Heavy metal analysis and health risk assessment of particulate matter (PM₂.₅/PM₁₀) and road dust in an urban region of greater Noida, India

2.1. Study area

Greater Noida ranks among the world’s most polluted urban areas, holding the 17^th position based on PM_2.5 content (∼88.6 µg∙m^-3). It is situated downwind of Delhi, Ghaziabad, and Noida, making it highly susceptible to air pollution. This rapidly expanding urban center plays a crucial role in various economic activities, covering an approximate area of 400 km². Greater Noida has a population of 102,054, of which 55,540 are males, while 46,514 are females, as per the report released by Census India 2011.

NH-24 roughly marks the northwestern boundary of this region, and the entire metropolitan area falls within Delhi’s National Capital Region (NCR). Greater Noida was developed to improve connectivity between Noida and the eastern industrial zones of Uttar Pradesh. Its urbanization was driven by increasing developmental pressures from Delhi and its neighboring regions. Compared to Ghaziabad in the north and Noida in the west, Greater Noida is positioned further north. NH-24, a crucial national highway, delineates the city’s eastern boundary, while the Hindon River forms its western boundary.

Greater Noida experiences a tropical savanna climate with three main seasons: summer, monsoon, and winter. During summer (March to June), temperatures range from 23°C to 45°C. The monsoon season, from mid-June to mid-September, brings an average rainfall of 93.2 cm (36.7 in). In winter, temperatures can drop to 3-4°C, and dense fog often reduces visibility in January.

Climatology and topography are the two major aspects that influence the occurrence of contamination, the extent of exposure, and the effects of pollution [20]. Meteorology influences the dispersion, residence time, chemical transformations, and transport of contaminants in the air. Similarly, climatological factors such as the speed and direction of the wind, temperature, and humidity influence the levels of heavy metal-containing PM.

2.2. Ambient particulate collection and analysis

PM monitoring was carried out at eight locations in Greater Noida (Figure 1). Details of all sampling sites with traffic characteristics have been summarized in Table 2. Airborne PM_2.5 and PM₁₀ samples were collected for 24 h with 46.2 mm PTFE filters and glass filters (Whatman GF/A 2000, 8” × 10”), respectively. The sampling technique involved the use of designated PM samplers: the Envirotech APM-550 MFC for PM_2.5 and the Envirotech APM-460 NL for PM₁₀. To ensure consistency, instruments were placed at a height of 2 m above the ground.

Close

Figure 1.

Map of the study area showing the sampling locations.

Export to PPT

Sampling rates were maintained at 16.67 L∙min^-1 for PM_2.5 and 1.1 m³∙min^-1 for PM₁₀. A total of 72 samples were collected for both PM_2.5 and PM₁₀ in the period starting November 2021 to February 2022. The study adhered to the standard operational procedures for sample collection, filter conditioning, and gravimetric analysis; Before and after the sampling, filters were conditioned under controlled environmental conditions of 25°C and 50% RH for the other before and after weight determination to reduce sample introduction errors. Initial and final filter weights were measured on the microbalance (Shimadzu AP225WD, Japan). Field and laboratory blanks were monthly collated to account for potential sources of analytical error. Filters were rolled into polyethylene bags and, after sampling, stored at 4°C, which maintains sample integrity.

During the sampling period, road dust samples were also obtained through a systematic procedure involving the careful sweeping of roads in specified locations using a dust-free plastic brush. Subsequently, the resulting road dust was collected using a dustpan. An area of 1 m² was swept to collect sufficient samples for chemical characterization. About 100-200 g of dust was collected from each sampling location with the help of a clean plastic brush and a dustpan and kept in sealed polythene bags. Then 24 samples were collected from eight site locations and followed collection procedure [20]. The dust samples were dried at 110°C for 2 h and sieved using a stainless-steel sieve (70 μm)to capture the minuscule particles. Prior studies have indicated that dust fragments with a diameter less than 70 µm have the potential to accumulate significant quantities of trace elements, thereby serving as a valuable means for conducting heavy metal analysis [21-23].

2.3. Chemical analysis

The collected filter substrates (47 mm for PM_2.5 and one-fourth portion of glass fiber for PM₁₀) were subjected to microwave acid digestion (Ethos One, Germany) for 1 h at 210°C. 8 mL of a mixture of tri acid that contains nitric acid, hydrochloric acid, and hydrofluoric acid (HNO₃+HCl+HF) in as of 1:3:3 (Suprapure, 70% GR grade, Merck) for the disintegration of particle-bound metals in PM_2.5 and PM₁₀. The digested samples were cooled at room temperature (20°C) diluted with ultrapure water MΩ), and filtered through a Whatman No. 42 filter paper, and the extracted samples were made up to 50 mL in vials for analytical analysis.

