Introduction

Air pollution is a matter of great concern because it can seriously harm both people and the environment. Atmospheric pollution is a serious threat on the global level and has been a persistent problem for decades due to unregulated industrial development and growing urbanisation (Li et al., 2022; Lima et al., 2021; Ramírez et al., 2020). According to a recent finding, one out of every eight people dies due to air pollution (Vural, 2020). Lung diseases are associated with 23% of the deaths caused by air pollution, as are cardiovascular disease (19%), ischemic disease (24%), and stroke (21%) (Sahu et al., 2021). The World Health Organization (WHO) reported that approximately 91% of the global population lives in the areas where pollutant levels surpass the guideline value (WHO, 2019). Air pollutants such as, nitrogen oxides, sulphur oxides, volatile organics, stratospheric ozone, and atmospheric particulates are the most studied pollutants because they exhibit detrimental effects on humans, deteriorate the environment, and change the climate (Alves et al., 2023; Kalaiarasan et al., 2018; Oliveira et al., 2021; Zhao et al., 2023).

Airborne particulates (including PM2.5, PM10, and TSP) are a condensed mixture of microscopic solid particles and liquid droplets in the atmosphere that involve atmospheric progression (Brodny & Tutak, 2021). Amongst these, fine/respirable particulate matter (PM2.5) has gained much attention and is regarded as more hazardous in the atmosphere (Ahmad et al., 2021; Liu et al., 2020; Mehmood et al., 2020). According to the state of global air report, most of the world-wide population (> 90%) is susceptible to PM2.5 concentrations that exceed WHO guidelines, with developing nations particularly more exposed (State of Global Air Report, 2020). The leading anthropogenic sources of airborne particles are forest fires, biofuel combustion, solid waste, and the open burning of municipal/domestic, and farming wastes. Similarly, uncontrolled industrial growth, brick kilns, power plants, crushing and foundries, cement manufacturing, iron/steel industries, and mining are important anthropogenic sources (Liu et al., 2020; Silva et al., 2022). Different photochemical reactions can also produce fine particulates in the atmosphere (Lv et al., 2019). Inhalation of PM2.5 has detrimental influence on human health because respirable particulates can easily enter the human respiratory system, accumulate in the inner parts of lungs, and may cause respiration problems (Schwela & Haq, 2020). Exposure to PM2.5 is also linked with an increased risk of emphysema, strokes, and chronic obstructive pulmonary disease. Around 90% of the preventive fatalities take place in low-income and overpopulated countries due to the increased prevalence of these diseases, especially in Southeast Asia and the Pacific regions (WHO, 2021a). Previous studies have demonstrated that exposure to elevated PM2.5 levels can cause early death and disruption to the sensory organs, cellular structure, and function (Nair et al., 2020; Tong, 2019). The studies also revealed that PM2.5 contents exceeding 10 µg/m3 are linked with a 4% rise in pulmonary fatalities, a 9% rise in respiratory diseases, and a 17% increase in cardiovascular diseases (Bayat et al., 2019). These airborne particulates also result in diminished radiation balance and impaired visibility (Chen et al., 2021; Jain et al., 2020; Wang et al., 2019).

