spent in each setting and the corresponding average indoor concentrations. The home environment accounted for the largest proportion of time spent (60%), followed by office settings (30%) and commercial spaces (5%)(Berry, 1991 ; Danilov & Benuzh, 2020 ; Diffey, 2011 ). Although offices exhibited the highest average indoor concentration of MPs (32.06 MPs/m³), the overall inhaled dose was highest for time spent at home due to the significant proportion of time spent in this setting. The total annual inhaled dose of microplastics was estimated to be 2,952 MPs/kg/year, with contributions from homes (1,808 MPs/kg/year), offices (1,152 MPs/kg/year), and commercial spaces (172 MPs/kg/year). 4. Discussion This study provides a comprehensive analysis of the distribution, physicochemical characteristics, and potential health risks associated with airborne microplastics (AMPs) in indoor environments across Ahvaz. The observed concentration patterns revealed that the highest AMP levels occurred in office settings, followed by moderate concentrations in residential spaces. This finding is consistent with previous studies, where indoor microplastic concentrations were shown to be influenced by the type of setting (Kashfi et al., 2022 ; Jianqiang Zhu et al., 2022 ), with offices typically hosting higher activity levels and foot traffic, contributing to a greater AMP presence (Ageel, Harrad, & Abdallah, 2022 ; Eberhard et al., 2024 ; Maurizi et al., 2024 ). Building characteristics, particularly the age of the building and the presence of ventilation systems, play a significant role in determining AMP concentrations (Kashfi et al., 2022 ). This study found that older buildings and those with ventilation systems had lower AMP levels. This is likely due to improved air circulation and filtration in ventilated environments, which reduces the accumulation of airborne particles, whereas older buildings may have fewer microplastic sources owing to less modern materials (Savanti, Setyowati, & Hardiman, 2022 ; Jianqiang Zhu et al., 2022 ). Seasonal analysis revealed that AMP concentrations were significantly higher during winter than in summer, which aligns with findings in other studies that attributed this seasonal variation to reduced ventilation rates and increased indoor occupancy during colder months (John et al., 2023 ). The higher concentrations of larger microplastic particles during winter also suggest reduced environmental turbulence, which may slow particle fragmentation (W. Li et al., 2023 ). Chemical characterization of AMPs using µ-Raman spectroscopy indicated the presence of several common polymers, including polyethylene (PE), polypropylene (PP), polystyrene (PS), and polyethylene terephthalate (PET). Spectral analysis revealed shifts consistent with these polymers: around 1440 cm⁻¹ and 2850 cm⁻¹ for PE; near 840 cm⁻¹, 1375 cm⁻¹, and 2850 cm⁻¹ for PP; around 1000 cm⁻¹, 1595 cm⁻¹, and 1600 cm⁻¹ for PS; and at 1710 cm⁻¹ for PET (Jessop, 2023 ; Lenz et al., 2015 ; Sheng, Zhang, & Zhang, 2021 ; Jiahui Zhu et al., 2022 ). These findings align with the widespread use of these polymers in households (Akhbarizadeh et al., 2021 ; Allen et al., 2019 ) and industrial products, contributing to their prevalence in indoor environments (Elia, Stylianou, & Agapiou, 2024 ; Wojtyła et al., 2020 ). Surface morphological analysis using SEM and elemental analysis using EDX demonstrated a significant degradation of the microplastic particles. The increased surface roughness and fragmentation observed on these particles indicate prolonged environmental exposure and weathering, which enhances their capacity to adsorb pollutants(Costigan et al., 2022 ). Notably, heavy metals such as mercury (Hg), barium (Ba), and lead (Pb) have been detected on the surfaces of AMPs (Hanun et al., 2023 ). This finding raises concerns regarding the potential role of microplastics as vectors for toxic substances in indoor environments. The calculated annual inhalation exposure doses reinforce these concerns. With the highest AMP concentrations found in office settings, individuals who spend significant time in these environments are at a greater risk of exposure. Although residential spaces exhibited lower AMP levels, the amount of time spent at home contributed the most to the overall inhaled dose. It is important to note that the possible inhalation of microplastics depends on their size, which determines whether they will reach the respiratory system. Particles < 10 µm are toxicologically relevant because of their greater probability of crossing epithelial barriers and increased reactivity with cells and tissues(Wadhawan et al., 2024 ). However, in our current study, we did not capture the size in the inhalation dose calculations, which is a limitation that should be addressed in future research. This study has several strengths, including the sampling of 30 locations from diverse settings and geographical locations across the two seasons, in alignment with standard protocols. This approach provides a robust database for future studies and can inform the implementation of policies and actions to reduce indoor pollution. However, there are limitations to consider. Subsampling for Raman spectroscopy and EDX analysis, while aiming to provide representative samples, may introduce some bias in the results (Feng, Zheng, & Liu, 2023 ). Future studies could benefit from more advanced chemical analysis techniques, such as FTIR, to provide a more comprehensive characterization of microplastics. 5. Conclusion This study offers a detailed characterization of airborne microplastics in the indoor environment of Ahvaz, demonstrating significant correlations between AMP concentrations and environmental factors, such as seasonality, building characteristics, and ventilation systems. The results indicate that office environments host the highest concentrations of microplastics, with significantly higher seasonal variation during the winter months, likely driven by reduced ventilation and increased indoor occupancy. Morphological and elemental analyses suggest that microplastic particles undergo significant environmental degradation, enhancing their capacity for hazardous metal adsorption. The detection of heavy metals, such as mercury, barium, and lead, on microplastic surfaces presents elevated health risks through inhalation exposure. These findings contribute to the growing body of evidence on the health implications of indoor microplastic pollution and underscore the need for further toxicological research focusing on respiratory exposure pathways. This study highlights the necessity for robust indoor air quality management strategies to mitigate microplastic exposure risks. Policies aimed at reducing microplastic emissions, improving ventilation systems, and enhancing public awareness of microplastic pollution in indoor environments are essential steps toward safeguarding public health. Future research should focus on more precise size-based inhalation dose calculations and employ advanced chemical analysis techniques to further our understanding of the complexities of indoor microplastic pollution. Declarations Acknowledgments We acknowledge the generous support of Ahvaz Jundishapur University of Medical Sciences, which provided funding for this study through grant ETRC-0205. This support was instrumental in enabling us to conduct our research and make meaningful contributions to the field of environmental science. Statements & Declarations Funding Ahvaz Jundishapur University of Medical Sciences provided funding for this study through grant ETRC-0205. Competing Interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper Author Contributions Neda Kaydi: Conducted the investigation, validated the results, managed and curated the data, created visualizations, prepared the original draft, and contributed to writing. Morteza Abdullatif Khafaie: Developed methodology, provided supervision, and contributed to reviewing and editing the manuscript. Neamatollah Jaafarzadeh Haghighi Fard: Led conceptualization and provided supervision. Sahand Jorfi and Afshin Takdastan: Contributed to manuscript reviewing and editing. Ethics approval This study received ethical approval from the Institutional Review Board of Ahvaz Jundishapur University of Medical Sciences (IR.AJUMS.REC.1402.042). Consent to participate Informed consent was obtained from all individual participants included in the study. Consent to publish Not applicable Availability of data and materials Data supporting the findings of this study are available from the corresponding author upon reasonable request References Ageel, H. K., Harrad, S., & Abdallah, M. A.-E. (2022). Occurrence, human exposure, and risk of microplastics in the indoor environment. Environmental Science: Processes & Impacts, 24 (1), 17–31. Akhbarizadeh, R., Dobaradaran, S., Amouei Torkmahalleh, M., Saeedi, R., Aibaghi, R., & Faraji Ghasemi, F. (2021). Suspended fine particulate matter (PM2.5), microplastics (MPs), and polycyclic aromatic hydrocarbons (PAHs) in air: Their possible relationships and health implications. 