For road dust, the collected samples were sieved with 1 mm mesh to remove larger particles such as stones, leaves, and other fragments. The samples were dried at room temperature for one week. The samples were sieved to obtain fine particles using a stainless-steel sieve with a mesh size of 70 µm. A 0.5 g sample of road dust was initially dried in an oven, and tri-acid (HNO₃+HCl+HF) was added in a ratio of 1:3:3 in a Teflon tube for microwave digestion for 35 min at 190°C. The digested sample was filtered using Whatman No. 42 filter paper, and the extracted sample was made up volume of 20 mL using ultrapure water. The final samples were stored in vials (Tarson) at 4°C in refrigeration for chemical analysis.

The analysis of trace metals (As, Cr, Hg, Co, Ni and Zn) was analyzed using atomic absorption spectrometer (AAS; Agilent 240FS AA, United States) for PM_2.5, PM₁₀, and road dust samples. The Central Instrumentation Facility was used for present study samples at Sharda University in Greater Noida. Only analytical grade chemical and reagents procured from Merck; Germany were used in this study. The ultra-pure water (resistivity = 18.2 MΩ) was used for all dilutions and reagent preparations. The metal standards were prepared by successive dilution of a certified standard solution of 1000 mg∙L^-1 stock solution (Merck, Germany). Repetitive analysis of the reagent blank prepared from ultra-pure water was conducted to avoid error due to contamination. Additionally, known standards and in-house spiked samples were regularly analyzed to avoid instrumental errors and to check method recovery.

2.4. Quality control

To ensure the accuracy and reliability of the elemental analysis, several quality control measures were implemented during the study. The AAS (Agilent 240FS AA, USA) was calibrated using certified standard solutions (1000 mg∙L^-1, Merck, Germany), and the calibration curves for each metal showed correlation coefficients (R²) above 0.999. Reagent blanks, duplicate digestions (performed on 10% of the samples), and spiked recovery tests were performed regularly. The recoveries for the spiked samples ranged from 85% to 100%, confirming the satisfactory accuracy and precision of the method. A mixture of HNO₃, HCl, and HF was used for sample digestion to ensure complete dissolution of particle-bound metals, especially those embedded in silicate matrices. HF was used carefully in a closed microwave digestion system (Ethos One, Germany) at controlled temperature and pressure to minimize risks and losses from volatilization. Particular attention was paid to the analysis of volatile metals such as mercury (Hg), where digestion in sealed vessels and rapid analysis helped to reduce potential losses. The method detection limits (MDLs), calculated as three times the standard deviation of the reagent blanks, were as follows: As (0.003 mg∙kg^-1), Hg (0.001 mg∙kg^-1), Cr (0.002 mg∙kg^-1), Co (0.002 mg∙kg^-1), Ni (0.005 mg∙kg^-1), and Zn (0.01 mg∙kg^-1).

2.5. Principal component analysis

The key purpose of this investigation was to ascertain the potential sources of particle-bound metals of PM_2.5 and PM₁₀ within a specific sampling site. To examine the associations between various particle-bound metals, we conducted a Pearson correlation analysis. The coefficient of correlation was utilized to quantify the extent of the association among the road dust at different site locations.

Before performing the principal component analysis (PCA), the suitability of the dataset was assessed using the Kaiser-Meyer-Olkin (KMO) test and Bartlett’s test of sphericity. The KMO value was 0.742, indicating a moderate to good level of sampling adequacy for PCA. Furthermore, the Bartlett’s test of sphericity was statistically significant (P < 0.001), confirming that the correlation matrix was not an identity matrix and that the dataset was suitable for factor extraction. These results support the validity of applying PCA to identify potential sources of particle-related heavy metals in PM_2.5, PM₁₀, and road dust samples from the Greater Noida study area. PCA was then conducted using Varimax rotation with Kaiser normalization to facilitate the interpretation of component loadings.

2.6. Health risk assessment

The human health risk assessment technique of (United States Environmental Protection Agency) USEPA was employed to assess the subjection of trace elements originating from ambient PM and road dust through three exposure pathways, i.e., ingestion, inhalation, and dermal absorption. Multiple research studies have employed a set of equations (Eqs. 1-3) to ascertain the average daily dosage [24-31].