Asian states, particularly developing nations in South Asia, are experiencing extreme particulate pollution from increasing suspended particulate levels. According to the latest survey, the South Asian nations such as, Bangladesh, India, and Pakistan have the worst air quality (Anjum et al., 2021). Some of the Pakistani cities, particularly Lahore has been reported as the world’s worst polluted city with very poor air quality index (Anjum et al., 2021). The mean concentration of PM2.5 in Lahore and Islamabad were reported as 115 µg/m3 and 47.3 µg/m3, respectively. During winter season, these urban areas are severely affected by elevated levels of PM2.5 which may originate from various regions, including the north-western countries (Afghanistan, Iran, and Turkmenistan) and southern countries (India, China, and Nepal). In addition, unplanned urbanization and growing industrial activity have created an unhealthy environment in Pakistan, and anthropogenic emissions are causing the air quality to deteriorate (Lv et al., 2019; Mehmood et al., 2018). Some earlier studies reported from Pakistan showed considerably higher levels of respirable particulates in the local atmosphere, and they also exhibited significant annual and seasonal variations (Lv et al., 2019; Mehmood et al., 2018; Rasheed et al., 2015). However, the comprehensive evaluation related to the characterization of the respirable particulates (PM2.5) for toxic trace elements and the discussion of their health risks and toxicity is inadequate. There is a dire need to characterise the prevailing levels of toxic elements and related health risks in the respirable particulate matter in the capital city, Islamabad. The present study aimed to understand the distribution of trace elements and their mutual relationships to estimate their anthropogenic contamination and enrichment in the urban airborne particulates. Association of the pollutant levels with the carcinogenic and non-carcinogenic risks were also evaluated in this study. The measured levels of trace elements in PM2.5 were compared with the reported levels from other regions. The findings of this study will help to identify the sources of trace elements, associated health risks, and their toxicity to both people and the environment, as well as it would help to control the pollution levels.

Experimental methodology

Site description and sample collection

Sampling was performed in the federal capital of Pakistan, Islamabad, which is located about 500 m above sea level (Latitude 33° 49' N; Longitude 72° 24' E). It covers an area of more than 900 km2 and has a population of about 2 million. It is flanked on three sides by the Margalla Hills to the north, the Punjab Plains to the east, and water bodies to the west. It lies 14 kms northeast of Rawalpindi (Mehmood et al., 2020). Islamabad has four distinct seasons throughout the year due to its subtropical climate: a hot and wet summer (June to August), a dry autumn (September to November), a cold and rainy winter (December to February), and a pleasant spring (March to May). The Kahuta industrial estate, the industrial sectors (I-9 and I-10), and vehicle exhaust are the main sources of air pollution in the city. The primary industries in the city are pottery, flour mills, paint, dyes, medicines, metallurgy, granite, and oil and ghee plants.

To perform detailed chemical analysis, fine atmospheric particulates (PM2.5) were collected on quartz fibre filter papers using a sequential air sampler (DPM5, AMS Analitica, Italy) which was installed on a rooftop about 30 feet from the ground and distant from other nearby buildings. The air sampler was equipped with an automatic flow rate controller, and samples were collected following the EU standard method (CEN, 2005). The sampling was performed in a typical urban area of Islamabad (Quaid-i-Azam University campus). The particulate samples were collected for 12–24 h. Before and after the sample collection, the mass of filter papers was determined gravimetrically using standard methodology (CEN, 2005).

Sample preparation

The particulate samples were further processed to prepare the solutions for trace elemental analysis following the USEPA methodology (Method IO-3.1, 1999). The acid extraction method was used to extract the elements on a hot plate. The filters containing particulates were folded in half along the length with the sample material facing inward and cut into 1" × 8" strips. The filter pieces were placed at the bottom of a labelled 100-mL bottle to make sure that acid should completely cover the filter paper. Then 50 mL of the extraction mixture (5.6% nitric acid and 16.8% hydrochloric acid, (v/v)) was added to the vessel and placed on the hot plate in a fume hood. The mixture was refluxed smoothly for 60 min. The flasks were then removed from the hot plate and allowed to cool. The digestate thus formed was filtered using Whatman filter paper (No. 42). Each solution was filtered in a volumetric flask (50 mL) and diluted up to the mark with a 0.1 N nitric acid solution. The blanks (which contained only a filter and reagents) was also prepared using the same method. The glassware was carefully cleaned by immersing it in a 5% HNO3 solution (v/v) for 24 h, then washed with distilled water and dried in an oven (70 °C) overnight. The filter papers were placed in a humidity-controlled desiccator (containing silica gel) for 24 h before and after the sample collection. The mass of the particulates was determined with an electronic balance and stored in an airtight Ziplock plastic bag. Each batch also included a reagent blank, which passed through the same process as the samples.