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Supplementary Files floatimage1.png Cite Share Download PDF Status: Published Journal Publication published 01 Mar, 2025 Read the published version in Environmental Geochemistry and Health → Version 1 posted Editorial decision: Revision requested 14 Dec, 2024 Reviews received at journal 09 Dec, 2024 Reviews received at journal 05 Dec, 2024 Reviewers agreed at journal 18 Nov, 2024 Reviewers agreed at journal 14 Nov, 2024 Reviewers invited by journal 13 Nov, 2024 Editor assigned by journal 13 Nov, 2024 Submission checks completed at journal 13 Nov, 2024 First submitted to journal 12 Nov, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5440514","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":388180336,"identity":"3476c1ff-123d-4065-90e1-bf983d29fa73","order_by":0,"name":"Neda Kaydi","email":"","orcid":"","institution":"Environmental Technologies Research Center, Medical Basic Sciences Research Institute","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Neda","middleName":"","lastName":"Kaydi","suffix":""},{"id":388180337,"identity":"ec50a8c8-7b4a-42f7-9dc5-1ef4cccbab17","order_by":1,"name":"Sahand Jorfi","email":"","orcid":"","institution":"Environmental Technologies Research Center, Medical Basic Sciences Research Institute","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Sahand","middleName":"","lastName":"Jorfi","suffix":""},{"id":388180338,"identity":"ef205a1f-15b0-472c-8539-3b20b24bfce0","order_by":2,"name":"Afshin Takdastan","email":"","orcid":"","institution":"Environmental Technologies Research Center, Medical Basic Sciences Research Institute","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Afshin","middleName":"","lastName":"Takdastan","suffix":""},{"id":388180339,"identity":"21d37c81-da53-4e8e-bebd-465efa076696","order_by":3,"name":"Neamatollah Jaafarzadeh Haghighifard","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA90lEQVRIiWNgGAWjYHCCBAYGGzDDgOEDkGRjJ0pLGkQL4wyQFmaiLIJqYeYBUYS0mDMwPPzwI8EuX7798MbHNr+2yfMxMzB++JiDW4tlA0OyZE9CsmVjT1qxcW7fbcM2ZgZmyZnbcGsxOMCQIMH7g9mAmSHHTDq35zYjUAsbMy9+Lck//yTUG7DxvzH/bdlz254YLWnSPAmHDXgkcsyYGX7cTiSs5TBDmrVMwnEDCYlnxZK9DbeT25gZm/H75XhP8s03CdUG8v3JGz/8+HPbdn5788EPH/FoYWDmSUBwGNvAZAMe9SDAfgCJ84eA4lEwCkbBKBiRAADK3EvRYxwCFwAAAABJRU5ErkJggg==","orcid":"","institution":"Environmental Technologies Research Center, Medical Basic Sciences Research Institute","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Neamatollah","middleName":"Jaafarzadeh","lastName":"Haghighifard","suffix":""},{"id":388180340,"identity":"64549745-7230-4c8c-8736-4b15ff5c4746","order_by":4,"name":"Morteza Abdullatif Khafaie","email":"","orcid":"","institution":"Environmental Technologies Research Center, Medical Basic Sciences Research Institute","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Morteza","middleName":"Abdullatif","lastName":"Khafaie","suffix":""}],"badges":[],"createdAt":"2024-11-12 14:53:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5440514/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5440514/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10653-025-02399-8","type":"published","date":"2025-03-01T15:57:19+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":71136284,"identity":"07cfef53-73a2-4f7f-bc9e-c24d55b3457e","added_by":"auto","created_at":"2024-12-11 13:13:29","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":349432,"visible":true,"origin":"","legend":"\u003cp\u003eSEM Images Illustrating the Morphology of Airborne Microplastics Collected in Various Indoor Urban Settings.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5440514/v1/883da464423dd62fdf0566bc.png"},{"id":77622420,"identity":"75689cb1-750a-4c5e-b2ab-130e38464b03","added_by":"auto","created_at":"2025-03-03 16:06:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1772973,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5440514/v1/909d1a8a-02fd-46a1-b49a-114586dcfd00.pdf"},{"id":71136282,"identity":"434445db-8f63-4377-abf6-2273421effe8","added_by":"auto","created_at":"2024-12-11 13:13:29","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1244829,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5440514/v1/16fa2b20b37ef4b618e16be2.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Morphological and Chemical Analysis of Indoor Airborne Microplastics: Implications for Human Health in Ahvaz, Iran","fulltext":[{"header":"1. Background","content":"\u003cp\u003eMicroplastics, generally defined as plastic particles with dimensions ranging from 5 mm to 100 nm (Thompson et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), have become a significant environmental concern owing to their widespread distribution and potential health risks. More recently, Hartmann et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) proposed an updated definition, categorizing microplastics as plastic particles ranging from 5 mm to 1 \u0026micro;m, according to the International Standard Unit nomenclature(Hartmann et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Microplastics can be classified into primary and secondary categories. Primary microplastics are intentionally manufactured, such as microbeads, while secondary microplastics arise from the breakdown of larger plastic debris, including synthetic textile fibers (Song, Wang, \u0026amp; Li, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This distinction is particularly relevant for understanding the aerodynamic behavior of these particles, which directly affects their transport in various environments, including indoor spaces.\u003c/p\u003e \u003cp\u003eThe pervasive nature of microplastics, derived from a multitude of sources such as plastic products, textiles, and industrial processes, has raised significant concerns regarding their potential health risks(Kannankai \u0026amp; Devipriya, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Recent experimental studies have demonstrated that high concentrations of microplastics can cause physical damage to both ecosystems and living organisms, including humans, leading to inflammation and other adverse health effects(Zhao et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The inhalation of microplastic particles has been linked to respiratory inflammation and systemic health effects, with some evidence suggesting that these particles may even enter the bloodstream and affect various organs(Lu et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Saha \u0026amp; Saha, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Vasse \u0026amp; Melgert, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Although epidemiological studies have associated particulate matter exposure with respiratory and cardiovascular diseases, the specific health risks posed by airborne microplastics remain poorly understood(Lei et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Singh, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eResearch on microplastics has predominantly focused on aquatic environments, with few studies exploring the presence and effects of airborne microplastics, particularly in indoor settings(Tan et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This gap is notable, as recent studies have indicated that indoor environments may contain higher concentrations of microplastics than outdoor spaces, largely due to the presence of synthetic textiles and other indoor sources(Jahandari, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Kek et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Furthermore, the complexities associated with measuring microplastics, particularly nanoplastics, make it challenging to fully understand their distribution and potential health impacts(Jing et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Ye et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The difficulty in accurately measuring airborne microplastics, coupled with the variability introduced by passive versus active sampling methods, has led to the underreporting of actual microplastic concentrations(Chen et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo address these gaps, it is crucial to develop comprehensive sampling and analysis methods that can provide a more accurate assessment of airborne microplastics, particularly in indoor environments where human exposure is likely to be more significant. The current study aimed to investigate the presence, distribution, and potential health risks of indoor airborne microplastics in a variety of urban settings to capture a wide range of particle sizes and morphologies. By advancing our understanding of indoor microplastics, this study seeks to contribute to the broader discourse on environmental health and to inform strategies for mitigating the risks associated with microplastic exposure.