1 Introduction

To improve the quality of life, developing countries are engaged in rapidly increasing the rate of urbanization and industrialization without properly managing industrial emissions [1]. In India, after the liberalization of the economy in the early 1990s, there has been a rapid increase in the rate of industrialization, especially in urban centers such as the National Capital Territory of Delhi, Bengaluru, Kanpur, Mumbai, Agra, and Hyderabad [2, 3]. Expectedly, developmental activities have increased the concentration of pollutants such as particulate matter (PM), carbon monoxide, and sulfur dioxide in the atmosphere [4, 5]. This increase in particulate load in the atmosphere has led to numerous environmental and human health issues, not just in India but in entire South Asia.

In India, a number of studies have reported on the health risks associated with exposure to atmospheric PM1 (submicron particulates having aerodynamic diameter ≤ 1 µm), PM2.5 (fine particulates having aerodynamic diameter ≤ 2.5 µm), and PM10 (respirable particulates having aerodynamic diameter ≤ 10 µm) [6]. However, to fundamentally understand the adverse impact of atmospheric pollution on the human respiratory system, it is necessary to monitor the atmospheric PM in size fractions that can enter various parts of the respiratory system [7]. The human respiratory tract resembles an aerodynamic particle size classifying system that filters out particles as they progress deeper into the airway with air flow. Previously, Ny and Lee [8] used cascade impactors in Ulsan, South Korea, to measure atmospheric PM concentrations in the size fractions of PM<0.7 µm, PM0.7–2.1 µm, PM2.1–5.8 µm, and PM5.8–10 µm that have the potential to enter the alveolar, bronchial, tracheobronchial, and extrathoracic regions, respectively.

PM comprises chemical species such as trace elements, ions, and organic compounds [9]. Owing to various anthropogenic activities, large quantities of these chemical species are emitted continuously into the atmosphere in the form of PM. Elements such as chromium (Cr) and cadmium (Cd) are emitted by the electroplating industry [10]; vanadium (V), copper (Cu), arsenic (As), and Cd by the metallurgical industry [11]; and zinc (Zn) by residual oil combustion [12]. Further, Cu, Zn, barium (Ba), antimony (Sb), lead (Pb), manganese (Mn), molybdenum (Mo), elemental carbon, organic carbon, and nickel (Ni) are widely used as vehicular source markers [13]. These chemical species have the potential to adversely affect human health. Calculating the Excess Cancer Risk (ECR) is the best practice to quantify the human carcinogenic risk posed by ambient PM. In the Indian subcontinent, several researchers have estimated human carcinogenic risks through ECR calculation [14, 15]. However, owing to large spatio-temporal variations in the chemical composition of PM, more studies are needed for data-deficient areas. In addition to assessing human health risks, source apportionment studies will help in formulating effective mitigation strategies for ambient PM. The Himalayas, in the north of India, constitute a pristine environment, and several studies have reported the influence of atmospheric aerosols emitted from the anthropogenic activities from the neighboring polluted regions [16,17,18,19]. However, detailed studies on particulate pollution from the industrialized areas in the proximity of the Himalayas are scarce.

The objectives of the present study are to (1) determine variations in the PM mass concentration in different size fractions, (2) examine the source apportionment of atmospheric respirable suspended particulate matter (RSPM), and (3) estimate the ECR because of exposure to toxic heavy metals associated with RSPM. Bhagwanpur, an emerging industrial hub in Uttarakhand, was selected to conduct this study because (1) Uttarakhand has a fragile ecosystem—representative of that of the Himalayas, and (2) Bhagwanpur is mostly a rural area. The latter criterion is important because most of the previous studies on this subject have been limited to urban sites.