Quantification of the trace elements

In the present study, an atomic absorption spectrometer (Shimadzu AA-670, Japan) was employed for the quantification of selected trace elements (Fe, Ca, Co, Mg, Cu, Mn, Zn, K, Li, Sr, Cd, Ag, Cr, Ni, and Pb) in the atmospheric particulate samples (Method IO-3.2, 1999). Using the calibration line, which demonstrated a strong correlation coefficient (> 0.990), the trace elements in the samples were quantified under the optimum analytical conditions which are given in Table S1 (supplementary material). Using stock solutions (1000 mg/L), freshly prepared working standards were used for each element. To justify the validity of the quantitative results, standard reference material (NIST SRM-2711) was evaluated which showed excellent recovery as given in Table S2 (supplementary material). An interlaboratory comparison of the results was also performed, and it was found that there was less than a 2% divergence between them. It was also recorded that the blank contribution for each element was less than 5% of the measured values. Similar analytical procedures were reported by several authors (Jain et al., 2020; Ramírez et al., 2019; Rojas et al., 2019).

Statistical analysis

The quantified data were then subjected to statistical analysis comprising of minimum, maximum, mean, median, standard deviation (SD), standard error (SE), kurtosis, and skewness. The analysis revealed relative distribution, variations, and dispersion of the individual variables. The mutual relationships between the variables in the particulate matter were investigated using correlation coefficients. Furthermore, the data were subjected to multivariate statistical techniques, including cluster analysis and principal component analysis which defined the pollution profile of the contaminants in the particulates (Alves et al., 2023). Multivariate methods enhance the worth of the extensive data by consolidating it into smaller, easy, and relatively simple groups. Additionally, pollution assessment, ecological risk measurement, and health risk evaluation for trace elements in the atmospheric particulates were accomplished as described in results and discussion section.

Pollution assessment

The pollution index was assessed by evaluating the elemental levels in airborne particles by computing the enrichment factor (EF) and geoaccumulation index (Igeo).

Enrichment factor

The enrichment factor (EF) of the trace elements was computed to objectively evaluate how human activities affected the levels of the elements present in the particulate samples. It usually illustrates the extent of environmental contamination and the pollution level of the elements in the particulates. It is regarded as a valuable tool for separating components from human activities from those of natural sources (Alves et al., 2023). The following relationship is used to calculate it:

$$EF=\frac{{\left[X/{M}_{ref}\right]}_{sample}}{{\left[X/{M}_{ref}\right]}_{crust}}$$

X represents the mean level of the target element and Mref indicates the average level of the reference element in the analysed particulates/sample (ng/m3) and background soil/crust (mg/kg). Although various elements can be employed as reference elements; Fe was selected in this study.

Geoaccumulation index

Geoaccumulation index (Igeo) is a geochemical criterion for assessing particulates' environmental contamination and pollution levels and it was computed by following relationship:

\({I}_{geo={\mathrm{log}}_{2}\left(\frac{{C}_{n}}{1.5{B}_{n}}\right)}\)

"Cn" denotes the element's measured concentration in the particles, and "Bn" is the geochemical background value in the soil. Factor (1.5) is employed to minimise the anthropogenic influences and probability of pre-industrial levels varying due to lithogenic factors.

Ecological risk assessment

Significant enrichment of trace elements in the atmosphere can disturb the equilibrium of natural environment and thus may exhibit lethal effects on microbes, animals, plants, and people. The degree of particle-bound contamination of trace elements has been assessed using the ecological risk index (RI). This variable determines the levels of the trace elements and their toxic impacts. The following expression was used for the calculation of trace metals’ toxic responses:

$$RI=\sum_{i=1}^{m}{E}_{r}^{i}$$

‘Er’ represents the ecotoxicological threat factor of the elements, and ‘Tr’ represents the element's toxic reaction factor, which is linked with its ability for emission and the relative amounts in various mediums, including igneous rock, soil, freshwater, terrestrial plants, terrestrial animals, etc.

