Seasonal characteristics of PM1, PM2.5, and PM10 over Varanasi during 2019–2020

Particulate matter (PM) concentrations and aerosol optical depth (AOD) are measured and correlated simultaneously using a high-volume sampler and a MICROTOPS-II Sunphotometer, respectively. The present work deals with the characteristics of particulate matter (PM₁, PM_2.5, and PM₁₀) over Varanasi, from April 2019 to March 2020. Daily variation, as well as seasonal variation, reveals the dominancy of fine-mode particles over the Varanasi region in the winter season and the dominancy of coarse-mode particles in the summer season, which was further confirmed by calculating the ratio between particulate matter (PM₁/PM₁₀ and PM_2.5/PM₁₀). This ratio was discovered to be lowest in the summer and highest in the winter. Annual mean concentrations of PM₁, PM_2.5, and PM₁₀ are found to be 93.91, 111.34, and 180.70 μgm⁻³, respectively. The seasonal variation shows relatively a higher concentration of PM₁, PM_2.5, and PM₁₀ in the winter season, which may be due to stable meteorological conditions and increased biomass burning in winter. Diurnal and seasonal variations in AOD were also studied during this period. A large and small value of AOD represents the dominancy of fine particles over coarse particles. At 500 nm, maximum (1.17) and minimum (0.44) AODs were measured in December and August of 2019, respectively. There was a statistically significant correlation between PM particles (PM₁, PM_2.5, and PM₁₀) and AOD. Elemental analysis shows that fluorine and carbon are the major elements that were observed in selected samples during the post-monsoon and winter season using SEM-EDX analysis.