2 Materials and methods

2.1 Site description

Bhagwanpur (lat: 29° 58ʹ 12.35ʺ N, long: 77° 45ʹ 10.76ʺ E, and altitude: 250 m above sea level), a mostly rural site in the Uttarakhand state located in the northern part of India, was chosen for this study (Fig. 1). It is an industrial hub surrounded by agricultural fields and residential areas. The average annual temperature and the total rainfall are about 28.9 °C and 960 mm, respectively. According to the 2011 census, the population in the study location was 7573. Bhagwanpur is situated along the New Delhi–Dehradun national highway, 197.5 km north of New Delhi. Various industries such as cement, brick, battery, electroplating, rubber, recycling, pharmaceutical, food, and packaging industries are located near the study area. The vehicular traffic count per hour for two-wheelers, three-wheelers, light-duty vehicles, and heavy-duty vehicles was observed to be 170, 44, 337, and 67, respectively. The methodology used for measuring the vehicular traffic count is mentioned in supplementary text S1.

Fig. 1
figure 1

Study site location in Bhagwanpur, Uttarakhand state, India. The location where vehicular count was performed is also mentioned

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2.2 Sampling and meteorological details

An eight-stage non-viable Andersen Cascade Impactor (ACI) (Thermo Fischer Scientific, USA) was used for estimating the concentration of PM in the size range 0.4–10 µm (Fig. S2). The sampler had the following aerodynamic cutoff diameters: 10–9.0 µm (Stage 1) (S1), 9.0–5.8 µm (Stage 2) (S2), 5.8–4.7 µm (Stage 3) (S3), 4.7–3.3 µm (Stage 4) (S4), 3.3–2.1 µm (Stage 5) (S5), 2.1–1.1 µm (Stage 6) (S6), 1.1–0.7 µm (Stage 7) (S7), and 0.7–0.4 µm (Stage 8) (S8). The combinations (S6 + S7 + S8) and (S1 + S2 + S3 + S4 + S5) represent the fine- and coarse-size fractions, respectively, of the PM. The flow rate of the instrument was set at 28.3 L/min, and the sampling duration was 24 h, from 6:00 AM of first day to 6:00 AM of the next day, Indian Standard Time (IST).

A Respirable Dust Sampler (RDS) (model: SLE-RDS104NL, Spectro Lab, India) was used for estimating the RSPM concentration. Aerosol dust was collected using glass fiber filter paper (EPM 2000-Whatman, Size: 8″ × 10″), and the concentration was measured gravimetrically. The flow rate for the RDS was set at 1.1 m3/min, and the sampling duration was 24 h, from 6:00 AM of a day to 6:00 AM of the next day (IST). The RDS was placed on a building rooftop, 5 m above the ground level, while the ACI was placed 1.5 meters above the ground level to represent the breathing height of an average adult. The glass microfiber filter papers used for both the samplers were kept in a vacuum desiccator for 48 h before pre and post weighing. The filter papers were weighed in triplicate by using a microbalance (model: XS105DU, Mettler Toledo, USA) and were transported in an airtight box. The filter papers were handled using powder-free gloves and non-serrated forceps. For every ten samples, one field blank was taken to the sampling site and back to the laboratory. About 40 samples each from the RDS and ACI were collected during the observation period—from the winter season (October, November, and December) of 2014 to the summer season (March, April, and May) of 2015.

Due to non-availability of meteorological station at the monitoring location, we could not able to obtain the meteorological parameters such as wind speed, wind direction, temperature, and relative humidity. So, we collected the information of these parameters during the study period from the neighbouring (~ 10 km) station located in the National Institute of Hydrology, Roorkee (lat: 29° 52′ 07.3″ N, long: 77°53′39.3″ E, and altitude: 268 m ASL) (Fig. 2). Daily atmospheric boundary layer height was acquired from National Oceanic and Atmospheric Administration Real-Time Environmental Applications and Display System (NOAA-READY) archived Global Data Assimilation System (GDAS) meteorological data (0.5° × 0.5°) once in every week during the study period (ftp://arlftp.arlhq.noaa.gov/pub/archives/gdas0p5). The 3-h resolution (5:30 AM, 8:30 AM, 11:30 AM, 2:30 PM, 5:30 PM, 8:30 PM, 11:30 PM, and 2:30 AM) ARL format data was read using Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model-4.