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Area and Sampling Plan\u003c/h2\u003e \u003cp\u003eThis study was conducted in Ahvaz, a city known for its industrial activities and unique climatic conditions. This study focused on indoor environments within four distinct urban areas: residential, commercial, office, and industrial. Thirty sampling sites were selected using ArcGIS to ensure broad representation across these different urban settings. Sampling was conducted in summer (July) and winter (December) to account for potential seasonal variations in microplastic pollution (Kaydi et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Sampling Methodology\u003c/h2\u003e \u003cp\u003eActive sampling was conducted in four key indoor environments representative of residential, commercial, office, and industrial settings, complementing passive sampling to specifically capture airborne microplastics. An SKC pump equipped with quartz microfilters was employed for the collection of airborne particles. The pump was positioned at a height of 1.2 to 1.8 meters to correspond with the average human breathing zone. The device operated at a flow rate of 5 L/min, continuously collecting air samples over an 8-hour period. Sampling was conducted when occupants were present, particularly in offices and commercial locations, during typical working hours. Active sampling took place in both the summer and winter seasons, resulting in a total of 60 samples collected across all locations. Following the collection, samples underwent rigorous laboratory analysis to identify the types and concentrations of microplastics present\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Laboratory Analysis\u003c/h2\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1 \u003cem\u003eSample Preparation\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eUpon arrival at the laboratory, the samples were pretreated to isolate microplastics from other particulate matter. This process included washing with hydrogen peroxide, incubation, and subsequent filtration through a vacuum filtration system equipped with mixed cellulose membrane filters. The filters were then dried, and the microplastic particles were identified and counted using a stereomicroscope.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2 Visual Analysis and Quantification\u003c/h2\u003e \u003cp\u003eMicroplastics were examined and evaluated using a Digital Stereomicroscope (DSM3000) at 80\u0026times; magnification. The process of identifying and counting microplastics followed specific guidelines, which included assessing uniform thickness, checking for the lack of visible organic structures, evaluating color consistency, and testing structural integrity when pressure was applied.(Liu et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). To differentiate microplastics from organic materials, researchers have conducted tactile examinations using sterilized needles and forceps. Any particles that broke apart when subjected to mild pressure were not categorized as microplastics.(Prata et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) To prevent sample contamination, all analyses were conducted under controlled conditions. Microplastics were characterized morphologically using four main criteria: shape (categorized as fibers, films, fragments, and spherules), size distribution (grouped into \u0026lt;\u0026thinsp;250 \u0026micro;m, =\u0026thinsp;250 \u0026micro;m, 251\u0026ndash;500 \u0026micro;m, 501\u0026ndash;1000 \u0026micro;m, and 1001\u0026ndash;5000 \u0026micro;m), color (classified as White/Transparent, Yellow/Orange, Red/Pink, Black/Gray, and Blue/Green), and thickness (categorized as thin, medium, or thick). This thorough methodology allowed for the precise classification and quantification of microplastic particles while maintaining consistency with established protocols.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.3.3 SEM-EDX Analysis\u003c/h2\u003e \u003cp\u003eA scanning electron microscope (TESCAN VEGA3) with energy dispersive X-ray spectroscopy (SEM-EDX) capabilities was employed to examine the surface morphology and elemental composition. For in-depth characterization, representative microplastic particles were randomly chosen from each sample group. The SEM technique utilizes a concentrated electron beam to produce high-resolution images of the particle surface topography. Using sterile metal tweezers and microscopic guidance, individual microplastic particles were extracted and placed on specimen stubs. To improve the conductivity, the mounted samples were coated with a metallic layer before analysis. The prepared specimens then underwent SEM-EDX examination, during which the surface morphological features were recorded at various magnifications. Simultaneously, EDX analysis yielded spectral data of the elemental makeup, with distinctive peaks corresponding to the elements present in the microplastic particles. This integrated SEM-EDX approach allowed for a thorough characterization of both the structural attributes and chemical composition of the isolated microplastic particles.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.3.4 \u0026micro;-Raman Spectroscopy\u003c/h2\u003e \u003cp\u003eChemical characterization of the microplastic particles was performed using a \u0026micro;-Raman spectroscopy system (Renishaw InVia, Horiba LabRAM HR Evolution). The system was equipped with a 633 nm near-infrared laser as the excitation source, operating at a power of 10 mW. Raman spectra were collected across a Raman shift range of 400\u0026ndash;1800 cm⁻\u0026sup1; with a spectral resolution of approximately 1 cm⁻\u0026sup1;. Representative particles encompassing diverse morphological characteristics and colorations were randomly selected from various sampling locations for spectroscopic analysis. Spectral acquisition and subsequent data processing were performed using Origin Lab software(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.originlab.com\u003c/span\u003e\u003cspan address=\"https://www.originlab.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) for peak identification and spectral visualization. Polymer identification was accomplished through comparative analysis of the characteristic Raman peaks with established reference spectra from peer-reviewed literature(Dong et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.3.5 Quality Control\u003c/h2\u003e \u003cp\u003eSample collection procedures adhered to standardized protocols, following either the ISO 16000-1:2004 guidelines for indoor air sampling (ISO, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) or the European Committee for Standardization (CEN/TC 264/WG 40) protocols for indoor air particulate matter measurements (Stacey, Mathe, \u0026amp; Amodeo, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Comprehensive quality assurance measures were implemented throughout all the experimental phases, including sample collection, preparation, and analytical procedures. Laboratory glassware underwent rigorous cleaning protocols using ultrapure water (18.2 MΩ\u0026middot;cm at 25\u0026deg;C). Sample handling was conducted exclusively using metal tweezers to prevent cross-contamination. After collection, the samples were preserved in sterile petri dishes sealed with aluminum foil to maintain sample integrity. All solution preparations and washing procedures used HPLC-grade distilled water. Laboratory personnel have maintained stringent contamination control measures, including the use of powder-free latex gloves and clean laboratory attires. Sample exposure was minimized and restricted to essential analytical procedures to preserve sample integrity and prevent environmental contamination.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Statistical Analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed to evaluate the spatial and temporal distribution patterns of airborne microplastics across indoor environments in Ahvaz. The normality of the microplastic concentration data was confirmed using the Shapiro-Wilk test. Seasonal variations (summer versus winter) in microplastic concentrations were assessed using independent sample t-tests, whereas differences among indoor environments (residential, commercial, and office spaces) were evaluated using one-way analysis of variance (ANOVA). Multivariate linear regression models were constructed to identify significant predictors of microplastic concentrations, incorporating building characteristics and occupancy parameters as independent variables. Data management and initial processing were carried out using Microsoft Excel, while statistical analyses were performed using Stata/MP 17.0 (StataCorp). Statistical significance was established at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for all analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Human Exposure Assessment\u003c/h2\u003e \u003cp\u003ePotential human exposure to airborne microplastics (AMPs) was quantified through inhalation exposure assessment calculations for occupants across the studied indoor environments. The exposure assessment considered the concentration of airborne microplastics, average inhalation rates, and duration of exposure based on typical indoor activities (Berry, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Danilov \u0026amp; Benuzh, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Diffey, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The inhaled dose for active sampling of airborne microplastics (AMPs) was calculated using the Exposure Factor Equation and Inhalation Exposure Equation, as recommended by the Agency for Toxic Substances and Disease Registry (ATSDR) (ATSDR, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e):\u003c/p\u003e \u003cp\u003eInhaled Dose (MPs/kg/year) = (EPC \u0026times; EF) / BW \u0026times; Days per Year\u003c/p\u003e \u003cp\u003ewhere EPC is Exposure concentration of airborne microplastics (MPs/m\u0026sup3;), EF is Exposure factor (percentage of time spent indoors), BW is Body weight (kg), and days per year are assumed to be 365 for annual calculations.\u003c/p\u003e \u003cp\u003eThis assessment considered the average indoor concentration of microplastics across different settings, as well as the duration of time adults typically spend indoors (home, office, and commercial environments). These findings provide insights into the potential health risks associated with microplastic exposure in indoor environments.