The dust samples were collected from various urban areas of Riyadh city, the capital of Saudi Arabia. Riyadh is characterized by the desert climate with extreme hot weather conditions. The average summer and winter temperature ranges from 45°C−50°C and 5°C−20°C, respectively (Alsabhan et al., 2022). Due to surrounding desert and hot climate, dust storms are common in Riyadh. Twenty sites were selected for dust sample collection during summer 2023, including both indoor and outdoor environments (Figure 1). A wooden brush was used to collect dust samples from the surfaces through the sweeping method. The collected samples were kept in Ziploc and transported to the laboratory for further analyses. Visible trash and stones were removed from the samples, and then the dust samples were air-dried at room temperature (23°C–25°C).

To extract MPs from the collected dust samples, density separation method was employed using a ZnCl₂ solution with a density of 1.60 kg L^-1 (Liu et al., 2019). Briefly, ZnCl₂ solution was filtered using 0.22 µm membrane filters and 15 mg of the dust sample was placed in 50 mL of ZnCl₂ solution in a glass beaker. The suspension was stirred for 5 min, and thereafter covered and left static for 2 h. The upper suspended fraction was separated and placed in 20 mL of ZnCl₂ solution. After stirring, the MPs particles were separated with the help of 0.45 μm glass microfiber filters. The filters were placed in clean Petri dishes, dried in an oven at 35°C and subjected to further analyses.

The occurrence of MPs in dust samples was estimated through microscopic techniques. For the identification and quantification of MPs visual inspection methods were followed. Abundance, shape, and color of MPs were observed using a phase contrast microscope (DM2500, Leica^®, Germany). Thereafter, the morphology of MPs was inspected under a scanning electron microscopy (SEM; EFI S50 Inspect, Netherlands). Briefly, carbon conductive tabs were placed on aluminum stubs, and then dried filters were gently tapped on the stubs to spread MPs particles. The images of the MPs were taken using different magnifications. The captured images were processed further to measure the size of the MPs particles using ImageJ software (version 1.54d, National Institutes of Health, United States).

The exposure of human beings to MPs was estimated through calculating the estimated daily intake (EDI) of MPs via inhalation of dust particles with the help of Eq 1, whereas, the estimated annual intake (EAI) was calculated by using Eq. 2. E D I   M P s   k g − 1 d a y − 1 = C × m B W (1) E A I   M P s   k g − 1 y e a r − 1 = E D I × 365 (2)

Where, m is the average dust ingestion rate (g day^-1), C represents the number of MPs in dust, and BW represents body weight in kgs. The calculations were done based on different age groups, i.e., 1–5 year old (toddlers), 6–11 year old (children), 12–19 year old (teenagers), and ≥20 year old (adult). The average dust ingestion rate was used as 0.06 g day^-1 for toddlers, children, and teenagers, while 0.03 g day^-1 for adults (US EPA, 2011). The average body weights used in this study were 19, 29, 53, and 63 kg for toddlers, children, teenagers, and adults, respectively (Liao et al., 2012).

For heavy metal analysis, the samples passed through 100 µm sieve, and digested according to Hossner (Hossner, 1996). Approximately 0.5 g of dry, homogenized sample was digested using a mixture of 10 mL of concentrated HNO₃ and 70% of concentrated HClO₄ on a hot plate. The digestion process was conducted for approximately 30–40 min, and concentrated H₂O₂ was added to the sample to aid complete digestion. After digestion, the samples were filtered. The total heavy metals contents including of cadmium (Cd), copper (Cu), lead (Pb), iron (Fe), nickel (Ni), Zinc (Zn), Mercury (Hg), Selenium (Se), Cobalt (Co), Manganese (Mn), Molybdenum (Mn) and arsenic (As), were measured using inductively coupled plasma optical emission spectrometry (ICP-OES; PerkinElmer Optima 4300 DV, United States). All samples analyses were repeated three times for precision. (Maqsoom et al., 2020).