Human health risk assessment

Generally, the people are exposed to trace elements through direct oral consumption, dermal contact, and inhalation (Jayarathne et al., 2018; Zhang et al., 2018). The contribution of different materials varies widely through these routes; however, for inhalable atmospheric particulates, inhalation is considered as the primary exposure route (Alves et al., 2023; Zhang et al., 2018). Therefore, the health risk was computed for inhalation exposure in this study. Exposure concentration through inhalation (ECinh, µg/m3) can be calculated as per USEPA methodology (USEPA, 2016a):

$${EC}_{inh}=C\times \frac{\mathrm{ET}\times \mathrm{EF}\times \mathrm{ED}}{{\mathrm{AT}}_{\mathrm{n}}}$$

Here, C is the average concentration, ET, EF, and ED refer to the exposure time (days), exposure frequency (days/years), and exposure duration (years), respectively, while ATn is the average lifetime (years). The non-carcinogenic risk was assessed by computing the hazard quotient (HQ) applying the USEPA methodology (Behrooz et al., 2021; USEPA, 2016a):

$${HQ}_{inh}=\frac{{EC}_{inh}}{RfCi\times 1000}$$

‘RfCi’ is the inhalation reference concentration (µg/m3). A hazard quotient value less than one is generally considered safe or non-hazardous. The hazard index (HI) was calculated in terms of the arithmetic sum of all elements' HQs, to determine the total non-carcinogenic risks. If HI is less than unity, there is no noticeable potential risk to the people.

The lifetime cancer risk (CR), which illustrates the gradual probability that people may develop cancer throughout their lives because of exposure to toxic elements by inhalation was calculated to determine the cancer risk. It was estimated by the following relationship (USEPA, 2016a; Zhang et al., 2018):

$${CR}_{inh}={EC}_{inh}\times IUR$$

‘IUR’ refers to the inhalation unit risk. The CR of Pb, Ni, Cd, Cr, and Co (via inhalation) was evaluated in this investigation because the CSF values for these hazardous elements are only available through inhalation. It is an indicative parameter of the lifelong carcinogenic threat to the local people. The carcinogenic risk is generally perceived as significant if the CR value is greater than 1.0 × 10–6, and people may develop cancer because of the lifetime exposure to harmful substances.

Results and discussion

Appraisal of PM2.5

Basic statistical parameters related to the distribution of respirable particulates (PM2.5) and selected trace elements in the atmosphere of Islamabad are shown in Table 1. PM2.5 showed significant variations in the local atmosphere, ranging from a minimal of 4.737 µg/m3 to a highest of 108.1 µg/m3. The average and median levels of PM2.5 were found at 44.09 and 41.69 µg/m3, respectively. Appreciable dispersion was found in PM2.5, as shown by reasonably higher standard deviation and standard error values, while skewness and kurtosis indicated the moderately random distribution of the particulates. A substantial decrease in the concentrations of fine particulate matter (44.09 µg/m3) was noticed in this investigation because the present mean contents were relatively lower than majority of the earlier reported concentrations. For instance, Rasheed et al. (2015) stated that the annual mean contents of PM2.5 in the atmosphere of Islamabad were 79.0 and 66.1 µg/m3 in 2010 and 2011, respectively. Likewise, another study reported that in 2016 and 2017, the annual levels of PM2.5 were 70 and 44.5 µg/m3, respectively (Lv et al., 2019). Similarly, Mehmood et al. (2018) showed four separate sets of temporal data with respirable particulate matter levels of 69.97 (winter), 51.40 (fall), 44.54 (summer), and 40.45 g/m3 (spring) during 2017 for Islamabad. However, the current average PM2.5 levels (44.09 µg/m3) exceeded the recommended limits set by both WHO (15 µg/m3) and Pakistan (25 µg/m3). Elevated levels of PM2.5 in Islamabad may result from multiple factors, such as, an increase in vehicular traffic, outdated automobiles, reliance on filthy fuels (petrol and diesel), and unrestrained emissions sources. The higher concentration of PM2.5 can lead to severe health problems including difficulty breathing, skin allergies, cardiovascular issues, and even hospitalization. Additionally, poor air quality can result in loss of workdays, which can significantly impact the financial sector. The increased healthcare costs and reduced working efficiency due to poor air quality can severely affect the economy. Moreover, elevated concentrations of the atmospheric particles can cause a hazy appearance in the city that can obscure the visibility. Nonetheless, the mean concentration of PM2.5 demonstrated a gradual decline in preceding years, which indicated that the particulate concentration has noticeably decreased. It may be attributed to the improvement in the infrastructure and transport system by introducing the improved metro system.