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Abundance and Distribution of AMPs\u003c/h2\u003e \u003cp\u003eIndoor microplastic concentrations (MPs/m\u0026sup3;) across the different sampling locations in Ahvaz varied significantly, depending on several environmental and structural factors. Samples were collected from 30 indoor sites in residential, office, and commercial settings. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, microplastic concentrations ranged from 3 MPs/m\u0026sup3; to 48 MPs/m\u0026sup3; during the summer and from 2 MPs/m\u0026sup3; to 48 MPs/m\u0026sup3; during the winter. The highest concentration was observed in office buildings, whereas residential areas exhibited a moderate level of MPs. The minimum concentrations were predominantly observed in residential settings with fewer residents and adequate ventilation systems.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive Characteristics of Indoor Sampling Locations in Ahvaz for Microplastic Pollution Analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLoc.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eType of Setting\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eArea of Building (m\u0026sup2;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBuilding age\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVentilation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNo of Residents\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBuilding Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eMPs/m\u0026sup3;\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eSummer\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eWinter\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eResidential\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eApartment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eResidential\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eApartment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOffices\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eVilla\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eResidential\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eVilla\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOffices\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eVilla\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOffices\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eVilla\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eResidential\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eVilla\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eResidential\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eApartment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eResidential\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eVilla\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eResidential\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eApartment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eResidential\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eVilla\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eResidential\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eApartment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eResidential\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eApartment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOffices\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eVilla\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eResidential\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eVilla\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eD1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCommercial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eApartment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eD6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCommercial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eVilla\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eD7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCommercial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eVilla\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eD10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eResidential\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eApartment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eResidential\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eVilla\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOffices\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eApartment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eResidential\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eVilla\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOffices\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eApartment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eResidential\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eApartment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eResidential\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eApartment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eResidential\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eApartment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eResidential\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eApartment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eResidential\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eVilla\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOffices\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eVilla\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCommercial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eVilla\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eRegression analysis (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) identified several significant factors influencing microplastic concentrations. The type of sampling site, building area, and season were all found to have a significant positive impact on MPs/m\u0026sup3; concentrations, with offices and larger building areas demonstrating the highest levels of microplastic contamination. The concentration of MPs was 4.703 MPs/m\u0026sup3; higher in winter than in summer (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting that seasonal indoor activities and ventilation conditions play a pivotal role in microplastic accumulation.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eKey Determinants of Microplastic Concentration in Indoor Environments of Ahvaz\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnstandardized Coefficients (B)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandardized Coefficients (Beta)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95% Confidence Interval\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType Sampling Site\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.974\u0026ndash;4.274\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear of Building\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-3.284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-4.360 \u0026ndash; -2.208\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType of Building\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.362\u0026ndash;7.484\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArea of Building\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.152\u0026ndash;8.692\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of Residents\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-7.557\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-8.700 \u0026ndash; -6.414\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVentilation System\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-18.496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-22.591 \u0026ndash; -14.400\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eConversely, older buildings and the presence of a ventilation system significantly reduced the MPs concentrations by 3.284 and 18.496 MPs/m\u0026sup3;, respectively. Additionally, the number of residents had a negative impact on MPs concentration, indicating that higher occupancy was associated with fewer MPs, likely due to increased air movement and cleaning activities.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Morphological Characteristics\u003c/h2\u003e \u003cp\u003eAnalysis of the microplastic characteristics revealed notable variations across different seasons and sampling settings in Ahvaz. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the seasonal characteristics of indoor microplastics, indicating that the average concentration of AMPs was higher during the winter months (28.25\u0026thinsp;\u0026plusmn;\u0026thinsp;12.01 MPs/m\u0026sup3;) compared to the summer months (26.27\u0026thinsp;\u0026plusmn;\u0026thinsp;16.11 MPs/m\u0026sup3;). The predominant type of microplastics identified was spherules, which constituted a significant portion of the total microplastics in both seasons, accounting for 69.3% in summer and 67.2% in winter.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSeasonal Characteristics of Indoor Microplastics in Ahvaz (Overall, Summer, and Winter)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMP Characteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSummer (n\u0026thinsp;=\u0026thinsp;1156)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWinter (n\u0026thinsp;=\u0026thinsp;1520)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eMP Type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFiber\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e230\u003csub\u003ea\u003c/sub\u003e (19.