There is a continuous process of toxic metal accumulation through inhalation, ingestion, and skin absorption. The parameters for the equations mentioned above have been provided in Table 3 [32-34]. In the present investigation, an assessment of the levels of potentially hazardous elements present in the atmosphere was conducted, denoted as C (mg∙kg^-1), through the examination of PM and road dust accumulations in the ambient atmosphere.

To evaluate non-cancer hazards associated with heavy metals found in ambient PM and road dust, the value of hazard quotient (HQ) was employed to calculate the accumulated daily dose (ADD) resulting from different pathways. Further, after calculating the HQ (Eq. 4), the value of hazard index (HI) was calculated by the summation of the hazard quotients as defined in Eq. (5) to evaluate the non-carcinogenic risk of the individuals.

Further, to evaluate the possible health risk of trace elements in relation to oral cancer, the mean daily intake is multiplied by the corresponding cancer slope factor, as depicted in Eq. (6). The reference values and cancer slope factor that were used to evaluate the carcinogenic risk have been listed in Table 4 [33,39].

The calculation of the HI for a specific group of trace elements involves the summing of the values of hazard quotients associated with each individual element within the group. When a HI of a non-carcinogenic hazard exceeds one, it signifies an increased threat to human well-being. Conversely, an HI value of one or less does not signify a significant risk to human health, as supported by studies conducted by [35-37]. According to the evaluation methodology employed by the USEPA to assess potential risks to human well-being, carcinogenic risk levels ranging from ^10^-6 to ^10^-4 are measured as acceptable or permissible [38].

3.1. Heavy metals in road dust

The summary statistics of heavy metals have been shown in Table 5. The results indicated that As exhibited the highest content (2.21± 0.50 mg∙kg^-1) followed by Hg (0.82 ± 0.38 mg∙kg^-1), Cr (0.13 ± 0.01 mg∙kg^-1), Co (0.06 ± 0.02 mg∙kg^-1), Ni (0.03 ± 0.01 mg∙kg^-1), and Zn (0.01 ± 0.04 mg∙kg^-1), respectively.

The boxplot in Figure 2(a-f) shows the lower quartile, median quartile, and lower quartile of the content of various heavy metals along the different sampling sites in Greater Noida. Heavy metal contents of road dust show a distinct variability in different sites within Greater Noida. In this figure, box plots are presented for As, Cr, Co, Hg, Ni, and Zn, where ‘box’ is outlined by the 25^th and 75^th percentile values of metals, while ‘whisker’ indicates the 5^th and 95^th percentile values. The solid line within the box corresponds to the 50^th percentile or median concentration. The highest content of As was observed at S1, S2, and S4 sites due to residential, commercial communities, and heavy traffic volume near highways in Greater Noida.

Close

Figure 2.

Average concentration of heavy metal (a) Arsenic (b) Chromium (c) Cobalt (d) Mercury (e) Zinc (f) Nickel content in ambient particulate matter and road dust at different sampling sites within the Greater Noida.

Export to PPT

3.2. Correlation coefficient analysis

We assessed the correlation between metals from urban road dust in the Greater Noida region by conducting Pearson correlation coefficients. A positive correlation emerged between elements As and Cr which showed coefficient of 0.77 (Table 6). Similarly, the correlation matrix between the different studied heavy metals among the different sampling sites (Figure 3) shows a robust positive association was identified between As and Co (r = 0.666) and As and Hg (r = 0.680). Furthermore, positive correlations were noted between Cr and Co (r = 0.725), Cr and Hg (r = 0.536), and Co and Hg (r = 0.623). The positive correlation among trace elements As, Cr, Co, and Hg imply shared geochemical properties and origins, indicating common environmental hazardous emissions. These findings align with the understanding that factors such as substantial vehicle loads and friction from vehicle components on highways and adjacent communities significantly contribute to environmental pollution. The observed similarities between chromium (Cr) and arsenic (As) suggest a parallel geological origin. A similar observation was noted in [40] and suggested that Cr and As emissions can result from processes such as fuel combustion, metal corrosion, and road dust abrasion.

Close

Figure 3.

Correlation matrix between the studied heavy metals along the different sampling sites.

Export to PPT

3.3. Pattern analysis of heavy metal associations

PCA with varimax rotation and Kaiser normalization was applied to determine potential sources of road dust samples using SPSS software (IBM, version 20.0). This method is commonly utilized for dimensionality reduction, minimizing the number of random variables based on eigenvalues. Varimax rotation with Kaiser Normalization was specifically employed to maximize the explained variance.

