Characterization, Seasonal Variations, Source Apportionment and Health Risk Assessment of Heavy Metals in PM2.5 in the Urban Site of Agra, India

doi:10.21203/rs.3.rs-1765734/v1

The present study evaluated the concentration, seasonal variations, source identification and health risk through inhalation of heavy metals (V, Cr, Mn, Fe, Ni, Cu, Zn, As, Cd, Si and Pb) in fine particulate matter. Samples of PM_2.5 were collected from an urban site in Agra (27°10′N, 78°05′E), India during January 2018 to January 2019. A total of 48 samples of PM_2.5 were collected using Fine Particulate Sampler (Envirotech APM 550) set at a flow of 16.6 L min^− 1 on 47 mm quartz fiber filters (Pallflex, Tissuquartz) for 24 h. Heavy metals were determined by Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES) after acid digestion. The average PM_2.5 mass concentration was 107 ± 57.85 µg m^− 3 which has been compared with NAAQS and WHO limit was found to be higher. Seasonal variation of PM_2.5 showed: winter > post-monsoon > summer > monsoon. The average concentration of heavy metals lied within the range of 4.92 to 505.38 ng m^− 3. Among heavy metals, the high concentration belonged to Si (505.38 ng m^− 3) and Fe (281.23 ng m^− 3). The highest metal concentration occurred in the winter season followed by post-monsoon, summer and monsoon seasons. The enrichment factor was used to estimate the main sources of the metals in PM_2.5. Two factors were identified using Principal Component Analysis (PCA) at the urban site including resuspended road dust, vehicular activities, solid waste incineration, industrial emission and construction activities. To assess the health risks through inhalation exposure pathway hazard quotient (HQ), hazard index (HI) and carcinogenic risk (CR) were estimated. The carcinogenic risks of Pb (for both children and adults) Cd (for children) were lower than the precautionary criterion (1×10^− 6), while Cd for children and As, Cr and Ni for both children and adults were higher than 1×10^− 6. The non-carcinogenic risks of As, Cd, Cr, Mn and V for both children and adults were lower than the safe level of 1, whereas Ni for both children and adults were higher than the safe level of 1. The HI values of all heavy metals for both children and adults were higher than the safe level of 1 indicating a non-carcinogenic adverse health effect. The results of the present study impart information for the behaviour and risk mitigation of heavy metals.

Urban site

PM2.5

Heavy metals

Seasonal variations

source apportionment

Health risk assessment

Urban air pollution is a severe environmental problem that results from rapid economic development, industrialization, urbanisation and population growth in most parts of the world (She et al. 2021). Among the various atmospheric particulate matter (PM), fine particulate matter with an aerodynamic particle diameter of ≤ 2.5 µm (PM_2.5) is of more concern due to its adverse health effects. It has been observed that particulate matters with an aerodynamic diameter less than 10 µm have a higher effect on human health. Fine particulate matters have small diameters and large surface areas and may be capable of carrying several toxic substances through diffusion and damaging various parts of the body through air exchange in the lungs. In the present scenario there is no clean or healthy air to breathe for human beings (Rajput et al. 2022). Particulate matter and their constituents are the most important compounds present in the air which cause air pollution and adverse health effects for both humans and animals (Yousuf et al. 2022). These are produced by waste incineration, combustion of fossil fuels, emissions from vehicles and dust particles (Kermani et al. 2021). Meanwhile, metal compounds are major parts of particulate matter that can be entered into the human body through inhalation, ingestion and dermal contact and adversely affecting human health (Wang et al. 2022). Several studies have indicated that due to long-term exposure to particulate matter air pollution increasing rates of lung cancer and cardiopulmonary mortality are observed in human beings (Rauf et al. 2021). Moreover, air pollution and particulate matter pollution are classified by the international agency for research on Cancer (IARC) in group 1 of human carcinogens (Ku et al. 2021). It is reported that particulate matters (PM_2.5) have very high concentrations of several potentially toxic compounds like arsenic (As), chromium (Cr), cadmium (Cd), mercury (Hg), manganese (Mn), copper (Cu), iron (Fe), zinc (Zn), nickel (Ni) and lead (Pb) in the air (Li et al. 2022). These heavy metals enter into the human body through the respiratory system (Lin et al. 2022). Many studies have shown that the metals are not easily deteriorated biologically, therefore accumulating in the human body and impairing the physiological function. Therefore, recognising these heavy metals are necessary strategies to minimise air pollution and causing mortality (Zhou et al. 2022). Iron (Fe) is also present in the Earth’s crust so their concentration in the air is greater than that of other heavy metals (Yan et al. 2022). Heavy metals such as nickel (Ni), vanadium (V), chromium (Cr) and copper (Cu) are redox active metals due to their variable oxidation state which generate redox active oxygen species in biological tissues (Alias et al. 2020). For the moment, long-term human exposure to lead (Pb) may cause blood and cardiovascular diseases and adverse effects on the central nervous system (CNS) (Gao et al. 2018). Arsenic (As) causes nausea and vomiting, cardiac arrhythmia and damage to blood vessels due to exposure to PM_2.5-bound metals. Cadmium (Cd) causes severe damage to the lungs and stomach irritation. Chromium (Cr) can cause various types of diseases in human body such as irritations, respiratory problems and cancers in lungs, bladder, kidneys, larynx and bones. Lead (Pb) damages brain, kidneys and abortion in pregnant women. Mercury (Hg) damages growing foetuses, brain and kidney (Han et al. 2021). As, Cd and Cr(VI) may cause higher risk of non-cancer in children than adults in the environment (Wang et al. 2021). The concentration of heavy metals is different in the air depending on the industrial region, meteorological conditions and their surrounding sources of emission (Zhang et al. 2021).