Fig. 2
figure 2

Variation in meteorological parameters during the study period. Winter season represents October, November, and December months. Summer season represents March, April, and May months

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2.3 Elemental composition of RSPM

About 1/10th (51.61 cm2) portion of the exposed filter paper was subjected to chemical analysis for estimating the concentration of trace elements. The mass concentrations of 13 trace elements (Pb, Cd, Ni, Co, Mg, Al, Mn, K, Ca, Cr, Fe, Cu, and Zn) were determined through Inductively Coupled Plasma-Optical Emission Spectroscopy (ICP-OES) (Teledyne Leeman Labs, USA). The filter papers were digested using a mixture of HNO3 and HClO4 in the ratio 4:1 [20] in a microwave digester (MARS 6, CEM, USA), automated with a ramp time of 15 min, hold time of 20 min, temperature of 180 °C, and power of 1600 W. Further, the digested solution was filtered through a syringe filter paper. Final extracts were refrigerated at 4 °C in high-density polyethylene bottles until further analysis with ICP-OES. The values obtained from the blank filter paper were subtracted from the sample values. A similar protocol was followed in an earlier study conducted by the same research group [18, 21].

2.4 Enrichment factor

Several studies worldwide have shown the importance of using enrichment factor (EF) analysis as an effective tool in differentiating metallic pollution from natural or anthropogenic activity [22]. For calculating EF, the methodology proposed by Basha et al. [12] was adopted. The information on the concentration of trace elements in the earth’s crust was obtained from an earlier study [23]. Further, aluminum was used as the reference material for calculating the EF [24]. In general, elements with concentrations in the earth’s crust substantially higher than that of other elements are chosen as reference elements.

If EF ≤ 1, the element was considered to have been predominantly derived from the earth’s crust. If 1 < EF ≤ 10, the element was considered to have been enriched by anthropogenic activity [25]. If EF > 10, the element was considered to have been highly enriched by anthropogenic activity.

2.5 Excess cancer risk

Excess cancer risk (ECR) indicates the incremental probability of an individual developing cancer during their lifetime as a consequence of exposure to toxic heavy metals. ECR is calculated using the following equation:

$${\text{ECR}}\,\left( {\text{Inhalation}} \right) = \frac{{\left( {{\text{C}}\times {\text{ET }}\times {\text{EF }}\times {\text{ED }}\times {\text{IUR}}} \right)}}{{\left( {\text{AT}} \right)}}$$

where C represents the mass concentration of heavy metals (µg/m3); ET, exposure time (8 h/day); EF, exposure frequency (184 days/year); ED, exposure duration (24 years); and AT, average time for the carcinogen (AT = 24 h/day × 24 years × 184 days). The inhalation unit risk for heavy metals such as Cd, Cr, and Ni was obtained from the US-EPA database (http://www.epa.gov/iris/). Further, the inhalation unit risk (IUC) for Pb was obtained from the California Environmental Protection Agency (CalEPA) database. The IUC value for Pb is 12.0 × 10−6 (µg/m3)−1 [26]. Previously, other researchers have also employed this technique in their work [6, 27]. The acceptable value is 10−6 for a protection, and 10−4 is likely to be tolerable risk. In the present study, ECR was calculated for Pb, Ni, Cr, and Cd.

2.6 Air mass back trajectory analysis and fire data

To determine the effect of regional and/or long-range-transported PM on the local air quality of Bhagwanpur, three-day air mass back trajectories converging at 500 m above the ground level were constructed using the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model [28]. The height of 500 m was chosen to represent the state of air parcel movement nearby the average boundary layer height (561.3 m) found during the study period over Bhagwanpur. The air mass back trajectories were generated for the period November 2014 to January 2015 to represent winter season and March 2015 to May 2015 to represent summer season. National Center for Environmental Prediction (NCEP) reanalysis meteorological data (2.5° × 2.5°) were utilized for generating the air mass back trajectories (ftp://arlftp.arlhq.noaa.gov/pub/archives/reanalysis/). Trajectories were calculated at 12:00 (HH:MM) Universal Time Coordinate (UTC) to provide comprehensive information on the air parcel over the monitoring location.