Introduction

A primary atmospheric component of air pollution, i.e., particulate matter (PM), has become a major source of concern all around the world due to its negative impacts on air quality, human health, and the earth's ecosystem (Chowdhury and Dey, 2016; Ghude et al., 2016; Seinfeld et al., 2016; Singh et al., 2016a). Air pollution is one of the leading causes of sickness. Due to severely poor air quality, several cities in India have been named among the top 20 most polluted cities in the world during the previous few decades, with eleven of them being on the Indo-Gangetic Plain (IGP) (Garaga et al., 2018). Furthermore, it is the world's fourth leading cause of premature mortality, according to the reports (Cohen et al., 2017). PM₁₀, PM_2.5, and PM₁ refer to airborne particulate matter having aerodynamic diameters of ≤ 10 μm, ≤ 2.5 μm, and ≤ 1 μm, respectively, and can also be considered as coarse-mode, fine-mode, and accumulation-mode particles, respectively (Spandana et al., 2021). PM_2.5 and PM₁ are significantly more harmful than PM₁₀ due to their complexity and smaller size (Miri et al., 2016, 2017; Li et al., 2017). Industry, power plants, three-wheelers, and other combustion activities are all responsible for the production of PM, which comes from both primary emission and chemical alteration of precursor gases, which results in secondary particles (Rahman et al., 2020). In the recent years, urbanization, industrialization, and anthropogenic activities have led to an increase in PM concentrations in the surrounding atmosphere. Due to the proliferation of these types of activities, major cities throughout the world have faced severe air quality concerns, since the middle of the twentieth century (Cheng et al., 2013; Elbayoumi et al., 2013). Particles produced from both natural and anthropogenic sources make up PM in the atmosphere (Kaufman et al., 2005). PM can travel a long distance from its source if the atmospheric conditions are favorable (Ancelet et al., 2015; Tiwari et al., 2018, 2019). Increased quantities of particulate matter (PM) pose a regional and global environmental threat (Delfino et al., 2005; Obaidullah et al., 2012). Particulate matter (PM) plays a significant role in disrupting the Earth's radiative budget by absorption and scattering of incoming solar radiation and outgoing long wave radiation. They also play a role in cloud formation, through which they also have an impact on cloud lifespan and the precipitation process (Wang and Penner, 2009; Seinfeld et al., 2016; Tiwari et al., 2018). Depending on the dominant activity, such as light absorption or scattering, PM can operate as both a cooling and warming agent in the atmosphere (Myhre, 2009). The scattering and absorption coefficients of a particle are determined by its physical and chemical characteristics. As a result of this coefficient, air particulates generate regional smog, discoloration, texture loss, and invisibility in a specific area (Malm and Day, 2000). PM_2.5 and PM₁ have a harmful impact on human health because they can enter into alveoli through the respiratory system, causing lung illnesses, heart attacks, neurological disorders, and other health problems, while PM₁₀ affects the atmosphere's radiation balance as well as visibility (Salma et al., 2002; Pope et al., 2008). According to the research, high PM_2.5 exposures can also damage brain development in new born babies and children (Egondi et al., 2018). About 1.24 million deaths happened in India out of the 5 million deaths that happen globally because of air pollution, 54% of which are attributed to ambient particulate matter (Balakrishnan et al., 2019). Due to westerly/south-westerly winds in conjunction with dry weather conditions, IGP experiences a sudden and intense dust storm during the pre-monsoon season. These dust storms carry coarse-mode particles from Southeast Asia and the Thar Desert (Tiwari and Singh, 2013; Kumar et al., 2015a; Singh et al., 2016b; Tiwari et al., 2019). Due to the increasing aggregation of coarse-mode particles, air quality and visibility have deteriorated (Smith et al., 2019; Taneja et al., 2020). Paddy residue is widely burnt in Haryana and Punjab every year during the post-monsoon season, which is a major source of air pollution (Kumar et al., 2016; Singh et al., 2016a; Ojha et al., 2020). IGP has a higher particulate matter concentration due to the low height of the atmospheric boundary layer, low wind speeds, and increased amount of wood burning during the winter time, which reduces visibility (Massie et al., 2004; Rajesh and Ramachandran, 2017; Ali et al., 2019). In 2016, an analysis of haze episodes in Delhi using applied carbon tracers revealed that the burning of agricultural waste was the dominant source of pollution (Sawlani et al., 2019). Aerosol optical depth (AOD) is a measure that is used to assess PM concentrations and to describe air quality and atmospheric conditions, and the extinction of incoming solar radiation by air particles distributed in a vertical column of the atmosphere can also be evaluated by this quantity (Kompalli et al., 2010; Dey et al., 2012; Srivastava et al., 2014). The concentrations of PM₁₀ and PM_2.5 have a significant connection with AOD (Srivastava et al., 2012a; Xin et al., 2014). Apart from this, the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data are widely utilized to investigate atmospheric processes and climate change because they have good spatial and temporal coverage (1° × 1°) (Kaufman et al., 2002). Tiwari and Singh (2013) used MODIS Terra data for 12 months above Varanasi in 2011. MODIS data are accessible on the NASA Giovanni website free of cost. The research was conducted utilizing a standard MICROTOPS-II Sunphotometer during the year 2019–2020, and it included a comparison of the ground-based measurement of AOD to those obtained by satellite using level 3 MODIS data. To observe the morphology, structure, and chemical composition of PM, some collected samples have been characterized through scanning electron microscope-energy dispersive X-ray spectrometry (SEM-EDX). Over Varanasi, only a few studies have been reported on the chemical characteristics of particulate matter and the sources of its emissions (Murari et al., 2016; Tiwari et al., 2016; Pratap et al., 2020a). Prior research has found that the PM_2.5/PM₁₀ ratio has a substantial temporal variability and a fine correlation with climatic factors such as temperature, wind speed, and relative humidity (Akinlade et al., 2015; Speranza et al., 2016; Mukherjee and Agrawal, 2017). Although few researchers have evaluated the properties of pollutants over the IGP, they have been only limited to a specific proportion of particles (either PM₁₀ or PM_2.5) through which the evolution of anthropogenic activities could have been understood (Saxena et al., 2017; Jain et al., 2019). Thus, this study analyses the variation in the concentration of PM₁, PM_2.5, and PM₁₀ and its correlation with aerosol optical depth during pre-monsoon and post-monsoon season at BHU, Varanasi, situated in central Ganges Valley. Using an NOAA Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT), 5-day air mass back trajectories at three different heights such as 500, 1,000, and 1,500 m have been displayed for the selected day every month to identify the regional or transboundary origin of particles (Draxler and Rolph, 2003).