It is essential to determine the contributions of various sources to reduce or manage particulate matter pollution. Source identification studies are important to analyse air pollution sources and measure their contributions. Multivariate receptor models are very famous tools for source identification methods. Such models have been applied to identify and quantify the sources of particulate matter in the atmosphere around the world (Manousakas et al. 2022). Principal component analysis/absolute principal component scores (PCA/APCS), edge analysis, chemical mass balance (CMB) and positive matrix factorization (PMF) are examples of receptor models. PMF is a powerful model capable of resolving PM sources without prior knowledge of the sources (Jain et al. 2021; Hassan et al. 2021). PMF has many advantages over other models (Jose et al. 2021; Yadav et al. 2022).

Therefore, The main objectives of this study were to evaluate (1) determining the concentration and seasonal variations of metals (Cd, As, V, Cd, Cr, Cu, Fe, Mn, Ni, Pb and Zn) in PM_2.5 (2) determining the source identification of metals in PM_2.5 (PCA analysis) (3) assessing the human health risks to children and adults through inhalation of PM_2.5. The extensive analysis based on the seasonal variation, source identification and health risk of metals will finally give scientific basis for the urban site of Agra to reduce atmospheric pollution.

Sampling Site

Agra (27°10′N, 78°05′E, 169 m.s.l.) is situated in the North-central part of India nearly 204 km south of Delhi. This city is famous for Taj Mahal, Fatehpur Sikri, Agra Fort, Itimad-ud-daulah's Tomb, Mehtab Bagh, Dolphin Water Park, Akbar's Tomb, Jama Masjid Agra, Chini ka Rauza, Anguri Bagh and Taj Museum. It is enclosed by the Thar Desert of Rajasthan. The estimated population of Agra in 2022 is 5,119,618 (https://www.indiacensus.net/district/agra). It is one of the most populated cities in Uttar Pradesh, India. It has three major National Highways (NH-2, NH-3 and NH-11) crossing the city. Meteorologically, the weather of Agra is divided into four seasons; summer (March-June), monsoon (July-September), post-monsoon (October and November) and winter (December-February) (IMD 1989). Summer season is related to strong hot, dry westerly winds and very high temperature ranges between 30 and 47 °C. The relative humidity in this season ranges between 18 and 50%. The monsoon season is associated with hot and humid, temperature ranges from 24 to 36 °C and the relative humidity ranges from 70 to 95%.The pre-monsoon and monsoon seasons are dominated by strong northeast and southeast winds and in the winter seasons, the temperature ranges from 10 to 25 °C during day time and drops below 5 °C at night. Dust storms and thunderstorms from Asian subcontinent and Thar Desert are frequently observed during the period from March to June. The wind speeds vary from 2.6 to 6.9 km h^-1 with maximum during summer and monsoons and minimum in winters (IMD 1989). Summer season is associated with strong, hot and dry westerly winds and high temperature ranging between 25 and 47 °C. Relative humidity (RH) in the summer ranges between 14 and 50%. The monsoon season is hot and humid; temperature ranges from 22 to 35°C and the relative humidity ranges from 70 to 95%. During post-monsoon, temperature ranges from 17 to 34 °C and relative humidity is around 50%. In winter temperature drops below 3 °C and relative humidity ranges from 23 to 90%. Millions of solid wastes are generated by the population of Agra every day that can be observed in the form of piles along roadsides and streets due to poor municipality services. There are about 127 industrial units in Agra that include several ferrous and non-ferrous industries at Rambagh, Foundry Nagar and Nunhai which are located nearly 5 km (East) away from the sampling site (NEERI, 2013). The other major industrial activities are rubber processing, metal plating units, diesel engine, tanneries, lime oxidation, pulverisation, engineering works and chemicals. It also has nearly 120 petha industries (Indian confectioneries) which consume wood and coal. As reported, the daily use by these units is nearly 500 kg and 4.7 tons of wood and coal (NEERI, 2013). Apart from these local sources, Mathura refinery, Firozabad glass industries and several brick kiln factories are also located about 40 km from Agra city (Kumar et al. 2007).

Samples were collected from the urban site. The urban site is ‘New Agra’ which lies near to the NH-2 and was selected to be the best in regard to the ambient particulate matter concentration (Fig. 1). It is one of the busiest places in Agra and has a very high traffic flow density of about 10⁵ vehicles per day in all four directions (Agarwal et al. 2017).

Sample collection

Samples of PM_2.5 (N = 48) were collected using Fine Particulate Sampler (Envirotech APM 550) operated at a constant flow of 16.6 L min^-1 on pre-weighed 47 mm quartz fiber filters (Pallflex, Tissuquartz) for 24 h. Each filter paper was desiccated for 24 h before and after the sampling. The mass concentration of particulate matter was evaluated from the difference in weight of filters before and after sampling. Prior to sampling, filter papers were pre-cleaned at 60 °C for 2 h to remove moisture. Sampling was carried out at a height of 12 m on the roof of the building from ground level for 24 h with a frequency of four in a week at the sampling site for twelve months from January 2018 to January 2019 to understand the seasonal variation and impact on atmospheric conditions.