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e280 \u003csub\u003ea\u003c/sub\u003e (18.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e510 (19.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFilm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42 \u003csub\u003ea\u003c/sub\u003e (3.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23\u003csub\u003eb\u003c/sub\u003e (1.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65 (2.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFragment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83 \u003csub\u003ea\u003c/sub\u003e (7.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e196 \u003csub\u003eb\u003c/sub\u003e (12.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e279 (10.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpherule\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e801 \u003csub\u003ea\u003c/sub\u003e (69.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1021 \u003csub\u003ea\u003c/sub\u003e (67.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1822 (68.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eMP Color\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite/Transparent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 \u003csub\u003ea\u003c/sub\u003e (1.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 \u003csub\u003ea\u003c/sub\u003e (1.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35 (1.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYellow/Orange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e108 \u003csub\u003ea\u003c/sub\u003e (9.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e138 \u003csub\u003ea\u003c/sub\u003e (9.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e246 (9.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRed/Pink\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24 \u003csub\u003ea\u003c/sub\u003e (2.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38 \u003csub\u003ea\u003c/sub\u003e (2.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62 (2.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack/Gray\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e965 \u003csub\u003ea\u003c/sub\u003e (83.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1291 \u003csub\u003ea\u003c/sub\u003e (84.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2256 (84.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlue/Green\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43 \u003csub\u003ea\u003c/sub\u003e (3.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34 \u003csub\u003eb\u003c/sub\u003e (2.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e77 (2.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eMP Size (\u0026micro;m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e731\u003csub\u003ea\u003c/sub\u003e (63.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e532\u003csub\u003eb\u003c/sub\u003e (35.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1263 (47.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e=\u0026thinsp;250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e206\u003csub\u003ea\u003c/sub\u003e (17.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e733\u003csub\u003eb\u003c/sub\u003e (48.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e939 (35.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e251\u0026ndash;500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85\u003csub\u003ea\u003c/sub\u003e (7.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e99\u003csub\u003ea\u003c/sub\u003e (6.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e184 (6.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e501\u0026ndash;1000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64 \u003csub\u003ea\u003c/sub\u003e (5.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85\u003csub\u003ea\u003c/sub\u003e (5.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e149 (5.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1001\u0026ndash;5000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70\u003csub\u003ea\u003c/sub\u003e (6.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71\u003csub\u003ea\u003c/sub\u003e (4.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e141 (5.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eMP Thickness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThick\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003csub\u003ea\u003c/sub\u003e (0.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003csub\u003eb\u003c/sub\u003e (0.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (0.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33\u003csub\u003ea\u003c/sub\u003e (2.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23\u003csub\u003eb\u003c/sub\u003e (1.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56 (2.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1114\u003csub\u003ea\u003c/sub\u003e (96.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1496\u003csub\u003eb\u003c/sub\u003e (98.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2610 (97.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eConcentration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMP /m\u0026sup3;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.27\u0026thinsp;\u0026plusmn;\u0026thinsp;16.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.25\u0026thinsp;\u0026plusmn;\u0026thinsp;12.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.39\u0026thinsp;\u0026plusmn;\u0026thinsp;13.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eEach subscript letter denotes a subset of Type of Sampling Site categories whose column proportions do not differ significantly from each other at the .05 level.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e* Denote the highest concentrations among the sampling settings, indicating statistically significant differences.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn terms of microplastic color, black and gray particles represented the majority in both seasons, comprising 83.5% of the total in summer and 84.9% in winter. This suggests that darker microplastics are a common feature of indoor environments. Conversely, the proportion of blue/green microplastics was significantly lower, at 3.7% in summer and 2.2% in winter, highlighting less common color variations.\u003c/p\u003e \u003cp\u003eAnalysis of microplastic size also revealed significant seasonal variations. As presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, microplastics less than 250 \u0026micro;m were more prevalent during summer (63.2% of the total concentration) than during winter (35.0%). In contrast, larger microplastics (\u0026ge;\u0026thinsp;250 \u0026micro;m) showed an increase in concentration during the winter months, particularly in the 250 \u0026micro;m size category, which increased to 48.2%. This variation suggests that seasonal factors may influence the distribution of microplastic sizes in the indoor air.\u003c/p\u003e \u003cp\u003eThe majority of microplastics identified were thin, accounting for 96.4% in summer and 98.4% in winter. The minimal presence of thick and medium microplastics emphasizes that finer particles dominate indoor air quality in the study area.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e further illustrates the distribution of microplastic attributes across different sampling settings. The data indicated that residential areas had the highest concentration of fibers (21.4%), whereas commercial settings had the highest concentration of fragments (21.9%). In contrast, the proportion of spherules remained consistently high across all settings, underscoring their prevalence in indoor environments.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistribution of Indoor Microplastic Attributes Across Different Sampling Settings in Ahvaz\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAttributes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResidential (n\u0026thinsp;=\u0026thinsp;19)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCommercial (n\u0026thinsp;=\u0026thinsp;4)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOffices (n\u0026thinsp;=\u0026thinsp;7)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;30)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eMP Type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFiber\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e333\u003csub\u003ea\u003c/sub\u003e (21.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78\u003csub\u003ea,b\u003c/sub\u003e (17.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e99\u003csub\u003eb\u003c/sub\u003e (14.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e510 (17.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFilm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50\u003csub\u003ea\u003c/sub\u003e (3.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003csub\u003ea,b\u003c/sub\u003e (1.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9\u003csub\u003eb\u003c/sub\u003e (1.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e65 (2.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFragment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e93\u003csub\u003ea\u003c/sub\u003e (6.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38\u003csub\u003ea\u003c/sub\u003e (8.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e148\u003csub\u003eb\u003c/sub\u003e (21.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e279 (9.