Experimental observations and methodology

Site description and meteorological condition

During the study period (April 2019–March 2020), particulate matter monitoring is carried out on the rooftop of the Department of Physics at Banaras Hindu University (BHU), Varanasi. Varanasi (25.26° N, 82.99° E, 83 m above mean sea level) is situated in the eastern part of Uttar Pradesh, which is a semi-urban city in the central Gangetic Plain and has various sources of contaminated pollutants and suffers from high particulate loading (Ram and Sarin, 2011; Tiwari and Singh, 2013; Kumar et al., 2015b; Tiwari et al., 2018). The Indo-Gangetic Plain (IGP) runs parallel to the Himalayan range, having a stretch of 2400 km from Jammu and Kashmir in the west to the Assam in the east, and has a significant aerosol hotspot of air contaminants due to its unique geomorphology, weather patterns, and climatic susceptibility (Dey et al., 2004; Ramachandran and Kedia, 2010; Srivastava et al., 2012b; Kumar et al., 2020; Pratap et al., 2020a). During the pre-monsoon season in which hot and dry summer exists, coarse-mode aerosol prevails, and extreme rainfall occurs during monsoon whereas an excess of fine-mode aerosols in extremely cold weather during winter and post-monsoon was reported (Satheesh et al., 2006; Tiwari et al., 2018).

Meteorological conditions over Varanasi during this study period from 01 April 2019 to 31 March 2020 are shown in Figure 1. Varanasi is extremely hot in the summer and humid in the monsoon while being outstandingly cold in the winter season. During the study period, temperature (°C), relative humidity (%), and wind speed (ms⁻¹) data are downloaded from the CPCB portal. The relative humidity, temperature, and wind speed were plotted jointly. The average relative humidity is found to be 61.58%, while the minimum and maximum relative humidities are 17.20 and 90% in May and September, respectively. The mean temperature is found to be 25.23°C while the minimum and maximum temperatures are 4.04 and 40.49°C in December and April, respectively. The average wind speed is found to be 1.34 ms⁻¹ while the minimum and maximum wind speeds are 0.30 and 5.64 ms⁻¹ in August, respectively.

FIGURE 1

Figure 1. Meteorological parameters (temperature, relative humidity, and wind speed) during the study period.

Instrumentation and methodology

PM sampler

Particulate matter samples were collected two times a week for 24 h using three Envirotech Pvt Ltd respirable dust samplers: Model no. APM 460 NL respirable dust samplers for collecting PM₁₀, APM 550 for PM_2.5 with a sampling rate of 16.7 lpm, and APM 577 with a sampling rate of 10 lpm for PM₁ dust samples for further analysis of their mass concentrations and elemental analysis during the study period (Bansal et al., 2019; Sah et al., 2019). Coarse-mode particulates (PM₁₀) are aggregated on a GF/A 8 × 10 in glass microfiber filter paper whereas fine particles (PM_2.5 and PM₁) are collected on a 2-μm PTFE microfiber filter paper of 46.2 mm diameter. The gravimetric method is used to calculate particulate matter concentration. For moisture removal, the filter papers were kept in desiccators for 24 h before and after sampling.

MODIS

The Moderate Resolution Imaging Spectroradiometer is used to monitor the ocean, land, and atmosphere. This instrument is onboard Terra and Aqua satellites, launched in December 1999 and May 2002 (Alam et al., 2014). MODIS provides daily global dust data in 36 spectral bands from visible to thermal infrared (0.41–14.38 μm) (Sharma et al., 2012). The MODIS global gridded level 3 aerosol products is derived from the level 2 MODIS product with 10 km of spatial resolution (Remer et al., 2005). The MODIS equipment does a full scan of the earth's surface once every 1 to 2 days. At the local solar time, 10:30 in the morning and 01:30 in the afternoon, respectively, the Terra and Aqua satellites pass over the Indian Territory. The daily AOD satellite data from MODIS on-board Terra satellite level 3 AOD (mean_MOD08_D3_6_1_Deep_Blue_Aerosol_Optical_Depth_550_Land_Mean) for 1° × 1° grid from April 2019 to March 2020 over Varanasi were collected at 550 nm wavelength.

MICROTOPS-II

Ground-based AOD observations were made about three times a week during clear sky conditions from 10:00 to 15:00 h using a portable multi-band MICROTOPS-II (MT-II) Sunphotometer (Solar Light Company, USA). It is a five-channel handheld sunphotometer that gives instantaneous columnar AODs at five wavelengths: 380, 440, 500, 675, and 870 nm. The MT-II Sunphotometer has a 2.5° field of view. The MT includes temperature and pressure sensors associated with GPS connectivity for obtaining position and time coordinates (Morys et al., 2001). This instrument is based on the principle of measuring the intensity of incoming solar radiation at specific wavelengths and then converting it to optical depth using the Langley method. In this study, a considerable correlation of spectral and seasonal AOD with the particulate matter was observed.