Chemical analysis

One half of the exposed filter paper was cut into small pieces with a clean Teflon-coated scissors and kept in a conical flask and digested in 10 mL HNO₃ (65%, Merck Supra pure) for 1.5 h on a hot plate at the temperature of 40-60 ºC until 1-2 mL of solution remained. Then the conical flask was allowed to cool at room temperature and the walls of the flask were rinsed with distilled water and kept for further 30 min for diffusion of acid from the filters into the solution. After that the solution was filtered through a microporous membrane filter (0.22 μm, Sartorius 393) the solution was diluted up to 50 mL with distilled water and transferred to a PTFE bottle at 4 ᵒC until analysis. PTFE bottles were kept overnight in 2 % HNO₃ before use and then also left overnight in distilled water to prevent the adsorption of metals on the bottles. The metal (Cd, Cr, Cu, Fe, Mn, Si, Ni, As, Pb and Zn) concentrations were analysed using Inductively Coupled Plasma-Optical Emission Spectroscopy (ICP-OES; Thermo Scientific, ICAP 6300 Duo). The detection limits of metals were determined by using 3 × standard deviation (SD) of the blank concentration and listed in Table 1. The instrument was calibrated using Multielement Standard Solutions (Fluka Chemicals Sigma Aldrich). The correlation coefficients of the standard curves of metals were found to be higher than 0.99. The recoveries of metals were between 80% and 120%.

Data analysis

Enrichment factors (EFs)

To differentiate between anthropogenic and natural sources of metals in PM_2.5, an enrichment factor (EF) was calculated. Al, Zr, Fe, Sc and Ti are generally used as reference metals for evaluation of EF (Zhang et al. 2018; Luo et al. 2022). In this study, Fe is used as reference metal because it is ubiquitous in the environment. It has no significant anthropogenic sources and is assumed to be a conservative metal. It is used for the normalisation in the crust. EF values were calculated using the following equation:

EF = (C_n/C_ref)_sample/ (B_n/B_ref)_background

where, (C_n/C_ref)_sample and (B_n/B_ref)_background are the concentration ratios between the metal (n) and the reference metal (ref = Fe) in the PM_2.5 samples and in the background topsoil, respectively. In this study, EF < 10 showed the metal was minimally enriched and mainly arose from soil material, 10 < EF < 100 showed the metal was moderately enriched, and EF > 100 showed the metal was highly enriched.

Principal Component Analysis (PCA)

PCA is the most commonly helpful method for analysis of multivariate statistical data among different options and recommended by the U.S. Environmental Protection Agency (EPA). It can minimise the dimensionality of large datasets and extract the number of principal components required to elucidate all the variances of such datasets, which is much less than the original number of variables (Zhang et al. 2022). This tool determines the orthogonal distribution between the components. It ascertains data patterns and reveals them on the basis of their similarities and differences. The factors were decided by selecting principal components with eigenvalues greater than 1 during the analysis (Huang et al. 2022). The factor loadings are categorised as strong (> 0.70), moderate (0.50-0.70) and weak (< 0.50) (Yang et al. 2022). The estimation of sampling adequacy used was the Kaiser-Mayer-Olkin (KMO) followed by the execution of the PCA. It was done to acquire the determination of the suitability of PCA and also to examine the sufficiency of samples. This method adequately represented the sources of each heavy metal in PM_2.5 and guided the understanding and control of heavy metal pollution emissions in the present study and also assisted to improve the health of local populations.

Health risks assessment

Health risk assessments for metals were applied for carcinogenic and non-carcinogenic risks from inhalation exposure. Both carcinogenic and non-carcinogenic exposures were estimated based on the US EPA Risk Assessment Guidance for Superfund (US EPA 1989). The exposure concentration (EC) for inhalation was calculated according to following equation (US EPA 2018):

EC = (C × ET × EF × ED)/ATn

According to USEPA (2009), EC is the exposure concentration, and C is the metal concentration in ambient air (μg m^-3). ET is the exposure time (h day^-1), EF is the exposure frequency (days year^-1), ED is the duration of exposure (years), ATn is the average time (h exposure period^-1). The input parameters for carcinogenic and non-carcinogenic risks used here have been taken from various studies (Izhar et al. 2016; Li et al. 2019) and are shown in Table 2.

The non-carcinogenic risk of metals was calculated with the hazard quotient (HQ) or hazard index (HI, the sum of the total HQs) and evaluated by dividing EC by the inhalation unit risk (IUR). The carcinogenic risk (CR) and HQ were estimated using the inhalation reference concentration (RfC_i) and inhalation unit risk (IUR) (US EPA 2004). An HQ value higher than one implies that the non-cancer risk merits attention (US EPA 1989). CR is the possibility of an individual developing any type of cancer from lifetime exposure to carcinogenic risks. CR value is lower than 1×10^-6 (i.e. one additional case of cancer per million people), indicating acceptable risk (US EPA 1989).