In this study, PCA was conducted on 48 samples and six selected species. Factor loadings greater than 0.5 (N > 0.5) were considered significant for clearly identifying the contributing factors to road dust composition. Factors with eigenvalues exceeding 1, representing the total variance, have been detailed in Tables 7-9, while the rotated component matrix has been illustrated in Figures 4-6.

Close

Figure 4.

Rotated component matrix for PM₁₀.

Export to PPT

Close

Figure 5.

Rotated component matrix for PM_2.5.

Export to PPT

Close

Figure 6.

Rotated component matrix for road dust.

Export to PPT

PC1 is characterized by high loadings of chromium and cobalt, suggesting that these metals may co-occur due to similar environmental behavior or possibly shared sources such as vehicular non-exhaust emissions. The contents of chromium and mercury in automobile exhaust gases have experienced an upward trend. Cr is predominantly generated through engine abrasion due to its metallic nature as a component of the engine. When conducting an observation of mercury, it is possible to ascertain its source, which may be attributed to various factors such as the degradation of brake pads or the combustion of fossil fuels. The increase in the variance of PC2 data to 27.246% can be attributed to the substantial loadings of As and Hg. The presence of Hg was influenced by automobile emissions. The combustion of plants results in the generation of significant quantities of As. The observed variance of PC3 is determined to be 19.510%, which can be attributed to the high contents of Co and Zn. Zn is predominantly emitted through the degradation process of vulcanized rubber tyres, the use of petrol additives, and the corrosion of vehicles. On the other hand, cobalt emissions primarily result from road grit that becomes suspended in the air due to vehicle tyre activity.

The results of the PCA revealed distinct groups of heavy metals that suggest potential similarities in their environmental behaviour or origin. PC1, which showed high loadings for cobalt (Co) and chromium (Cr), may reflect emissions from transport sources such as engine wear, brake and clutch abrasion, and industrial activities, all of which are common along major roads in Greater Noida. PC2 was dominated by arsenic (As) and mercury (Hg), elements commonly associated with fuel combustion, waste incineration, and industrial emissions, activities known to occur at and around city intersections and commercial areas. PC3 showed a significant contribution of zinc (Zn), likely derived from tyre wear, lubricants, and vehicle corrosion. Although PCA does not establish a definitive source distribution, the observed statistical associations are in good agreement with the known anthropogenic activities in the study area. Thus, the PCA results, when interpreted together with local land-use and traffic characteristics, provide valuable information on the possible sources of metal pollution in road dust and airborne particles.

The PCA results suggest patterns of co-occurrence that may reflect similar anthropogenic influences, such as transport emissions or industrial activities. In this study, metals with high loadings on the same major component suggest common environmental behaviour or co-occurrence due to related anthropogenic activities, such as vehicle emissions, brake and tyre wear, or industrial input. Care was taken in interpreting the PCA results to avoid over-attribution of specific pollution sources in accordance with the statistical limitations of the method.

3.4. Health risk assessment

People with weakened immune systems face an increased risk of adverse effects due to trace metals in road dust. A comprehensive sampling campaign was conducted in Greater Noida to assess the heavy metals associated with airborne particles and road dust in the ambient air. The samples collected revealed the presence of heavy metals that can be inhaled, ingested, or absorbed through the skin. To assess the potential human health impacts, we used the methodology approved by the US Environmental Protection Agency [41], as there are no established regional standards. The objective of this study was to assess the potential human health hazards associated with exposure to these hazardous metals.