SEM-EDX

A computer-controlled SEM coupled with EDX was used to characterize the morphology and elemental analysis of airborne particles. This instrument is made by Carl Zeiss Microscopy in Germany. The main filter paper was randomly sliced into a 1-mm² sample for the investigation of elemental composition and surface morphology (Pratap et al., 2020a). To achieve more favorable secondary images and to make them electrically conductive, the samples were sputtered with gold. The EDX spectrum of airborne particulate was recorded at 15–30 KV (Murari et al., 2016). For qualitatively spectra, elements were measured at different points.

Results and discussion

Variation in mass concentration of particulate matters

Using a high-volume sampler, the mass concentration of particulate matter was measured over Varanasi from April 2019 to March 2020. In Figure 2, the daily variation in mass concentrations of PM₁, PM_2.5, and PM₁₀ is shown. Mean mass concentration from April 2019 to March 2020 was found to be 89.9±44.4 for PM₁, 106.5±67.2 for PM_2.5, and 180.8±71.4 μg/m³ for PM₁₀, respectively. Most of the time mass concentrations of particulate matter were found to be exceeded the NAAQS limit (40 μg/m³ for PM_2.5 and 60 μg/m³ for PM₁₀). A similar result was reported by Kumar et al. (2020) in Varanasi. Murari et al. (2017) reported that annual mass concentrations of PM₁₀ and PM_2.5 were found to be 161.3 and 81.8 μg/m³, respectively, in 2014 over Varanasi. Jain et al. (2021) found the average concentration of PM₁₀ and PM_2.5 in Varanasi was 257.90 and 99.33 μg/m³, respectively from January 2015 to December 2016. Singh et al. (2021) recorded mean mass concentrations of daily PM₁₀ and PM_2.5 of 239 ± 128 and 123 ± 89 μg/m³, respectively, during the period from July 2014 to June 2018 over Varanasi. The seasonal behavior of PM₁, PM_2.5, and PM₁₀ reported in the previous studies in IGP is also shown in Table 1. Mass concentrations of particulate matter depend on different seasons which are shown in Figure 3. As per data available, we have considered October as a post-monsoon season, November–February as the winter season, March as a transition period named as the spring season, and April–June took as the summer season (Prasad and Singh, 2009; Kumar et al., 2021). Mass concentration of PM₁ for post-monsoon, winter and spring was found to be, 104.6±43.2, and 57.5±31.8 μg/m³, respectively. Mass concentration of PM₁ for the summer season was not available. Mass concentration of PM_2.5 during post-monsoon, winter, spring, and summer was found to be 79.0±19.6, 156.1±59.3, 82.0±31.3, and 43.5±19.7 μg/m³ respectively. Additionally, the mass concentration of PM₁₀ during post-monsoon, winter, spring, and summer was found to be 142.5±47.9, 224.0±70.8, 127.6±30.0, and 139.6±40.2 μg/m³, respectively. It can be observed that the higher concentration of PM₁, PM_2.5, and PM₁₀ was found to be in the winter season. Winter is a season of stable meteorological conditions, calm wind, cold weather, and increased biomass burning as well as increased vehicular emissions (Guttikunda and Calori, 2013; Banerjee et al., 2015). Most importantly, the atmospheric boundary layer shifted downward and particulate matter gets trapped in the lower atmosphere (Li et al., 2020).

FIGURE 2

Figure 2. Daily variation in mass concentration of particulate matters (PM₁, PM_2.5, and PM₁₀) from April 2019 to March 2020 over Varanasi.

TABLE 1

Table 1. Previous study of PM₁, PM_2.5, and PM₁₀ samples at different locations in IGP.

FIGURE 3

Figure 3. Seasonal variation in mass concentration of particulate matter (PM₁, PM_2.5, and PM₁₀) from April 2019 to March 2020 over Varanasi.