Fire data around the study site were obtained from the moderate resolution imaging spectroradiometer (MODIS) (https://firms.modaps.eosdis.nasa.gov/). The MODIS derived fire data uses 1 km channel with wavelengths of 4 and 11 µm. The shape file of the fire data was obtained, and only those fires with a detection confidence greater than 80% were considered [29]. MODIS fire detection uses a contextual algorithm that exploits the emission of mid-infrared radiation from fires [30].

3 Results and discussion

3.1 Size-segregated PM mass concentration

The overall mean ± standard deviation of RSPM was observed to be 259.6 ± 52.6 µg/m3. The range and mean of RSPM during the winter season were observed to be 192.3–370.6 µg/m3 and 284.6 µg/m3, respectively. The respective values during the summer season were 167.2–313.7 µg/m3 and 234.5 µg/m3, respectively. The high PM mass concentration during the winter season can be attributed to meteorological conditions such as low boundary layer height (297.3 m) and low wind speed (0.52 km/h). On all sampling days, the 24-h RSPM mass concentration exceeded the permissible limit by Central Pollution Control Board (CPCB), 100 µg/m3 [31], and WHO, 50 µg/m3 [32]. Despite Bhagwanpur being a rural site, the 24-h RSPM mass concentration exceeded that of some highly polluted sites in India, such as Kakinada [11], Hisar [33], Kanpur [34], and Raipur [7], as well as that in a few major cities in Uttarakhand, namely Dehradun and Rishikesh [35].

The mass concentration range of PM0.4–10 during the winter and summer seasons was observed to be 162.9–217.4 and 156.0–190.7 µg/m3, respectively (Fig. 3). The Student’s t test indicated that the mean concentrations of PM9.0–10, PM2.1–3.3, PM1.1–2.1, and PM0.7–1.1 differed significantly between the winter and summer seasons (p < 0.05). In contrast, the mean concentrations of PM5.8–9.0 (p > 0.05, t = 0.81), PM4.7–5.8 (p > 0.05, t = − 1.3), PM3.3–4.7 (p > 0.05, t = 1.21), and PM0.4–0.7 (p > 0.05, t = − 1.63) were non-significant. Among the measured PM in various size fractions, PM2.1–10 µm was considered the coarse fraction, while PM0.4–2.1 µm was considered the fine fraction. The 24-h average mass concentrations of fine PM (PM0.4–2.1 µm) and coarse PM (PM2.1–10 µm) were observed to be statistically nonsignificant (p > 0.05, t = 1.57) between weekdays and weekends. This phenomenon can be attributed to the continuous operation of industries around the study site. As a contrasting example, a significant difference between the PM10 concentrations on weekdays and weekends was observed in Chennai, India, where vehicular emission was reported as the main source of PM contribution [36].

Fig. 3
figure 3

Variation in RSPM measured through RDS along with boundary layer height (BLH) and size-segregated PM measured through ACI during the measurement period

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During the winter and summer seasons, the ratio of fine and coarse fractions was 0.51 and 0.48, respectively. This implies that the PM concentration measured at the study site had major contributions from coarse particles. The literature reveals that the coarse particles in the atmosphere are mostly derived from soil/crustal re-suspension [37]. The roads adjacent to the study site were not paved with tar or concrete, thereby increasing the possibility of high dust re-suspension [38]. In both winter and summer seasons, the PM concentration was found to be highest for PM9.0–10 µm and lowest for PM2.1–3.3 µm. At Dehradun, the capital city of Uttarakhand located ~ 59 km from Bhagwanpur, the ratio of fine (PM0.4–2.1 µm) and coarse (PM2.1–10 µm) PM was observed to be 0.47 and 0.44 at a commercial and residential site. Moreover, the PM concentration was observed to be highest in the size range of PM9.0–10 µm and lowest in PM0.4–1.1 µm [39].