3.5. Non-carcinogenic risk

The analysis revealed that the HQ for all hazardous components in the pollutants indicated that oral and inhalational exposure posed the highest risk for both age groups, children and adults, while dermal exposure was found to be less hazardous. The study suggests that ingestion and dermal contact are the major routes of exposure for both infants and adults. Airborne PM and road dust samples were collected from eight different locations in Greater Noida. These samples were then used to estimate the non-carcinogenic risk through different routes of exposure. As per empirical evidence, the subsequent calculation represents the mean HI values ​​for individual weight components. Inhalation of PM_2.5 posed a greater health risk as compared to road PM or PM₁₀. Although adults showed higher non-carcinogenic risk scores for each severity component in equivalent functional domains, there was a marked difference in the mean hazard index scores between children and adults, as shown in Table 10 and Figure 7(a-c). Road dust levels were markedly higher in adults as compared to children, probably due to adults spending more time in the environment. Mercury showed a higher degree of toxicity among the toxic elements analyzed, even at its relatively low concentration. Inhalation of mercury vapor poses a significant health hazard due to its high rate of absorption in the lungs [42,43]. Mercury exhibits a high degree of impermeability due to its ability to cross biological barriers, including cellular membranes, neurological barriers, and cellular and tissue barriers. According to [44], oxidation of mercury in red blood cells and tissues by catalase and peroxidase results in the formation of inorganic mercury (Hg++) and mercury (Hg+). The need for increased caution and attention is evident for people living in areas with heavy traffic, especially children and adults, due to their long-term exposure to commercial and industrial pollution. This finding suggests that the distribution pattern of regional heavy element content remains constant among both adults and children. Water, food, and cigarettes have been identified as potential sources of arsenic exposure. Chronic arsenic poisoning is characterized by long-term exposure to inorganic arsenic, which is usually consumed through food and water. Based on a study conducted by [45] and published by the World Health Organization (WHO), it was found that the human body effectively absorbs arsenic from contaminated water. Such absorption of arsenic can cause adverse health effects that may vary depending on the metabolic state of the individual being studied.

Close

Figure 7.

Non-carcinogenic risk for adults and children in (a) PM₁₀, (b) PM_2.5, (c) Road dust.

Export to PPT

In addition to arsenic (As) and mercury (Hg), other heavy metals such as chromium (Cr), cobalt (Co), nickel (Ni), and zinc (Zn) also contributed to varying degrees to overall non-carcinogenic and carcinogenic health risks. Chromium (Cr), especially in its hexavalent form, is a known carcinogen and has shown significant contributions to cancer risk in both PM₂.₅ and PM₁₀ samples, particularly in heavily trafficked locations [46]. Cobalt (Co), although present at lower concentrations, showed elevated hazard quotient values ​​in several locations due to its potential to cause respiratory irritation and systemic toxicity upon chronic exposure. Nickel (Ni) is associated with allergic reactions and respiratory effects and makes a moderate contribution to carcinogenic risk, especially through inhalation [47]. Zinc (Zn), although an essential trace element, may lead to non-carcinogenic effects at higher exposures; in this study, its levels were generally low, and the associated risk remained well below acceptable thresholds. The co-occurrence of these metals in air particles and road dust highlights the cumulative health burden, especially for vulnerable groups such as children. These results highlight the importance of considering all pollutants in health risk assessments rather than focusing solely on the elements with the highest abundances.

3.6. Carcinogenic risk

This study evaluated the carcinogenic risks associated with trace elements, including chromium (Cr), nickel (Ni), cobalt (Co), and arsenic (As), based on their contents in PM₂.₅, PM₁₀, and road dust. As shown in Tables 10-13 and illustrated in Figures 8-10, these elements contributed to cancer risk values ranging from 3.61E–04 to 2.21E+02, with site-specific variation. Among the media assessed, PM₂.₅ exhibited the highest carcinogenic potential, reflecting its ability to penetrate deep into the respiratory tract. Although the total cancer risk associated with trace elements in road dust remained below the USEPA’s upper threshold of 1×10⁻⁴, the elevated contents of arsenic and chromium in PM raise concern due to their established toxicological profiles.

Close

Figure 8.

Carcinogenic risk percentage for PM₁₀ (a) Adults (b) Children.

Export to PPT

Close

Figure 9.

Carcinogenic risk percentage for PM_2.5 (a) Adults (b) Children.

Export to PPT

Close

Figure 10.

Carcinogenic risk percentage for road dust (a) Adults (b) Children.

Export to PPT

Arsenic (As), widely recognized for its carcinogenicity, was found in levels that could contribute to long-term health effects such as keratosis, leukomelanosis, and various skin cancers [48]. The average combined cancer risk value across all sites was 1.32E+02, with PM₂.₅ and road dust contributing 2.21E+02 and 3.61E–04, respectively.

Chromium (Cr), particularly in its hexavalent form, is widely used in industrial applications such as electroplating, battery production, and the manufacturing of plastics and fertilizers [49]. Its elevated presence in residential and commercial areas of Greater Noida could be attributed to emissions from these activities, including material use in institutional and household settings. This underscores the need to monitor Cr exposure in both industrial and non-industrial environments.

Given the multifactorial nature of urban pollution, it is important to consider not only individual metals but also other co-occurring carcinogens such as polycyclic aromatic hydrocarbons (PAHs), which may have synergistic effects on human health. Overall, the findings highlight the significance of integrated monitoring and source-specific mitigation strategies to address carcinogenic risks in rapidly urbanizing areas.