The HQ and CR values posed by metals in PM_2.5 through inhalation exposure were calculated using the following equations:

HQ = EC/ (RfC_i × 1000 μg mg^-1)

CR = IUR × EC

The RfCi and IUR values for metals are shown in Table 3, were obtained from the regional screening level (RSL) summary tables provided by the US EPA (US EPA 2014). According to the US EPA, Cr(VI) and Cr(III) are classified as Group A (human carcinogens) and Group D (not classifiable for human carcinogenicity), respectively. In this study, the total concentration of Cr was calculated. The concentration ratio of Cr(VI) to Cr(III) in the atmosphere is reportedly about 1-6 (Li et al. 2015). In this study, the concentration of Cr (VI) was assumed to be 1/7 of the total concentration of Cr when calculating the Cr health risk (US EPA 1998; Li et al. 2016).

Mass concentration of PM_2.5 and its seasonal variation

The mean mass concentration of PM_2.5 throughout the year was 107 ± 57.85 µg m^-3 during the study period. This was 2.7 times higher than the annual mean of National Ambient Air Quality Standards (NAAQS 2009) for PM_2.5 is 40 µg m^-3 and 10.7 times higher than the WHO limit (10 μg m^-3) showing high loading of particulate matters at urban site which cause several millions premature deaths and result in the loss of millions of healthy life every year. High concentrations of PM_2.5 cause reduced lung function and growth, respiratory and asthma problems in children. Heart diseases, diabetes and neuro problems in adults are due to high exposure to these particulate matters. Earlier studies at other sites reported the mean concentrations at Delhi, Varanasi and Kolkata were 135 ± 64, 99 ± 33 and 116 ± 38 μg m^-3, respectively (Jain et al. 2021). Fig. 2 shows seasonal variation of PM_2.5. Seasonal variations of PM_2.5 were found the highest in winter (168.85 μg m^-3) followed by post-monsoon (133.92 μg m^-3), summer (88.85 μg m^-3) and monsoon (54.35 μg m^-3). In the winter season, high levels of PM_2.5 were due to the adverse meteorological conditions, and due to the increased strength of source emissions from coal combustion for resident heating. Contrarily, in summer and monsoon seasons the calm meteorological conditions and the reduction of coal combustion, mostly the decreasing residential usage led to the lower PM_2.5 concentrations (Liu et al. 2018; Zhang et al. 2015).

The air mass back trajectories were plotted in each season to analyse and trace the trans-boundary migration of particulate matter using the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model (Draxler and Rolph 2003). These seven days backward trajectories were estimated for daytime at height of 500 AGL in different seasons at the sampling site and is shown in Fig. 3. In the summer season, the trajectories arise from Pakistan, Punjab, Bihar and Saudi Arabia. In monsoon season, the trajectories originated from the Arabian Sea. In the monsoon season, the moisture loaded winds arising from Arabian Sea causes heavy rainfall, hence lowering the particulate matter concentrations significantly. In the winter short trajectories are observed indicating the major contribution from local sources, hence higher concentrations of PM_2.5. In the post monsoon season the air mass trajectory exhibits major contributions from the North West direction.

Concentration of heavy metals and their seasonal variations in PM_2.5

The annual metal concentrations in the PM_2.5 are shown in Fig. 4. Of the heavy metals As, Si, Cd, Fe, Cr, Cu, Mn, Ni, Pb, V and Zn; Si and Fe had the highest annual mean concentration and V had lowest. The comparison was done between the metal concentrations and the limits set by the NAAQS of India and the World Health Organization (WHO 2014). The NAAQS of India has established annual concentration limits for different heavy metals including 6 ng m^-3 for As, 20 ng m^-3 for Ni, and 500 ng m^-3 for Pb. The concentration of Pb was lower than the NAAQS annual limits, but the annual mean concentration of Ni and As exceeded the NAAQS limit. The concentrations of Pb in all PM_2.5 samples were above the WHO limit of 500 ng m^-3. Ni concentration was below the WHO limit of 25 ng m^-3. Cd concentration was below WHO limit of 5 ng m^-3. As concentration was below WHO limit (6.6 ng m^-3). The seasonal variations in the metal concentrations in PM_2.5 are shown in Fig. 5. The seasonal variation of metals was characterised by high levels in winter followed by post-monsoon, summer and monsoon. In the summer season Cd, Cu, Mn, Pb and Zn are released from tyres, brake lining and brake shoes into the environment due to high road temperature and increased friction between road and tyres (Banoo et al. 2022). Fe and Mn concentrations were found to be high in industrial and heavy traffic areas due to the use of Fe and Mn in steel industries, weathering of rocks, coal powered power plants and thermal power plants. Fe and Mn originated during steel production and cutting and wearing processes therefore steel alloy industries are the major sources of Fe and Mn. Other sources of Fe and Mn are wearing and tear of tyres and brake lining (Mandal et al. 2022). High concentration of Cd might be due to the heavy vehicular traffic density and industry sources such as phosphate fertilizer, chemical industries. Cd is used as stabilizers to alloys to provide protection from sunlight, chemical attack and heat degradation. Cd is released from fuel, coal combustion power plants, tyres, engine oil, galvanized structures (Duan et al. 2021). Concentration of Ni might be due to the wear and tear of brake lining, brake shoe, diesel fuel emission and Ni-Cd batteries. Cu is released from fuel and oil leakage, wearing of engine parts, metals works, brake linings pads and vehicle accessories (Moryani et al. 2020). Cr originates from coal combustion and heavy vehicular traffic density in the atmosphere. Pb and Zn concentrations might be due to the wear and tear of tyres, brake lining, leakage of lubricating oils and grease (Bisht et al. 2022). Though in India, Pb has been phased out of leaded fuel from the year 2000, but still it exists in road dust from previously vehicular exhaust emission due to its longer residence time in environment. Pb and its compounds are used as an anti-wear agent in lubricant oils for engines (Okorie et al. 2012). Moreover, thermal power plants and leaded paints from vehicles could also add Pb into the environment. Pb and Zn might be released from cement industries. Zn is emitted from high vehicle flow, motor oil and tyre residues. Zn and its compounds are used as antioxidants and lubricating oils (Gope et al. 2018). Brass, bronze, dry cell batteries, paints, rubber and ceramic are the major sources of Zn in the environment.