Enrichment factor (EF), pollution index (PI), integrated pollution index (IPI), and potential ecological risk index (RI) were used to assess the levels of heavy metal contamination (Chen et al., 2005). According to Sutherland (Sutherland et al., 2000), the EF value is a useful metric for determining the extent of metal pollution and predicting the likelihood of natural or man-made sources. Because Fe is naturally occurring, a reference concentration is token for normalization. The following equation was used to estimate the EF value: E F m = C m d u s t   s a m p l e / C F e d u s t   s a m p l e   C m e a r t h   c r u s t / C F e e a r t h   c r u s t (3) where C_{m (dust sample)} is the examined metal content in the dust sample, C_{Fe(dust sample)} is the content of the reference metal (Fe) in the dust sample, C_{m(earth crust)} is examined metal content in the earth’s crust, and C_{Fe(earth crust)} is the content of the reference metal (Fe) in the earth’s crust. Generally, EF values higher than two are mainly considered to have anthropogenic sources, while EF values less than two predominantly originate form background soil material. Moreover, the EF value also assists in estimating the metal contamination degree. Five categories of contamination are standard based on EF factor: EF ˂2 indicates deficiency to minimal enrichment; EF = two to five, indicates moderate enrichment; EF = 5–20, indicates significant enrichment; EF = 20–40, indicates very high enrichment; and EF ˃40, indicates extremely high enrichment.

The PI is estimated as the ratio of the metal concentration in the dust sample to the background content of the corresponding metal in the earth’s crust. The PI value can be classified as: PI ≤ 1, low level; 1 < PI ≤ 3, middle level; and PI > 3, high level. The IPI values of all measured metals in dust samples were identified as the mean value of the PI metals. The classified IPI is as follows: IPI ≤1, low level; 1 < IPI ≤2, middle level; and IPI >2, high level.

The potential ecological risks (RI) originally reported by Hakanson (1980) was also estimated to evaluate the degree of metal pollution in dust samples by using Eqs 4–6. R I = ∑ i = 1 n   E i , (4) E i = T i   f i , and (5) f i = C i B i , (6) where RI is the sum of all six risk factors for HMs, E_i is the monomial potential ecological risk factor, T_i is the metal toxic factor (with the values for each metal in the order of Zn = 1 < Cr = 2 < Cu = Ni = Pb = 5 < Cd = 30), f_i is the metal pollution factor, C_i is the metals concentration of in dust, and B_i is a reference value for metals (Hakanson, 1980). Different RI classifications of metal pollution are low ecological risk (RI ≤ 150), moderated ecological risk (150 ≤ RI < 300), considerable ecological risk (300 ≤ RI < 600), and high ecological risk (RI ≥ 600).

Risk assessment is a measure to estimate the nature and probability of heavy metals in an environment and their adverse effects on human health (Wang et al., 2014). Health risk assessment considers human exposure to dust via three various pathways, including inhalation pathway. The average daily dosages (ADDs) through inhalation pathway for dust was calculated according to the following Eq 7: A D D i n h = C ∗ i n h R ∗ E F ∗ E D B W ∗ A T (7)

Where, A D D i n h the average daily dose contacted through inhalation, C Concentration of metal in dust (mg kg^-1), i n h R for inhalation, E F the exposure frequency (day), E D The exposure duration (year), B W average body weight (kg), and A T average lifetime (day). The parameters values of the variables for human health risk are shown in Supplementary Table S1.

The non-carcinogenic quotients of exposure to heavy metals in dust was estimated. Non-cancer risks are expressed as a hazard quotient (HQ). The HQ is the quotient of the average daily dosages (ADDs) divided by the reference dosage of a specific heavy metals and for the exposure through inhalation pathway. The HQ of each metal was estimated by the following Eq 8: H Q = A D D R F D (8)

Where, H Q is hazard quotient, R F D is chronic inhalation reference dosage.

The indoor and outdoor dust samples collected from different locations in Riyadh city were analyzed for the presence of MPs. The microscopic analyses of the samples showed that all of the collected samples contained different levels of MPs. Figure 2 shows different shapes of MPs as detected through a phase contract microscope, while Figure 3 represents different MPs as observed under an SEM. The detected MPs were of different shapes and colors. However, red and black were the dominant colors, followed by green, brown, and transparent. The variation in the colors suggested multiple sources of MPs in dust samples. The black colored particles could be of microrubbers tire abrasions, black textile products, and building materials (Narmadha et al., 2020; Zhu et al., 2022). The transparent MPs could be due to the transparent polythene bags, disposable plastics as well as prolonged photoleaching of colored MPs (Nematollahi et al., 2022). The colorful MPs such as red, green, and brown could be due to consumable and highly durable plastic products (Nematollahi et al., 2022). Though color of MPs does not directly indicate the source; however, the identification of MPs based on their color is considered an important aspect as it may suggest a possible source.