On comparing the results obtained from RDS and ACI, it was observed that RSPM concentration was 1.4 times higher than PM0.4–10 concentration. During winter and summer season, about 1.5 and 1.3 times, higher concentration was observed in RSPM compared to that of PM0.4–10, respectively.

3.2 Relationship between PM and meteorological parameters

The average temperature (temp), relative humidity (RH), wind speed (WS), and boundary layer height (BLH) were observed to be 21.5 °C, 74.7%, 1.0 km/h, and 561.3 m, respectively, during the measurement period. Pearson’s correlation was established between the size-segregated PM concentration and the meteorological parameters such as temp, WS, RH, and BLH. A negative correlation was observed for temp (r = − 0.32 for RSPM; r = − 0.22 for PM2.1–10; and r = − 0.22 for PM0.4–2.1), WS (r = − 0.34 for RSPM; r = − 0.08 for PM2.1–10; and r = − 0.36 for PM0.4–2.1), and BLH (r = − 0.31 for RSPM and r = − 0.37 for PM0.4–10). The average BLH during sampling days in winter season was 297.3 m, but expanded to 825.4 m in summer season. The increasing local air temperature enhances the turbulence/dispersion resulting in deepening of BLH. Hence, temperature and BLH were negatively correlated with the PM concentration.

In contrast, a positive correlation was observed for RH (r = 0.32 for RSPM; r = 0.20 for PM2.1–10; and r = 0.22 for PM0.4–2.1). High RH in the area can be attributed to the surrounding natural vegetation and river streams. High RH plays an important role in the secondary aerosol formation that can elevate PM mass concentration in the atmosphere [40, 41]. Increasing RH can also reduce the removal rate of airborne PM through the scavenging effect [42]. Therefore, positive correlation between PM concentration and RH could be mainly due to secondary aerosol formation. A detailed quantification of secondary aerosol formation is beyond the scope of this study.

3.3 Chemical species comprising RSPM

In this study, 13 trace elements associated with RSPM were quantified. The estimated trace elements constituted around 13.17% of the RSPM. The concentrations of crustal elements (Ca, Mg, K, and Al; average concentration: 6.87 µg/m3) were found to be higher than those of anthropogenic elements (Cd, Co, Ni, Pb, Cr, Zn, and Cu; average concentration: 0.62 µg/m3) (Table 1). The mean mass concentrations of the elements were in the order Ca > K > Mg > Al > Zn > Fe > Mn > Cr > Co > Ni > Cu > Pb > Cd. Among the trace elements analyzed, Ca had the highest concentration 3.9–19.5 µg/m3 during the winter season and 5.0–17.4 µg/m3 during the summer season, while Cd had the lowest concentration 0.02 × 10−1–0.01 µg/m3 during the winter season and 0.01 × 10−1–0.01 µg/m3 during the summer season. The average Ca concentration in this study was found to be significantly higher than other industrial centers reported in previous studies: 1.4 times higher than that in New Delhi [43] and 1.9 times higher than that in Argentina [44]. Similarly, Sharma et al. [45] reported higher Ca levels near a cement plant in Chhattisgarh, India. The average concentrations of metals such as Ni, Pb, Mn, Cu, and Zn found in this study were higher than those observed in a coastal industrial city in a previous study [2]. Further, the Cd concentration observed in this study was higher than that reported in Spain and Norway but less than that in New Delhi [46]. In addition, the average concentrations of all elements were higher in the winter season than in the summer season. Further, the 24-h average concentration of Pb (0.07 µg/m3) was less than the permissible limit by CPCB (1.0 µg/m3).