Enrichment factor

Fig. 6 showed the annual values of EFs of metals in PM_2.5. V, Mn, Fe and Si had EF values <10, and these metals can be considered as minimally enriched and to originate mainly from the natural sources such as wind-blown soil minerals. Cr, Ni, Cu, Zn, As, Cd and Pb had very high EF values (>100) and were considered highly enriched, indicating that their sources were mainly from anthropogenic emissions like steel smelting, coal combustion, waste incineration, contaminated soil and vehicle emissions (Bai et al. 2019; Feng et al. 2022; Sui et al. 2022).

Source apportionment of metals

To analyse the results, two principal components were extracted that demonstrated 71.65 and 23.92 % of the total variance, respectively. As shown in table 4, the first component includes Cr, Mn, Cu, Zn, As, Cd, Pb and Si showing that these metals may have a common source. Cr, Zn and Mn was likely to be derived from non-exhaust traffic, i.e. brakes and tire wear were the source (Wen et al. 2018). Ni is emitted from burning of lubricant oil (Liu et al. 2021; Chen et al. 2022). Pb is emitted from resuspended road dust. Pb, Cd and As originated from car fuels, exhaust gases, decorative materials, batteries, indoor smoking, the paint used for painting walls, erosion and corrosion of rubber of cars. The second component was associated with V, Mn and Fe which probably had a common source. It is attributed to vehicular emission along with resuspended road dust.

Health risk posed by toxic metals in PM_2.5

The health risk posed by inhalation was evaluated to determine the adverse effects of the exposure to airborne metals on public health. As shown in Fig 7, the HQ values for the inhalation of As, Cd, Cr, Mn and V were below the safe limit (1) for both children and adults. Ni has the highest HQ value for both children and adults. The hazard index (HI) for these metals was higher than the safe limit (1), indicating that accumulative non-carcinogenic risks are posed by both children and adults through inhalation exposure. The carcinogenic risks of As, Cr and Ni were higher than the precautionary criterion (1×10^-6) for both children and adults (Fig. 8), indicating that the carcinogenic risks from these metals after PM_2.5 exposure are not negligible. The carcinogenic risks of Cd for only adults exceeded the precautionary criterion (1×10^-6), indicating that the carcinogenic risks of Cd from PM_2.5 exposure are not negligible. However, the carcinogenic risks of Pb for both children and adults were less than the precautionary criterion (1×10^-6). It indicates that there is a small risk of non-carcinogenic adverse effects of heavy metals in PM_2.5 for both children and adults.

In this study, the health risks were evaluated by making some uncertainties of the models, exposure parameters, population characteristic parameters and the metal toxicity data. Meanwhile, few metals such as Fe, Cu, Zn and Si and other exposure routes like ingestion and dermal contact were not considered in this study. However, though there are some uncertainties, this assessment model is an effective tool for assessing the human health risk because of exposure to heavy metals in the urban environment. However, although there are few uncertainties, the model is a useful tool to assess the human health risk due to exposure to heavy metals in the urban environments (Wang et al. 2013; Zhai et al. 2014). It could impart the public and governments with reference information for the risk mitigation and management of heavy metals (Li et al. 2015; Feng et al. 2017).

Samples of PM_2.5 were collected during January 2018 to January 2019 from an urban site in Agra, India. The average mass concentration of PM_2.5 was 107 ± 57.85 µg m^− 3 which was above the limit recommended by the WHO and NAAQS. The highest concentration of PM_2.5 was found in winter followed by post-monsoon, summer and monsoon. Si and Fe had high mass concentration and the other metals had low concentration. The concentrations of Pb were lower than the NAAQS limits, but Ni and As concentrations exceeded the NAAQS limit. The concentrations of Cd, As and Ni were below WHO limit. Seasonal variations during the study period indicated that the air in Agra is most polluted with metals in winter followed by post-monsoon, summer and monsoon.

Enrichment factor analysis showed that Cr, Ni, Cu, Zn, As, Cd and Pb originate from anthropogenic sources while V, Mn, Fe and Si originate from natural sources. Furthermore, PCA indicated two major sources for the studied metals: coal combustion, motor vehicles, wind-blown dust, construction activities and industrial emissions.