The ratio of mass concentration of particulate matters

To identify the dominant contribution of mass concentration in different months and seasons, the ratio of PM₁ and PM_2.5 to PM₁₀ was calculated. The monthly variation in the ratio of particulate matter is shown in Figure 4, and the seasonal variation in the ratio of particulate matter is shown in Figure 5. From Figure 4, the ratio of PM_2.5/PM₁₀ was found to be < 0.4 in April, May, and June 2019, which suggests the dominancy of coarse-mode aerosol particles. On the other hand, this ratio was found to be >0.5 in the other months and was highest in November and December (>0.7), which suggests an increase in fine-mode aerosol particles. Similar results for the ratio PM₁/PM₁₀ were found (this ratio is not shown in April, May, and June 2019 due to data unavailability). Concentrations of particulate matter depend on the different seasons (Pratap et al., 2020a,b), which are shown in Figure 5. The ratio of PM_2.5/PM₁₀ was found to be lower (0.4) in the summer season, which suggests the dominancy of coarse-mode particles. It may be due to the dust transportation from mid-arid regions and the Thar Desert over the Indo-Gangetic Basin (Kumar et al., 2015b; Murari et al., 2015; Tiwari et al., 2019). On the other hand, ratios of PM₁/PM₁₀ and PM_2.5/PM₁₀ were found to be higher in the winter and spring seasons, which suggests the enhancement of fine-mode particles. Punjab and Haryana states are considered the largest regions of crop residue burning, which leads to fine aerosol loading during post-monsoon as well as during winter seasons. These aerosol particles have transport properties and hence get transported long distances, which may cover the whole Indo-Gangetic Plain (Kaskaoutis et al., 2014). The ratio of PM_2.5/PM₁₀ and PM₁/PM₁₀ was found to be relatively higher in the post-monsoon as compared to the summer season. It may be due to the washout of coarser particles from the atmosphere while smaller particles remain suspended in the atmosphere. It is also reported that crop residue burning is intense over the Indo-Gangetic Plain during the post-monsoon season, causing an increase in fine-mode particles (Sarkar et al., 2018).

FIGURE 4

Figure 4. Daily variation in the ratio of particulate matters (PM₁, PM_2.5 with PM₁₀) from April 2019 to March 2020 over Varanasi.

FIGURE 5

Figure 5. Seasonal variation in the ratio of particulate matters (PM₁, PM_2.5 with PM₁₀) from April 2019 to March 2020 over Varanasi.

Variation of AOD(τ)

During the sampling period from April 2019 to March 2020, the monthly mean spectral fluctuation of AOD at five distinct wavelengths (380, 440, 500, 675, and 870 nm) derived from the ground-based MICROTOPS II Sunphotometer over Varanasi is shown in Figure 6. The spectral fluctuation of AOD demonstrates rather plainly that the AOD was found to be substantially greater at shorter wavelengths, which can be due to the presence of fine particles, while the AOD was observed to be comparatively lower at longer wavelengths, which can be attributed to the presence of coarse particles (Reddy et al., 2011). A seasonal variation in AOD at five distinct wavelengths was also measured by the MICROTOPS-II Sunphotometer as shown in Figure 7. AOD at all five distinct wavelengths is found to be maximum during the post-monsoon season followed by winter, while it is minimum in the monsoon except for the summer season. The reason for the low value of AOD during the monsoon season could be the washout of aerosol particles from the atmosphere during rainfall (Dey and Di Girolamo, 2010; Lodhi et al., 2013; Tiwari and Singh, 2013), and crop residue burning during the winter season could be the reason for higher AOD (Kaskaoutis et al., 2014; Sarkar et al., 2018). In the summer season, AOD has an increasing trend and found maximum at 870 nm. Generally, dust storms are formed in the summer, which can be the reason for the presence of coarse particles. The daily average of AOD over Varanasi was found to be ranged from 0.2 to 2.0. AODs from MODIS and MICROTOPS-II are measured at 550 and 500 nm, respectively (Tiwari and Singh, 2013). Using the Angstrom power law (equation 1), the MICROTOPS-II AOD at 500 nm was calculated at a wavelength of 550 nm to validate the MODIS and MICROTOPS-II Sunphotometer. Angstrom's power law is given as Ångström (1964).