Table 1 Comparison of trace elements concentration (µg/m3) associated with RSPM in few locations across the world
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Pearson’s correlation between the elements revealed that several elements were highly correlated with each other (Fig. 4). Co had high correlation with Cr (0.80), Cu (0.88), and Ni (0.70). Similarly, Cu had high correlation with Cr (0.88). These observations indicate that these anthropogenic elements had similar sources. Further, the Kurtosis test conducted on the elements revealed mixed results: Some elements such as Cd, Pb, Mg, Al, Ca, and Zn showed negative values, whereas elements such as Co, Ni, K, Cr, Mn, Fe, and Cu showed positive values. Negative values denote a flat distribution of the data, while positive values denote sporadic high emissions [47, 48]. Figure S4 shows a box and whisker plot of the estimated trace elements.

Fig. 4
figure 4

Pearson’s correlation plot among the estimated trace elements

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3.4 Enrichment factor (EF) and principal component analysis (PCA)

The enrichment factor (EF) of elements such as Fe (0.6) and Al (1.0) was ≤ 1. Further, the EF of Ca (5.1), K (7.1), and Mg (4.4) was found to be < 10, denoting considerable anthropogenic activity. Elements such as Zn (766.4), Co (312.6), Cu (117.5), Pb (103.0), Cr (94.0), Ni (91.0), and Mg (16.0) showed high EF was chiefly attributable to various industrial processes, while Cd (10,094.1) showed highest EF [49, 58]. Similarly, a study conducted in Chennai revealed high EF for elements such as Cd, Ni, As, Zn, Pb, Ba, Cu, and V; the high level of EF was attributed to the industrial processes in the vicinity of the monitoring site [14].

In environmental studies, factor analysis is often used to simplify complex data sets for identifying the sources of pollution [50]. In this study, principal component analysis (PCA) was conducted using varimax-rotated factor analysis, and the minimum number of samples required for performing PCA was maintained. The factors having an eigenvalue of more than one were considered (Table 2). PCA revealed three principal components representing a cumulative variance of about 65.6%. The first component, responsible for about 28.2% of the variance, comprised Co, Ni, Cu, and Cr. These elements could have possibly originated from various industrial activities: Ni from electroplating industries; Cu from smelting activities and metallurgical industries; Cr from chromium plating operations [51]; Co and Cr from incinerator emission and coal combustion [25, 52]; and Ni, Co, and Cr from cement dust [59].

Table 2 Varimax-rotated factor loading matrix for the estimated trace elements during the study period
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The second component, responsible for about 20.7% of the variance, comprised Mg, K, and Ca, which belong to crustal elements [54], and may have been generated by soil re-suspension. As the study site was surrounded by agricultural land, some Ca could also have been generated by the re-suspension of agricultural soil because of wind activity [13]. Moreover, along the sampling site, there is frequent movement of heavy vehicles carrying fine-particle materials such as cement bags and packaging materials, and these could be one of the sources of the second component.

The third component, responsible for about 16.7% of the variance, comprised Cd, Al, Zn, and Pb, which could have originated from vehicular activity. Cd is emitted because of brake and tire wear. Pb can be a part of vehicular emission. Though leaded fuel was banned in India in the year 2000 [55], traces of Pb are still found in road dust, as observed by Hsu et al. [56] and Pandey et al. [57]. Zn and Al may have originated from tire and brake pad wear of vehicles [60].

3.5 Excess cancer risk (ECR)

Among the elements analyzed in this study, Cd, Cr, Ni, and Pb are known carcinogenic metals that can pose significant risks to human health. The carcinogenic group of these elements, as per the US Environmental Protection Agency’s Integrated Risk Information System (IRIS), is given in Table 3. These carcinogenic elements can enter the human bloodstream through the inhalation pathway [27]. The mass concentration of carcinogenic Cr used for computing ECRs was calculated using a method proposed in an earlier study [47].