For both children and adults, carcinogenic risks due to exposure to As, Cr and Ni were higher than the precautionary criterion ((1×10^− 6), whereas the carcinogenic risks of Cd was lower than 1×10^− 6 for children. For both children and adults, the carcinogenic risk for Pb was less than (1×10^− 6). The non-carcinogenic health risks of As, Cd, Cr, Mn, and V were lower than the safe limit (1) for both children and adults. The highest non-carcinogenic health risk was shown from Ni for both children and adults. The findings reported that the health risk due to exposure to PM_2.5 for the residents in this city is not negligible and the current situation suggested that a clean air action plan for controlling the emissions in this area would be of benefit.

Acknowledgments

This work was financially supported by UGC-BSR (No. ETP/UGC Res. Fellowship/3089). We are thankful to Dr. Anita Lakhani, Dayalbagh Educational Institute, Agra for his support in analysing samples by ICP-OES. We also wish to thank the Director and Head, Department of Chemistry, Dayalbagh Educational Institute, Agra, for providing us the essential facilities.

Conflict of interest statement

The authors declare no conflict of interest.

Agarwal A, Mangal A, Satsangi A, Lakhani A, Kumari KM (2017) Characterization, sources and health risk analysis of PM_2.5 bound metals during foggy and non-foggy days in sub-urban atmosphere of Agra. Atmos Res 197:121–131
Alias NF, Khan MF, Sairi NA, Zain SM, Suradi H, Rahim HA, Latif MT (2020) Characteristics, emission sources, and risk factors of heavy metals in PM_2.5 from Southern Malaysia. ACS Earth Space Chem 4(8):1309–1323
Bai L, He Z, Ni S, Chen W, Li N, Sun S (2019) Investigation of PM_2.5 absorbed with heavy metal elements, source apportionment and their health impacts in residential houses in the North-east region of China. Sustain Cities Soc 51:101690
Banoo R, Sharma SK, Vijayan N, Mandal TK(2022) Assessment of Potential Source and the Source Region of Particulate Matter in an Urban Area of Delhi, India.Aerosol Sci Eng1–15
Bisht L, Gupta V, Singh A, Gautam AS, Gautam S (2022) Heavy metal concentration and its distribution analysis in urban road dust: A case study from most populated city of Indian state of Uttarakhand. Spat Spatio temporal Epidemiol 40:100470
Chen R, Zhao Y, Tian Y, Feng X, Feng Y(2022) Sources and uncertainties of health risks for PM2.5-bound heavy metals based on synchronous online and offline filter-based measurements in a Chinese megacity. Environ Int 107236
Draxler RR, Rolph GD (2003) HYSPLIT Model access via NOAA ARL READY Website. NOAA Air Resources Laboratory, Silver Spring, MD. http://www. arl.noaa.gov/ready/hysplit4.html
Duan X, Yan Y, Li R, Deng M, Hu D, Peng L (2021) Seasonal variations, source apportionment, and health risk assessment of trace metals in PM_2.5 in the typical industrial city of changzhi, China. Atmos Pollut Res 12(1):365–374
Feng J, Yu H, Liu S, Su X, Li Y, Pan Y, Sun J (2017) PM_2.5 levels, chemical composition and health risk assessment in Xinxiang, a seriously air-polluted city in North China. Environ Geochem Health 39(5):1071–1083
Feng W, Zhang Y, Huang L, Li Y, Wang S, Zheng Y, Xu K (2022) Source apportionment of environmentally persistent free radicals (EPFRs) and heavy metals in size fractions of urban arterial road dust. Process Saf Environ Prot 157:352–361
Gao Y, Ji H (2018) Microscopic morphology and seasonal variation of health effect arising from heavy metals in PM_2.5 and PM₁₀: One-year measurement in a densely populated area of urban Beijing. Atmos Res 212:213–226
Gope M, Masto RE, George J, Balachandran S (2018) Tracing source, distribution and health risk of potentially harmful elements (PHEs) in street dust of Durgapur, India. Ecotoxicol Environ Saf 154:280–293
Han Y, Wang Z, Zhou J, Che H, Tian M, Wang H, Chen Y (2021) PM_2.5-bound heavy metals in Southwestern China: characterization, sources, and health risks. Atmos 12(7):929
Hassan H, Latif MT, Juneng L, Amil N, Khan MF, Fujii Y, Banerjee T (2021) Chemical characterization and sources identification of PM_2.5 in a tropical urban city during non-hazy conditions. Urban Clim 39:100953
Huang Y, Cheng X(2022) Characteristics and sources of atmospheric particulate matter and health risk in Southwest China.In Asian Atmos Pollut409–433
Indian Meteorological Department (IMD) (1989) Climate of Uttar Pradesh. Government of India Press, New Delhi
Izhar S, Goel A, Chakraborty A, Gupta T (2016) Annual trends in occurrence of submicron particles in ambient air and health risk posed by particle bound metals. Chemosphere 146:582–590
Jain S, Sharma SK, Srivastava MK, Chatterjee A, Vijayan N, Tripathy SS, Sharma C (2021) Chemical characterization, source apportionment and transport pathways of PM_2.5 and PM₁₀ over Indo Gangetic Plain of India. Urban Clim 36:100805
Jose J, Srimuruganandam B (2021) Source apportionment of urban road dust using four multivariate receptor models. Environ Earth Sci 80(19):1–16
Kermani M, Jonidi Jafari A, Gholami M, Arfaeinia H, Shahsavani A, Fanaei F (2021) Characterization, possible sources and health risk assessment of PM_2.5-bound Heavy Metals in the most industrial city of Iran. J Environ Health Sci Eng 19(1):151–163
Ku MS, Liu CY, Hsu CY, Chiu HM, Chen HH, Chan CC (2021) Association of Ambient Fine Particulate Matter (PM2.5) with Elevated Fecal Hemoglobin Concentration and Colorectal Carcinogenesis: A Population-Based Retrospective Cohort Study. Cancer Control 28:10732748211041232
Li H, Wang J, Wang QG, Qian X, Qian Y, Yang M, Wang C (2015) Chemical fractionation of arsenic and heavy metals in fine particle matter and its implications for risk assessment: a case study in Nanjing, China. Atmos Environ 103:339–346
Li K, Liang T, Wang L (2016) Risk assessment of atmospheric heavy metals exposure in Baotou, a typical industrial city in northern China. Environ Geochem Health 38(3):843–853
Li L, Meng R, Lei Y, Wu S, Jiang Y(2022) Human health risk assessment of heavy metals from PM_2.5 in China’s 29 provincial capital cities.Environ Sci Pollut Res1–13
Li N, Han W, Wei X, Shen M, Sun S (2019) Chemical characteristics and human health assessment of PM1 during the Chinese Spring Festival in Changchun, Northeast China. Atmos Pollut Res 10(6):1823–1831