Table 1 Statistical distribution parameters for PM2.5 (µg/m3) and concentrations of trace elements (ng/m3) in the atmospheric particulates of Islamabad
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Distribution of trace elements in PM2.5

The mean concentration of the selected trace elements (Table 1) revealed that Ca (1016 ng/m3), K (759.8 ng/m3), Mg (483.0 ng/m3), Fe (469.7 ng/m3), and Zn (341.1 ng/m3) showed relatively higher contributions, while Cu (40.99 ng/m3) exhibited relatively lower levels, followed by Pb (27.69 ng/m3), Mn (14.75 ng/m3), and Cd (8.696 ng/m3). The lowest average levels were observed for Li (1.237 ng/m3) and Ag (0.578 ng/m3) in the respirable particulates, as shown in Table 1. The variability in elemental concentration was examined in terms of range and standard deviation/standard error values, which revealed appreciable variations in their levels in the local atmosphere. Overall, most of the elements showed significant dispersion and random distribution, as indicated by large variations in their maximum and minimum levels and diverse mean and median levels. Significant lop-sidedness in the distribution was observed for Pb, Ag, Cu, Li, Co, and Ca in the respirable particulates, as demonstrated by higher kurtosis and skewness values.

Average concentrations of trace elements in the respirable particulates revealed following ascending trend: Ag < Li < Co < Cr < Ni < Sr < Cd < Mn < Pb < Cu < Zn < Fe < Mg < Ca < K. The quartile distribution of PM2.5 and atmospheric trace elements is shown in Fig. S1 (supplementary material). A widespread and sporadic distribution was noticed for particulate metals, including Co, Ag, Pb, Ni, Li, K, Zn, Cu, and Mg. In contrast, relatively narrow and symmetrical distribution was shown for Ca, Cr, Cd, Sr, and PM2.5. Overall, most of the elements exhibited random and significantly distorted distribution in the fine particulates, indicating considerable anthropogenic influence or impact on the level of trace elements in the local atmosphere.

Correlation of PM2.5 and trace elements

The correlation coefficients among the selected trace elements and PM2.5 are demonstrated in Table 2, where the statistically significant coefficient (r) values are indicated in bold. PM2.5 showed strong and significant direct relationships with Fe (r = 0.698), Mn (r = 0.642), Mg (r = 0.525), Sr (r = 0.514), Ni (r = 0.490), and Ca (r = 0.324). Among the trace elements, strong correlations (r > 0.700) were observed among Fe–Mn (r = 0.942), Fe–Mg (r = 0.842), Mg-Mn (r = 0.824), and Cr-Li (r = 0.716), followed by some strong associations between Sr-Mn (r = 0.656), Zn-Li (r = 0.647), Fe-Sr (r = 0.641), Zn-Cu (r = 0.624), Mn-Li (r = 0.599), and Mg-Sr (r = 0.593). Some statistically significant correlations were also observed among Cr-Mn, Cr-Cu, Mg-Cr, Zn-Cr, Fe–Cr, Mn-Ni, Pb–Zn, Ca-Mn, Cu-Li, Co-Ag, K-Ag, Mg-Li, Fe-Li, Ca-Sr, Ca-Mg, and Ca-Fe (Table 2).