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpherule\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1079\u003csub\u003ea\u003c/sub\u003e (69.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e324\u003csub\u003ea\u003c/sub\u003e (72.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e419\u003csub\u003eb\u003c/sub\u003e (62.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1822 (61.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eMP Color\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite/Transparent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28\u003csub\u003ea\u003c/sub\u003e (1.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003csub\u003eb\u003c/sub\u003e (0.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003csub\u003ea,b\u003c/sub\u003e (0.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35 (1.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYellow/Orange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e155\u003csub\u003ea\u003c/sub\u003e (10.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24\u003csub\u003eb\u003c/sub\u003e (5.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67a (9.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e246 (9.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRed/Pink\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37\u003csub\u003ea\u003c/sub\u003e (2.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12\u003csub\u003ea\u003c/sub\u003e (2.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13\u003csub\u003ea\u003c/sub\u003e (1.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e62 (2.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack/Gray\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1279\u003csub\u003ea\u003c/sub\u003e (82.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e402\u003csub\u003eb\u003c/sub\u003e (90.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e575\u003csub\u003ea\u003c/sub\u003e (85.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2256 (84.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlue/Green\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56\u003csub\u003ea\u003c/sub\u003e (3.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003csub\u003ea\u003c/sub\u003e (1.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14\u003csub\u003ea\u003c/sub\u003e (2.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e77 (2.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eMP Size (\u0026micro;m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e648\u003csub\u003ea\u003c/sub\u003e (41.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e170\u003csub\u003ea\u003c/sub\u003e (38.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e445\u003csub\u003eb\u003c/sub\u003e (65.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1263 (45.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e=\u0026thinsp;250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e593\u003csub\u003ea\u003c/sub\u003e (38.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e205\u003csub\u003eb\u003c/sub\u003e (46.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e141\u003csub\u003ec\u003c/sub\u003e (20.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e939 (33.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e251\u0026ndash;500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e123\u003csub\u003ea\u003c/sub\u003e (7.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28\u003csub\u003ea,b\u003c/sub\u003e (6.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33\u003csub\u003eb\u003c/sub\u003e (4.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e184 (6.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e501\u0026ndash;1000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e110\u003csub\u003ea\u003c/sub\u003e (7.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21\u003csub\u003ea,b\u003c/sub\u003e (4.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18\u003csub\u003eb\u003c/sub\u003e (2.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e149 (5.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1001\u0026ndash;5000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81\u003csub\u003ea\u003c/sub\u003e (5.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22\u003csub\u003ea\u003c/sub\u003e (4.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38\u003csub\u003ea\u003c/sub\u003e (5.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e141 (5.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eMP Thickness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThick\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003csub\u003ea\u003c/sub\u003e (0.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003csub\u003ea\u003c/sub\u003e (0.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003csub\u003ea\u003c/sub\u003e (0.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10 (0.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43\u003csub\u003ea\u003c/sub\u003e (2.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003csub\u003ea\u003c/sub\u003e (1.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8\u003csub\u003ea\u003c/sub\u003e (1.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e56 (2.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1505\u003csub\u003ea\u003c/sub\u003e (96.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e440\u003csub\u003ea\u003c/sub\u003e (98.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e665\u003csub\u003ea\u003c/sub\u003e (98.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2610 (97.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eConcentration\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMP/m\u0026sup3;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.15\u0026thinsp;\u0026plusmn;\u0026thinsp;13.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.84\u0026thinsp;\u0026plusmn;\u0026thinsp;11.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32.06\u0026thinsp;\u0026plusmn;\u0026thinsp;14.45 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.39\u0026thinsp;\u0026plusmn;\u0026thinsp;13.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eEach subscript letter denotes a subset of Type of Sampling Site categories whose column proportions do not differ significantly from each other at the .05 level.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e* Denote the highest concentrations among the sampling settings, indicating statistically significant differences.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eStatistical analyses confirmed significant differences in microplastic characteristics between summer and winter, as well as across different sampling settings. These variations emphasize the importance of understanding the sources and dynamics of microplastic pollution in indoor environments, which can inform targeted mitigation strategies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.3 \u0026micro;-Raman spectroscopy\u003c/h2\u003e \u003cp\u003eIn order to identify the polymer types present in suspected microplastic particles, \u0026micro;-Raman spectroscopy was employed (Z. Li et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The selected particles were sampled from various indoor settings, including residential, commercial, and office environments. The samples were handled using microneedles to minimize contamination during spectroscopic analysis. The \u0026micro;-Raman spectra were processed and visualized using the Origin Lab software to interpret the characteristic polymer peaks. The resulting data are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e6\u003c/span\u003e, which details the Raman shifts and corresponding polymers identified for each particle across different settings.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults from Micro Raman Spectra for All Indoor Particle Samples\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eType of Setting\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRaman Shift (cm⁻\u0026sup1;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIdentified\u003c/p\u003e \u003cp\u003ePolymers\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResidential\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1061.03, 1327.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePE, PP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResidential\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1061.61, 1328.20, 1528.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePE, PP, PVC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOffices\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1063.91, 1326.56, 1359.24, 1455.91, 1528.93, 1572.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePE, PP, PS, PVC, PET\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOffices\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e488.64, 1155.13, 1179.22, 1213.98, 1283.13, 1341.57, 1573.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePE, PP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCommercial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1342.12, 1572.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePP, PE\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCommercial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1283.41, 1573.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePP, PE\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eRaman shifts represent the characteristic peaks detected for each sample, allowing polymer identification based on known reference spectra.