Table 3 Excess cancer risk of carcinogenic elements associated with respirable suspended particulate matter (IUR: inhalation unit risk)
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The ECR of the carcinogenic trace elements was in the order Cr > Ni > Cd > Pb. Although the concentration of Cr(VI) was less than that of Pb and Cd, the ECR of Cr(VI) was highest because of higher IUR. The calculated ECR values are as follows: Cr: 2.72 × 10−4; Ni: 0.3 × 10−4; Cd: 0.04 × 10−4; and Pb: 0.28 × 10−6. ECR for Cr was 2.7 times higher than the tolerance limit (10−4), while elements such as Ni and Cd were 30 and 4 times higher than the safe limit (10−6), respectively. ECR for Pb was below the safe limit, chiefly because of its low IUR (12.0 × 10−6). The total ECR was calculated to be 3.06 × 10−4, which indicates that ECR was around 3 times higher than the tolerance limit. Earlier, the ECR values of these carcinogenic elements were reported to be lower than the tolerance risk and higher than the safe limit at Dehradun city [18].

3.6 Back trajectory analysis and fire data

The three-day air mass back trajectory analysis revealed that air masses mostly arrive through the North Western Indo-Gangetic Plains (NW-IGP), where huge quantities of crop residues are burned (Fig. 5). An earlier study reported that vast quantities of paddy (kharif crop) residues are burned in the NW-IGP region, resulting in emissions [61]. The number of fires (CI ≥ 80) observed during the winter and summer seasons was 2573 and 9373, respectively. Every year, during the summer season, a prolonged dry and hot period leads to frequent forest fires in Uttarakhand [62]. A substantial number of trajectories pass through the mountainous region of Uttarakhand and Himachal Pradesh, where air masses get loaded with emissions from the forest fires. Soni et al. [19] reported that emissions from the forest fire in the neighboring mountains influence the local particulate pollution at Dehradun city. Also, during the summer season, some of these trajectories pass through the semiarid regions of Western India.

Fig. 5
figure 5

Three-day air mass back trajectories converging over Bhagwanpur during winter and summer season are shown along with the respective MODIS fire spot

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This analysis reveals that, in addition to the PM originating from vehicular emissions, industrial emissions, and road-side soil re-suspension, emissions from biomass burning also contribute to the deterioration of air quality in Bhagwanpur.

4 Conclusions

The present study is aimed at determining the mass concentrations of RSPM and PM0.4–10.0 µm, divided into eight size fractions, at Bhagwanpur, an emerging industrial area located along the foothills of the Himalayas. The following results were found:

  1. 1.

    During all sampling days, the 24-h RSPM mass concentration was higher than the Indian national ambient air quality standard (100 µg/m3). Further, the mass concentration of coarse PM (PM2.1–10 µm) was greater than that of fine PM (PM0.4–2.1 µm). This observation was attributed to the unpaved roads neighbouring the study area.

  2. 2.

    Negative correlation was observed for PM with temp, WS, and BLH, while positive correlation was observed with PM and RH.

  3. 3.

    Among the analyzed trace elements, Ca had the highest average concentration and Cd the lowest. Further, the EF analysis revealed high enrichment of Cd and Pb, indicating substantial anthropogenic activity in the study area. PCA showed three components accounting for 65.6% of cumulative variance with contributions from industrial (28.2%), vehicular (20.7%), and crustal sources (16.7%).

  4. 4.

    Excess cancer risk for Cr was 2.7 times higher than the tolerance level (10−4), while only Pb was below the safe limit (10−6). The total ECR for Cr, Cd, Pb, and Ni was around 3 times higher than the tolerance level.

  5. 5.

    The 3-day air mass back trajectory analysis revealed significant influence of crop residue burning in NW-IGP and forest fire emissions in Uttarakhand and Himachal Pradesh.

Therefore, effective management strategies should be implemented to reduce the PM mass concentration at the study site. We believe this work on source apportionment and health impacts of atmospheric PM can play a key role in designing air pollution modeling and epidemiological studies.