The relative shape distribution of MPs particles in dust samples was also studied and presented in Figure 4. As of right now, several forms of air MPs have been identified, including films, spherules, granules, fibers, foam, and pellets (Abbasi et al., 2017; Abbasi et al., 2019; Stanton et al., 2019; Vianello et al., 2019). The different morphotypes’ existence is dependent upon the atmospheric compartment in which they are found as well as human activity in the immediate vicinity. According to the results of the study, the composition of MPs particles by shape varies. However, considering the overall results, it could be deduced that the shapes of the MPs particles are more abundant in the order fragments > granules > fibers > lines. The variation in the composition of MPs particles by shape was also affirmed by previous studies. An example of such is the research conducted by Abbasi et al., where fiber was reported to be the most abundant shape with a record 75.87% of observed morphotypes. On the contrary, granule (65.9%) and spherule (65.9%) were reported to be the dominant shapes among all the shapes present in the studies by Dehghani et al. (2017) and Abbasi et al. (2017), respectively.

Another metric for classifying MPs is size distribution. One goal of size reporting in atmospheric investigations is to ascertain their tendencies for ingestion and inhalation among the human population (Gasperi et al., 2018). Microplastic characteristics include size, color, and type, depending on the characteristics of the textiles used. For instance, in a study, the discovery of polypropylene particles was consistent with the polypropylene carpet in the living room (Abbasi et al., 2019). Figure 5 describes the relative size distribution of MPs particles in different dust samples collected from the city of Riyadh, Saudi Arabia. The classification of MPs by size is mostly reported in terms of particle length or diameter due to their irregular shapes. Across all the samples collected, the results of the study indicate that most of the MPs particles have sizes greater than 1,000 μm, followed by 501–1,000 µm sizes, then 251–500 µm sizes, followed by 101–250 µm sizes, and 1–100 µm sizes. Similarly, Dris et al. (2017) reported the predominance of microfiber distribution in the large particle size range (200–600 μm) with fewer fibers less than 200 μm. Dris et al. (2017) and Dehghani et al. (2017) reported a lower proportion of MPs in the smallest size range. It could be educed that larger sizes of MPs particles are more abundant as compared with smaller sizes.

The number of MPs (particles g^-1) in the dust samples collected from different locations in Riyadh, Saudi Arabia is presented in Figure 6. From the results of the study, sample collected from site-17 had the highest number of MPs (1,052 particles g^-1) while the least number of MPs (20 particles g^-1) was found in the dust sample collected from site-10. A significant number of MPs were also found in dust samples collected at site 3, site 11, site 15, and site 20. Overall, the results indicated the presence of a higher number of MPs particles in the outdoor samples as compared to indoor samples. The abundance of MPs particles in the outdoor environment has been associated to wind erosion paying a major role (Rezaei et al., 2019; La Porta et al., 2023).

Dust can be a source for the exposure of various environmental pollutants to humans. The occurrence of MPs in dust has come up as a widespread health concern for human beings in recent times. The results showed that toddlers were relatively more prone to the MPs ingestion through dust due to higher inhalation rate. Our calculations show that toddlers can potentially inhale 0.86 MPs particles kg^-1 day^-1 and 312.93 particles kg^-1 year^-1, suggesting a higher risk of MPs toxicity (Table 1). Followed by toddlers, children can inhale 0.65 particles kg^-1 year^-1, and teenagers can inhale 7.32 × 10⁻⁴ particles kg^-1 year^-1. Adults could inhale relatively lower MPs (6.98 × 10⁻⁷ particles kg^-1 year^-1). Similar results have previously been reported by Zhang et al. (2020) where it was suggested that toddlers can inhale 10-time higher MPs than adults through dust. Therefore, indoor and outdoor dust could be one to the important pathways for human exposure to MPs.