Lin CH, Lai CH, Hsieh TH, Tsai CY(2022) Source apportionment and health effects of particle-bound metals in PM_2.5 near a precision metal machining factory.Air Qual Atmos Health1–13
Liu B, Wu J, Wang J, Shi L, Meng H, Dai Q, Hopke PK (2021) Chemical characteristics and sources of ambient PM_2.5 in a harbor area: quantification of health risks to workers from source-specific selected toxic elements. Environ Pollut 268:115926
Liu T, Marlier ME, DeFries RS, Westervelt DM, Xia KR, Fiore AM, Mickley LJ, Cusworth DH, Milly G (2018) Seasonal impact of regional outdoor biomass burning on air pollution in three Indian cities: Delhi, Bengaluru, and Pune. Atmos Environ 172:83–92
Luo H, Wang Q, Guan Q, Ma Y, Ni F, Yang E, Zhang J (2022) Heavy metal pollution levels, source apportionment and risk assessment in dust storms in key cities in Northwest China. J Hazard Mater 422:126878
Mandal S, Bhattacharya S, Paul S (2022) Assessing the level of contamination of metals in surface soils at thermal power area: Evidence from developing country (India). Environ Chem Ecotoxicol 4:37–49
Manousakas M, Furger M, Daellenbach K, Canonaco F, Chen G, Tobler A, Prevot ASH (2022) Source identification of the elemental fraction of particulate matter using size segregated, highly time-resolved data and an optimized source apportionment approach. X 100165, Atmos Environ
Moryani HT, Kong S, Du J, Bao J (2020) Health risk assessment of heavy metals accumulated on PM_2.5 fractioned road dust from two cities of Pakistan. Int J Environ Res Public Health 17(19):7124
National ambient air quality Standards (NAAQS) (2009) at website on 2-11-08. http://cpcb.nic.in/oldwebsite/Environmental%20Standards/default_Environment_standards.html
NEERI (National Environmental Engineering Research Institute) (2013) Comprehensive Environmental Management Plan (CEMP) for Taj Trapezium Zone. TTZ) area
Rajput JS, Trivedi MK (2022) Determination and assessment of elemental concentration in the atmospheric particulate matter: a comprehensive review. Environ Monit Assess 194(4):1–30
Rauf AU, Mallongi A, Lee K, Daud A, Hatta M, Al Madhoun W, Astuti RDP (2021) Potentially Toxic Element Levels in Atmospheric Particulates and Health Risk Estimation around Industrial Areas of Maros, Indonesia. Toxics 9(12):328
She Q, Cao S, Zhang S, Zhang J, Zhu H, Bao J, Liu Y (2021) The impacts of comprehensive urbanization on PM_2.5 concentrations in the Yangtze River Delta, China. Ecol Indic 132:108337
Sui S, Gao Y, Yuan T, He C, Peng C, Wang Y, Liu Z(2022) Pollution characteristics and health risk assessment of PM_2.5-bound arsenic: a 7-year observation in the urban area of Jinan, China.Environ Geochem Health1–12
US EPA. Risk assessment guidance for superfund. Volume 1: Human Health Evaluation Manual (Part A) (1989)
US EPA. Air toxics risk assessment reference library volumes 1–3 (2004)
US EPA. Risk assessment guidance for superfund volume I: Human Health Evaluation Manual (Part F, Supplemental Guidance for Inhalation Risk Assessment) (2009)
US EPA. Risk assessment guidance for superfund. In: Part A. (2011). Human Health Evaluation Manual; Part E, Supplemental Guidance for Dermal Risk Assessment; Part F, Supplemental Guidance for Inhalation Risk Assessment, vol. I. http://www.epa.gov/oswer/riskassessment/human_health_exposure.htm
US EPA (2014) Regional screening level tables. http://www.epa. gov/region9/superfund/prg/index.html. Last updated May 2014
US EPA (2018) Dose-response assessment for assessing health risks associated with exposure to hazardous air pollutants
Wang J, Hu Z, Chen Y, Chen Z, Xu S (2013) Contamination characteristics and possible sources of PM₁₀ and PM_2.5 in different functional areas of Shanghai, China. Atmos Environ 68:221–229
Wang S, Hu G, Yu R, Shen H, Yan Y (2021) Bioaccessibility and source-specific health risk of heavy metals in PM_2.5 in a coastal city in China. Environ Adv 4:100047
Wang W, Lin Y, Yang H, Ling W, Liu L, Zhang W, Jiang G (2022) Internal Exposure and Distribution of Airborne Fine Particles in the Human Body: Methodology, Current Understandings, and Research Needs. Environ Sci Technol
Wen J, Wang X, Zhang Y, Zhu H, Chen Q, Tian Y, Feng Y (2018) PM_2.5 source profiles and relative heavy metal risk of ship emissions: Source samples from diverse ships, engines, and navigation processes. Atmos Environ 191:55–63
WHO, World Health Organization (2014) Ambient (outdoor) air pollution in cities database 2014. Retrieved 31 May 2015
Yadav S, Tripathi SN, Rupakheti M(2022) Current status of source apportionment of ambient aerosols in India.Atmos Environ118987
Yan RH, Peng X, Lin W, He LY, Wei FH, Tang MX, Huang XF (2022) Trends and Challenges Regarding the Source-Specific Health Risk of PM_2.5-Bound Metals in a Chinese Megacity from 2014 to 2020. Environ Sci Technol
Yang X, Zheng M, Liu Y, Yan C, Liu J, Liu J, Cheng Y (2022) Exploring sources and health risks of metals in Beijing PM_2.5: Insights from long-term online measurements. Sci Total Environ 814:151954
Yousuf S, Donald AN, Hassan ABUM (2022) A review on particulate matter and heavy metal emissions; impacts on the environment, detection techniques and control strategies. MOJ Eco Environ Sci 7(1):1–5
Zhai Y, Liu X, Chen H, Xu B, Zhu L, Li C, Zeng G (2014) Source identification and potential ecological risk assessment of heavy metals in PM_2.5 from Changsha. Sci Total Environ 493:109–115
Zhang J, Wu L, Fang X, Li F, Yang Z, Wang T, Wei E (2018) Elemental composition and health risk assessment of PM₁₀ and PM_2.5 in the roadside microenvironment in Tianjin, China. Aerosol Air Qual Res 18(7):1817–1827
Zhang X, Eto Y, Aikawa M (2021) Risk assessment and management of PM_2.5-bound heavy metals in the urban area of Kitakyushu, Japan. Aerosol Air Qual Res 795:148748
Zhang X, Ji G, Peng X, Kong L, Zhao X, Ying R, Wang L(2022) Characteristics of the chemical composition and source apportionment of PM_2.5 for a one-year period in Wuhan, China.J Atmos Chem1–15
Zhang YL, Cao F (2015) Is it time to tackle PM_2.5 air pollutions in China from biomass-burning emissions? Environ Pollut 202:217–219
Zhou B, Wang J, Liu S, Ho SSH, Wu T, Zhang Y, Wang Q(2022) Extrapolation of anthropogenic disturbances on hazard elements in PM_2.5 in a typical heavy industrial city in northwest China.Environ Sci Pollut Res1–15