Table 2 Correlation coefficient (r)* matrix for PM2.5 and selected elemental levels in the atmospheric particulates
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The correlation study, therefore, revealed mutual associations and probably similar source of Fe, Mn, Mg, Sr, and Ca in the atmospheric particulates. Similarly, Cr, Li, Zn, Cu, and Pb revealed strong or significant mutual associations in the fine particulates. The first set of elements was primarily derived from lithogenic sources, whereas the second set was largely considered anthropogenic. Conversely, a few pairs exhibited significant inverse relationships: Sr-Ag (r = -0.487), Ca-Zn (r = -0.437), and Ca-Cu (r = -0.384), thereby implying opposite variations in the atmospheric particulates. It was interesting to note that Cd exhibited statistically insignificant correlations with all other elements; it may be ascribed to its multiple source origin and diverse variations in the inhalable atmospheric particulates.

Pollution assessment

Evaluation of the anthropogenic influence on trace elemental burden in the atmospheric particulates was an essential part of the present investigation. It was evaluated by computing the enrichment factors and geoaccumulation indices, as shown in Fig. 1. The EF is usually utilized to establish trace elements' anthropogenic pollution level. It is mainly interpreted in the following categories: EF < 10 shows the crustal source of the metals, while EF > 10 proposes the primary anthropogenic source of the element. An EF of 10–100 implies that the metals are moderately enriched, while EF > 100 signifies anomalous enrichment of the elements in the particulates. As shown in Fig. 1a, among the selected trace elements, the highest EF in the atmospheric particulates was observed for Cd, followed by Ag, Zn, Cu, and Pb; all these elements were enormously enriched in the atmospheric particulates. Nonetheless, moderate enrichment was noted for Ni and Co in the local atmosphere, while the rest of the elements were mainly derived from lithogenic sources or crustal materials.

Fig. 1
figure 1

Comparison of average a enrichment factors and b geoaccumulation indices of selected elements in the atmospheric particulates

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The average geoaccumulation index (Igeo) of various trace elements is shown in Fig. 1b for comparative assessment. Generally, Igeo is taken as follows: Igeo ≤ 0 unpolluted, 0 < Igeo ≤ 1 shows unpolluted to moderately polluted, 1 < Igeo ≤ 2 shows moderately polluted, 2 < Igeo ≤ 3 shows moderately to deeply polluted, 3 < Igeo ≤ 4 shows severely polluted, 4 < Igeo ≤ 5 shows severely to extremely polluted, and Igeo ≥ 5 shows extremely contaminated. Accordingly, among the trace elements, Cd revealed extreme contamination, followed by Ag, which showed moderate to heavy contamination, while Zn and Pb exhibited moderate contamination in the atmospheric particulates. The Igeo values for the rest of the elements were < 0, indicating the uncontaminated status of these elements in the atmospheric particulates. Overall, the pollution assessment revealed considerable anthropogenic impacts of Cd, Ag, Pb, and Zn in the local atmosphere.