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003ePE: Polyethylene, PP: Polypropylene, PS: Polystyrene, PVC: Polyvinyl Chloride, PET: Polyethylene Terephthalate.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe analysis revealed the presence of common polymers such as polyethylene (PE), polypropylene (PP), polystyrene (PS), polyvinyl chloride (PVC), and polyethylene terephthalate (PET). For instance, a sample from an office setting (A7) displayed prominent Raman shifts at 1063.91, 1326.56, 1359.24, 1455.91, 1528.93, and 1572.19 cm⁻\u0026sup1;, indicating the presence of PE, PP, PS, PVC, and PET. Similarly, a residential sample (E5) exhibited shifts at 1061.03 and 1327.66 cm⁻\u0026sup1;, corresponding to PE and PP, respectively.\u003c/p\u003e \u003cp\u003eCommercial settings have also shown diverse polymer types. For example, sample D6 from a commercial environment had peaks at 1342.12 and 1572.97 cm⁻\u0026sup1;, indicating PP and PE, while sample D7 demonstrated shifts at 1283.41 and 1573.49 cm⁻\u0026sup1;, also representing PP and PE.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.5. \u003cem\u003eSurface morphology and elemental composition\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eSEM analysis revealed distinctive surface characteristics of the microplastic particles, exhibiting varied morphological features indicative of environmental degradation (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). EDX spectroscopy identified predominant elemental compositions of C 40.80%) and O 36.78%), consistent with the organic polymer composition of common plastics such as polyethylene (PE), polypropylene (PP), polystyrene (PS), and polyethylene terephthalate (PET). Additional elements detected included silicon (Si; 7.23%), mercury (Hg; 4.89%), barium (Ba; 3.31%), and calcium (Ca; 1.84%), alongside trace concentrations of sodium (Na), aluminum (Al), magnesium (Mg), and lead (Pb). The presence of these inorganic elements, particularly heavy metals, indicates significant environmental interactions and pollutant adsorption on microplastic surfaces (Liu et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Human Exposure Estimates\u003c/h2\u003e \u003cp\u003eHuman exposure to airborne microplastics (AMPs) through inhalation was assessed using the indoor concentration of MPs and typical exposure factors. The annual inhaled dose of MPs was estimated for adults based on the time spent in different indoor environments, including home, office, and commercial spaces (see Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnnual Exposure Assessment of Airborne Microplastics in Indoor Settings Among Adults in Ahvaz\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSetting\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAverage Indoor Concentration (MPs/m\u0026sup3;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTime Spent (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEF-adjusted Air Concentration (MPs/m\u0026sup3;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eInhaled Dose (MPs/kg/year)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1,808.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOffice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32.06*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1,152.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommercial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e172.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2,952.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eEF\u0026thinsp;=\u0026thinsp;Exposure Factor, calculated based on time spent in each setting; The inhaled dose is calculated using body weight (70 kg); *Denotes the highest concentration setting among the evaluated environments.; The overall average concentration (27.39 MPs/m\u0026sup3;) is a weighted average, calculated based on the time spent in each setting and the corresponding average indoor concentrations.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe home environment accounted for the largest proportion of time spent (60%), followed by office settings (30%) and commercial spaces (5%)(Berry, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Danilov \u0026amp; Benuzh, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Diffey, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Although offices exhibited the highest average indoor concentration of MPs (32.06 MPs/m\u0026sup3;), the overall inhaled dose was highest for time spent at home due to the significant proportion of time spent in this setting. The total annual inhaled dose of microplastics was estimated to be 2,952 MPs/kg/year, with contributions from homes (1,808 MPs/kg/year), offices (1,152 MPs/kg/year), and commercial spaces (172 MPs/kg/year).\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study provides a comprehensive analysis of the distribution, physicochemical characteristics, and potential health risks associated with airborne microplastics (AMPs) in indoor environments across Ahvaz. The observed concentration patterns revealed that the highest AMP levels occurred in office settings, followed by moderate concentrations in residential spaces. This finding is consistent with previous studies, where indoor microplastic concentrations were shown to be influenced by the type of setting (Kashfi et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Jianqiang Zhu et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), with offices typically hosting higher activity levels and foot traffic, contributing to a greater AMP presence (Ageel, Harrad, \u0026amp; Abdallah, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Eberhard et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Maurizi et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBuilding characteristics, particularly the age of the building and the presence of ventilation systems, play a significant role in determining AMP concentrations (Kashfi et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This study found that older buildings and those with ventilation systems had lower AMP levels. This is likely due to improved air circulation and filtration in ventilated environments, which reduces the accumulation of airborne particles, whereas older buildings may have fewer microplastic sources owing to less modern materials (Savanti, Setyowati, \u0026amp; Hardiman, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Jianqiang Zhu et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSeasonal analysis revealed that AMP concentrations were significantly higher during winter than in summer, which aligns with findings in other studies that attributed this seasonal variation to reduced ventilation rates and increased indoor occupancy during colder months (John et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The higher concentrations of larger microplastic particles during winter also suggest reduced environmental turbulence, which may slow particle fragmentation (W. Li et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eChemical characterization of AMPs using \u0026micro;-Raman spectroscopy indicated the presence of several common polymers, including polyethylene (PE), polypropylene (PP), polystyrene (PS), and polyethylene terephthalate (PET). Spectral analysis revealed shifts consistent with these polymers: around 1440 cm⁻\u0026sup1; and 2850 cm⁻\u0026sup1; for PE; near 840 cm⁻\u0026sup1;, 1375 cm⁻\u0026sup1;, and 2850 cm⁻\u0026sup1; for PP; around 1000 cm⁻\u0026sup1;, 1595 cm⁻\u0026sup1;, and 1600 cm⁻\u0026sup1; for PS; and at 1710 cm⁻\u0026sup1; for PET (Jessop, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Lenz et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Sheng, Zhang, \u0026amp; Zhang, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Jiahui Zhu et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These findings align with the widespread use of these polymers in households (Akhbarizadeh et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Allen et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and industrial products, contributing to their prevalence in indoor environments (Elia, Stylianou, \u0026amp; Agapiou, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Wojtyła et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSurface morphological analysis using SEM and elemental analysis using EDX demonstrated a significant degradation of the microplastic particles. The increased surface roughness and fragmentation observed on these particles indicate prolonged environmental exposure and weathering, which enhances their capacity to adsorb pollutants(Costigan et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Notably, heavy metals such as mercury (Hg), barium (Ba), and lead (Pb) have been detected on the surfaces of AMPs (Hanun et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This finding raises concerns regarding the potential role of microplastics as vectors for toxic substances in indoor environments.