Table 1 The detection limit of metals

Metal	Detection limit (μg)
V	0.0018
Cr	0.0167
Mn	0.0093
Ni	0.0092
Cu	0.025
As	0.0142
Zn	0.1134
Cd	0.0004
Pb	0.0052
Si	1.21
Fe	0.94

Table 2 Input parameters for carcinogenic and non-carcinogenic risks

Parameter	Notation	Unit	Children	Adults	References
Exposure time	ET	h day^-1	8	8	Duan et al. 2021
Exposure frequency	EF	days year^-1	250	250	Li et al. 2015
Exposure duration	ED	year	6	24	US EPA 2011
Average time	ATn	hours	ED ×365 ×24 (for non-carcinogens) 70×365×24 (for carcinogens)	ED ×365 ×24 (for non-carcinogens) 70×365 ×24 (for carcinogens)	US EPA 2011

Table 3 RfCi and IUR values for carcinogenic and non-carcinogenic risks of inhalation exposure in PM_2.5.

Metal	IUR	RfCi
As	4.30E−03	1.50E−05
Cd	1.80E−03	1.00E−05
Cr(VI)	8.40E−02	1.00E−04
Mn	-	5.00E−05
Ni	2.40E−04	1.40E−05
V	-	1.00E−04
Pb	8.00E−05	-

Table 4 PCA analysis of heavy metals in PM_2.5 (n = 48).

Metals	Component
Metals	1	2
V	.473	.530
Cr	.801	.367
Mn	.518	.517
Fe	.171	.984
Ni	.231	.285
Cu	.554	.427
Zn	.537	.296
As	.610	.183
Cd	.728	.277
Pb	.766	.341
Si	.953	.303
Variance (%)	71.65	23.92
Cumulative (%)	71.65	95.58

Characterization, Seasonal Variations, Source Apportionment and Health Risk Assessment of Heavy Metals in PM_2.5 in the Urban Site of Agra, India

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Abstract

Figures

Introduction

Materials And Methods

Results And Discussion

Conclusions

Declarations

References

Tables

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