Source apportionment

Source apportionment and identification of the elements in the atmospheric particulates was another important part of this investigation. It was accomplished using principal component analysis (PCA) and cluster analysis (CA). PC loadings of PM2.5 and trace elements are shown in Table 3, and the corresponding CA is shown in Fig. S2 (supplementary material). Five significant PCs with eigen values exceeding unity were obtained by varimax normalized rotation on the dataset, explaining about 71% of the cumulative variance. PC 1 revealed dominant loadings for PM2.5, Mn, Fe, Mn, Mg, Ca, and Sr which were mainly derived from lithogenic sources or crustal materials. These variables also showed a strong mutual cluster in CA, thus supporting the PCA results. PC 2 showed elevated loadings for Cr, Cu, Li, and Zn that were predominantly influenced by industrial emissions, and cluster analysis is supporting these results. PC 3 and PC 4 showed higher loadings in favour of Co, Ag, and Ni, K, respectively. These elements were primarily emanated from incinerators and combustion processes. The last PC showed higher loadings in favour of Pb and Cd, along with some considerable input of Zn. These elements were largely released from transportation activities and vehicular emissions in the local atmosphere. The CA and PCA results were in excellent agreement with each other. Overall, the multivariate methods showed significant anthropogenic contributions of the trace elements (mainly emanating from industrial emissions, incineration/combustion processes, and automobile emissions) in the atmospheric particulates.

Table 3 Principal component analysis of PM2.5 and selected elemental levels in the atmospheric particulates
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Ecological risk assessment

The ecological risk caused by trace elements in the atmosphere was evaluated using an ecological risk index (RI) and environmental risk coefficients (Er). RI is a cumulative index, and generally, it is classified into four categories:

  1. (i)

    low ecological risk (RI < 150)

  2. (ii)

    moderate ecological risk (150 ≤ RI < 300)

  3. (iii)

    considerable ecological risk (300 ≤ RI < 600)

  4. (iv)

    very high ecological risk (RI ≥ 600)

Generally, for each metal, the possible ecological risk coefficient can be classified into five categories depending on the Er values: low risk when Er < 40, moderate risk when 40 ≤ Er < 80, significant risk when 80 ≤ Er < 160, high risk when 160 ≤ Er < 320, and extremely high risk when Er ≥ 320. The ecological risk coefficients of the trace elements in the present investigation were less than 40, indicating a low risk to the organisms and the ecosystem, as shown in Fig. 2. Overall, low ecological risk due to respirable was noted in this study.

Fig. 2
figure 2

Average values of ecological risk coefficients (Er) of trace elements in the atmospheric particulates

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Health risk assessment

Assessing the health risks associate with the trace elements in atmospheric particulates was the most significant aspect of the current investigation. The average exposure concentrations of trace elements through inhalation of the atmospheric particulates are shown in Fig. 3a. Relatively higher exposure amounts were observed for Ca, K, Mg, Fe, and Zn. The elevated exposure to these elements is not typically considered toxic because they are mostly essential without having detrimental health effects. Moderately higher exposure levels were noticed for Cu, Pb, Mn, Cd, Sr, Ni, Cr, and Co. The elevated and prolonged exposure to these elements may result in serious health effects, which would be further explored in succeeding section.

Fig. 3
figure 3figure 3

Average levels of a exposure concentrations (µg/m3), b hazard quotients and c carcinogenic risks of trace elements through inhalation of PM2.5

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The non-carcinogenic health risk associated with the prevailing elemental levels was assessed in terms of the hazard quotient/index. The average hazard quotients of the trace elements are shown in Fig. 3b. Typically, an HQ value of less than one is regarded as safe. The HQ values computed for all trace elements in the present study were less than 1.0, and the HI value (0.753) was also within the safe limit; consequently, no significant non-carcinogenic risks were associated with the elemental contact through inhalation or breathing of the atmospheric particulates. However, on a comparative basis, the highest HQ value was noted for Cd, followed by Co, Mn, Ni, Cr, and Pb, while it was almost negligible for Cu and Zn, as shown in Fig. 3b.

Additionally, the carcinogenic risk related to the inhalation of the particulate matter containing toxic trace metals was assessed, as shown in Fig. 3c. Generally, a calculated cancer risk less than 1 × 10–6 is considered as safe for the local population; a risk value of 1 × 10–4 is considered significant, and a calculated cancer risk higher than 1 × 10–4 is considered unacceptable. In the present investigation, the calculated CR for Pb and Ni were found to be within the safe limit (< 1 × 10–6); however, significant CR was found to be associated with Cr, Co, and Cd exposure through inhalation of the particulates (Fig. 3c). Among the toxic elements, the maximum CR was observed for Cr, followed by Co and Cd, while considerably lower values were noted for Pb and Ni. Overall, the present study revealed significant carcinogenic risks associated with the exposure to toxic metals by inhalation of the fine atmospheric particulates. Therefore, immediate attention should be focused on controlling or reducing the emission sources of toxic metals into the local atmosphere.