\u003c/p\u003e \u003cp\u003eThe calculated annual inhalation exposure doses reinforce these concerns. With the highest AMP concentrations found in office settings, individuals who spend significant time in these environments are at a greater risk of exposure. Although residential spaces exhibited lower AMP levels, the amount of time spent at home contributed the most to the overall inhaled dose. It is important to note that the possible inhalation of microplastics depends on their size, which determines whether they will reach the respiratory system. Particles\u0026thinsp;\u0026lt;\u0026thinsp;10 \u0026micro;m are toxicologically relevant because of their greater probability of crossing epithelial barriers and increased reactivity with cells and tissues(Wadhawan et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, in our current study, we did not capture the size in the inhalation dose calculations, which is a limitation that should be addressed in future research.\u003c/p\u003e \u003cp\u003eThis study has several strengths, including the sampling of 30 locations from diverse settings and geographical locations across the two seasons, in alignment with standard protocols. This approach provides a robust database for future studies and can inform the implementation of policies and actions to reduce indoor pollution. However, there are limitations to consider. Subsampling for Raman spectroscopy and EDX analysis, while aiming to provide representative samples, may introduce some bias in the results (Feng, Zheng, \u0026amp; Liu, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Future studies could benefit from more advanced chemical analysis techniques, such as FTIR, to provide a more comprehensive characterization of microplastics.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study offers a detailed characterization of airborne microplastics in the indoor environment of Ahvaz, demonstrating significant correlations between AMP concentrations and environmental factors, such as seasonality, building characteristics, and ventilation systems. The results indicate that office environments host the highest concentrations of microplastics, with significantly higher seasonal variation during the winter months, likely driven by reduced ventilation and increased indoor occupancy.\u003c/p\u003e \u003cp\u003eMorphological and elemental analyses suggest that microplastic particles undergo significant environmental degradation, enhancing their capacity for hazardous metal adsorption. The detection of heavy metals, such as mercury, barium, and lead, on microplastic surfaces presents elevated health risks through inhalation exposure. These findings contribute to the growing body of evidence on the health implications of indoor microplastic pollution and underscore the need for further toxicological research focusing on respiratory exposure pathways.\u003c/p\u003e \u003cp\u003eThis study highlights the necessity for robust indoor air quality management strategies to mitigate microplastic exposure risks. Policies aimed at reducing microplastic emissions, improving ventilation systems, and enhancing public awareness of microplastic pollution in indoor environments are essential steps toward safeguarding public health. Future research should focus on more precise size-based inhalation dose calculations and employ advanced chemical analysis techniques to further our understanding of the complexities of indoor microplastic pollution.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledge the generous support of Ahvaz Jundishapur University of Medical Sciences, which provided funding for this study through grant ETRC-0205. This support was instrumental in enabling us to conduct our research and make meaningful contributions to the field of environmental science.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatements \u0026amp; Declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAhvaz Jundishapur University of Medical Sciences provided funding for this study through grant ETRC-0205.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNeda Kaydi: Conducted the investigation, validated the results, managed and curated the data, created visualizations, prepared the original draft, and contributed to writing. \u0026nbsp;Morteza Abdullatif Khafaie: Developed methodology, provided supervision, and contributed to reviewing and editing the manuscript. Neamatollah Jaafarzadeh Haghighi Fard: Led conceptualization and provided supervision. Sahand Jorfi and Afshin Takdastan: Contributed to manuscript reviewing and editing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study received ethical approval from the Institutional Review Board of Ahvaz Jundishapur University of Medical Sciences (IR.AJUMS.REC.1402.042).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eAvailability of data and materials\u003c/h3\u003e\n\u003cp\u003eData supporting the findings of this study are available from the corresponding author upon reasonable request\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAgeel, H. 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Science of the Total Environment, \u003cem\u003e833\u003c/em\u003e, 155256. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/https://doi.org/10.1016/j.scitotenv.2022.155256\u003c/span\u003e\u003cspan address=\"10.1016/j.scitotenv.2022.155256\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-geochemistry-and-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"egah","sideBox":"Learn more about [Environmental Geochemistry and Health](https://www.springer.com/journal/10653)","snPcode":"10653","submissionUrl":"https://submission.nature.com/new-submission/10653/3","title":"Environmental Geochemistry and Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Airborne microplastics, Indoor air quality, Seasonal variation, Chemical characterization, Heavy metal adsorption, Human exposure assessment, Environmental health","lastPublishedDoi":"10.21203/rs.3.rs-5440514/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5440514/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eAim\u003c/h2\u003e \u003cp\u003eAirborne microplastics (AMPs) present significant health risks indoors due to prolonged exposure. This study evaluates AMP concentration, types, and health impacts in residential, office, and commercial settings in Ahvaz, Iran, during winter and summer. The annual inhaled AMP dose was calculated based on typical occupancy patterns.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eAMP particles were collected from 30 locations using active sampling at 5 L/min for 8 hours. Raman spectroscopy identified polymers, and SEM-EDX analysis examined surface morphology and elemental composition. The inhaled dose was estimated using MP concentrations and typical indoor exposure times.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe highest AMP concentrations were in offices during winter (up to 48 MPs/m\u0026sup3;), moderate in residential areas, and lowest in commercial settings. Predominant AMPs were spherules (67.2% in winter, 69.3% in summer), with black/gray particles being most common. Smaller particles (\u0026lt;\u0026thinsp;250 \u0026micro;m) were more frequent in summer. The estimated annual inhaled AMP dose was 2,952 MPs/kg/year, mainly from residential, followed by offices and commercial spaces.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eResults underscore the need for policies to reduce indoor AMP pollution, improve ventilation, and manage exposure risks, especially in high-occupancy areas like offices. Future research should focus on advanced chemical analyses and size-specific dose assessments to better evaluate health risks from inhaled microplastics.\u003c/p\u003e","manuscriptTitle":"Morphological and Chemical Analysis of Indoor Airborne Microplastics: Implications for Human Health in Ahvaz, Iran","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-11 13:05:25","doi":"10.21203/rs.3.rs-5440514/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-12-14T22:54:28+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-09T17:45:07+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-05T13:23:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"312120540286758041681812878618900124945","date":"2024-11-19T03:20:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"88798636624540076194896419054003574207","date":"2024-11-14T05:19:54+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-11-13T22:04:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-11-13T13:52:39+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-11-13T10:43:11+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Geochemistry and Health","date":"2024-11-12T14:45:17+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-geochemistry-and-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"egah","sideBox":"Learn more about [Environmental Geochemistry and Health](https://www.springer.com/journal/10653)","snPcode":"10653","submissionUrl":"https://submission.nature.com/new-submission/10653/3","title":"Environmental Geochemistry and Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"d7e02b66-5ddc-4e99-b581-900640f95e21","owner":[],"postedDate":"December 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-03-03T16:00:30+00:00","versionOfRecord":{"articleIdentity":"rs-5440514","link":"https://doi.org/10.1007/s10653-025-02399-8","journal":{"identity":"environmental-geochemistry-and-health","isVorOnly":false,"title":"Environmental Geochemistry and Health"},"publishedOn":"2025-03-01 15:57:19","publishedOnDateReadable":"March 1st, 2025"},"versionCreatedAt":"2024-12-11 13:05:25","video":"","vorDoi":"10.1007/s10653-025-02399-8","vorDoiUrl":"https://doi.org/10.1007/s10653-025-02399-8","workflowStages":[]},"version":"v1","identity":"rs-5440514","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5440514","identity":"rs-5440514","version":["v1"]},"buildId":"FbvkV6FR0MCFSLy54lSbu","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}