Table 2 reveals the concentrations of heavy metals in dust samples collected from the indoor and outdoor environments from Riyadh city. The results showed that the concentration of heavy metals varied from one site to other. The average Co, Cr, Cu, Fe, Mn, Ni, Pb, and Zn concentrations in the dust samples were 5.84, 28.20, 10.88, 11125.25, 175.68, 8.75, 3.37, and 162.94 mg kg^-1, respectively. On the other hand, Hg, Se, Cd, and Mo concentration was not detected in all dust road samples. Overall, the highest heavy metals concentrations varied from site to other. For instance, Co was highest at site 17, Cr was highest at site 1, Fe and Ni were highest at site 13, and Pb and Zn were highest at site 6. The heavy metals concentrations in dust samples were compared with guidelines, such as the common range in the earth’s crust, the average shale values, and the average concentrations in world soil. In our finding, the average concentrations heavy metals in dust samples were lower than their corresponding values of the guidelines except Zn, which was higher than their corresponding values of the guidelines (Turekian and Wedepohl, 1961; Lindsay, 1979; Huang et al., 2009; Al-Swadi et al., 2022; Alotaibi et al., 2022).

The maximum, minimum, and mean EF, PI, IPI, Ei, and RI parameters for all heavy metals in dust samples are shown in Table 3. The all calculated date is presented in Supplementary Table S2-4. Among all heavy metals in dust samples, the greatest EF values were recorded for Zn (57.37) at site 6, indicating extremely high enrichment. In addition, EF values at all sites recorded in two categories two to five and 5–20, indicating moderate and significant enrichment. Furthermore, EF values showed moderate enrichment for Cr (2.88) and Ni (3.08) at site-1, Pb (4.12) at site 6, Ni (3.18) at site 13, Co (2.64) and Cu (2.67) at site 14, Pb (2.45) at site 16, and Cu (6.15 and 4.16) at sites 16 and 20. Previously, it has been indicated that the EF index could be applied to identify the heavy metals source. Therefore, the EF index being less than two pointed out that the heavy metals originated from natural sources, and some previous studies reported that the EF values of the heavy metals less than two were not a major concerned contaminates (Yongming et al., 2006; Ye et al., 2011). In this context, the EF index for Zn in dust samples at all sites was more than 2, indication a major contamination concern and anthropogenic sources responsible for the existence of Zn in dust. Moreover, the aforementioned metals, such as Zn at all sites and Co (at site 14), Cr (at site 1), Cu (at sites 14, 18, and 20), Ni (at sites 1 and 13), and Pb (at sites 6 and 16), had EF values higher than 2, indicating that these metals were a major concern contaminant at these sites. Therefore, the anthropogenic influence was responsible, including tire tread, brake dust, and emission from the vehicles (Al-Swadi et al., 2022; Alotaibi et al., 2022).

The results revealed that the maximum, minimum, and mean PI index for most heavy metals in dust samples were <1, suggesting a low pollution level. On the contrary, the PI values of Cu at site 18, Ni at site 13, and Zn at sites 1, 4, 7, 12, 13, 18, 19, and 20 were in the range of 1 < PI ≤ 3, indicating a middle level of pollution. On the other hand, the PI value of Zn at sites 6, 8, and 17 was more than 3, indicating a high level of pollution. Likewise, the maximum, minimum, and mean IPI index of heavy metals in dust samples in all sites were less than 1, indicating a low level of pollution. Additionally, the evaluation of potential ecological risk was investigated in this study for Co, Cu, Ni, Pb, and Zn. The single Ei and integrated potential ecological risk (RI) are shown in Table 3; Supplementary Table S4. The findings showed that the maximum, minimum, and mean of RI index in all dust roads samples were classified by low ecological risk (RI ≤ 150). Generally, since each heavy metal pollution index has advantages and disadvantages of its own, no one approach can be suggested for risk evaluation. For instance, the PI index is simple to employ and has a precise scale, but it excludes the variations in natural sources for its calculations and ignores the available ability of heavy metals. Similarly, EF index can estimate the anthropogenic impacts of heavy metals and evaluate pollution caused by individual heavy metals, but it needs a reference value for its calculation.

The average daily dose of heavy metals for children and adults are shown in Supplementary Table S5, 6, while the HQ values are shown in Supplementary Table S7, 8. The results indicated that the HQ values for the investigated heavy metals in dust samples were higher for children than those of adults (Table 4). However, all the HQ values for heavy metals for all dust samples were <1, suggesting that there were no adverse health risks of the heavy metals on children or adults.