Comparison with the reported levels

A comparison of the current average levels of PM2.5 and trace elements was also made with the reported levels globally (Table 4). The mean PM2.5 concentration in this study (44.09 g/m3) was found to be significantly exceeding than the 24-h guideline values recommended by WHO (15 g/m3), USEPA (35 g/m3), and Pak-EPA (25 g/m3), as well as those of the annual average standards of WHO (5 g/m3), USEPA (12 g/m3), and Pak-EPA (15 g/m3) (Pak-EPA, 2010; USEPA, 2016b; WHO, 2021b). Similarly, the present PM2.5 levels were also higher than those reported from Karaj (Kermani et al., 2021), Colombo (Dhammapala et al., 2022), Batu Pahat, Johor Bahru (Alias et al., 2020), Southern California (Habre et al., 2021), and Las Angeles (Farahani et al., 2021). However, the current average contents of PM2.5 were relatively lower than those reported from other South Asian cities, including Lahore, Peshawar (Ahmad et al., 2021), Delhi (Jain et al., 2020; Sharma et al., 2016), Dhaka (Rahman et al., 2020), Chiang Rai, Bangkok (Kayee et al., 2020), and Ho Chi Minh City and Beijing (Guo et al., 2021; Phan et al., 2020).

Table 4 Comparison of the present average levels of PM2.5 (µg/m3) and trace elements (ng/m3) with the reported levels around the world
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The comparison of the average elemental levels in the particulates revealed that the present concentrations of most of the elements were considerably lower than the reported levels from Lahore, Peshawar, Isfahan (Soleimani et al., 2018), Delhi, Shandong (Zhang et al., 2018), and Ho Chi Minh City. Conversely, the present average contents of the airborne elements were significantly higher than the reported levels from Singapore (George et al., 2020), Chiang Rai, Bangkok, Batu Pahat, Johor Bahru, Southern California, and Las Angeles. The average contents of PM2.5, Cr, Mn, and Cd were somewhat comparable to the reported levels from Guiya (Zheng et al., 2014), whereas Pb was significantly lower than the reported levels. Similarly, compared with the reported concentrations from Shenzhen (Qin et al., 2020), the current levels of Ni, Co, Pb, Ca, and Cu were almost comparable, while the mean levels of Cr and Mn were less than the reported levels. Overall, the comparative study revealed that the present average levels of particulate elemental levels were less than those reported from Pakistan, India, Bangladesh, China, and Vietnam but considerably higher than those reported from the USA, Singapore, Malaysia, Iran, Sri Lanka, and Thailand (Table 4).

Conclusions

The present study revealed significant variations in the PM2.5 and trace elemental levels in the local atmosphere of Islamabad. Most of the elements showed large dispersion and random distribution in the atmospheric particulates. The correlation analysis exhibited several strong mutual relations among the trace elements. The enrichment factor and geoaccumulation index revealed significantly elevated contamination of Cd, Ag, Zn, Pb, and Cu in the particulates. Multivariate methods showed considerable anthropogenic emissions of the trace elements, mainly emanating from industrial emissions, automobile exhaust, combustion, and incineration processes. Health risk assessment demonstrated insignificant non-carcinogenic risks; however, considerable cancer risk was associated with the Cd, Cr, and Co levels in the atmospheric particulates. Finally, the average levels were compared with the guideline values and the reported levels from other regions around the world. It revealed that PM2.5 levels were above the guidelines set by the WHO, USEPA, and Pakistan EPA.