$\begin{array}{ll}\text{AO}{\text{D}}_{550\text{nm}}=\text{AO}{\text{D}}_{500\text{nm}}\times {(550/500)}^{-\alpha }& (1)\end{array}$

where α is angstrom exponent (AE). For two different wavelengths (λ₁ and λ₂), AE is given by

$\begin{array}{ll}\alpha =-ln({\tau }_{\lambda 1}/{\tau }_{\lambda \text{2}})/ln({\lambda }_2/{\lambda }_1)& (2)\end{array}$

Angstrom exponent (wavelength range 380–870 nm) is a measure of the aerosol particle size and a fraction of fine to coarse-mode aerosols (Tiwari et al., 2018). AOD loading is observed to have large seasonal fluctuations, which are obtained higher in the winter season (November–February) and lower during the monsoon season (July–September). Over Varanasi, we found that satellite and ground-measured AOD data have an excellent one-to-one correlation, as shown in Figure 8. It has been observed that the total correlation coefficient between MODIS and MICROTOPS-II AOD data is 0.59. Later, diurnal variation in MODIS and MICROTOPS-II Sunphotometer AOD data at 550 nm is observed during this study period, as shown in Figure 9.

FIGURE 6

Figure 6. Spectral variation in AOD at different wavelengths 380, 440, 500, 675, and 870 nm over Varanasi during the study period (April 2019–March 2020).

FIGURE 7

Figure 7. Seasonal variation in AOD over Varanasi using MICROTOPS-II Sunphotometer during April 2019–March 2020.

FIGURE 8

Figure 8. Validation of level 3 MODIS AOD over Varanasi using MICROTOPS-II AOD, at 550 nm, during the study period (April 2019–March 2020).

FIGURE 9

Figure 9. Comparison of MODIS AOD at 550 nm with the MICROTOPS-II at time of pass of Terra satellite over Varanasi during the study period (April 2019–March 2020).

Correlation between MICROTOPS-II AOD (500 nm) and mass concentration of particulate matters

Figure 10 depicts the relationship between MICROTOPS—II AOD (500 nm) and particulate matter (PM₁₀, PM_2.5, and PM₁). AOD data were chosen for the day when PM data were available. Sometimes, AOD was not available for the day on which PM data were available. So, one-to-one corresponding data are only correlated. A total of 37 samples of PM₁₀ and PM_2.5 have been chosen, whereas 21 samples of PM₁ have been chosen for this correlation. PM₁₀ has apositive correlation with R² = 0.42, PM_2.5 has a positive correlation with R² = 0.44, and PM₁ has a positive correlation with R² = 0.43, respectively. Concentrations of particulate matter have a significant effect on AOD since an increment in the concentration of PM leads to an enhancement of AOD, which suggests a positive relationship. Similarly, during the monsoon season, when particles are washed out of the atmosphere, the PM concentrations decreased, and hence, the AOD also decreased.

FIGURE 10

Figure 10. Correlation between MICROTOPS-II AOD (500 nm) and particulate matters (PM₁, PM_2.5, and PM₁₀).

Elemental composition of particulate matter

The elemental analysis of PM is also a significant factor that impacts the scattering nature of particulates. The various constituents' weight percentages of PM_2.5 samples were measured throughout the investigation, which is summarized in Table 2. We have studied the chemical concentration of five PM_2.5 samples. The order of these compositions is F > O > C > N > Al > K > S > Mg > Si > Cl > Na, C > F > O > S > K > Si > Cl > Al > Mg >Na, F > C > O > N > Cl > K > S > Mg > Si > Na >Al, F > C > O > N > Cl > Si > Na > K > Al > S, and F > C > O > N > Si > Cl > K > Mg > S > Na >Al in October 2019, November 2019, December 2019, January 2020, and February 2020, respectively, as shown in Figure 11. These samples are found to be rich in F, O, C, and N. The concentration of fluorine is higher in all months except November. The use of firecrackers during Diwali may contribute to carbon emissions. Carbon is the second highest contributor to PM_2.5 samples during the winter season due to crop residual burning and anthropogenic activities. The lesser contributions of Mg, Si, Na, S, and Al have been found during the study period due to other anthropogenic activities (Liu et al., 2019).

TABLE 2

Table 2. Weight % (±standard deviation) of the elements quantified in the PM_2.5 sample during the study period.

FIGURE 11

Figure 11. Percentage (%) elemental composition particulate samples for PM_2.5on (A) 28 October 2019, (B) 15 November 2019, (C) 31 December 2019, (D) 12 January 2020, (E) 11 February 2020.

Source identification of particulate matter and their transportation

The National Oceanic and Atmospheric Administration (NOAA) Hybrid Single Particle Lagrangian Integrated Trajectories (HYSPLIT) model, which uses NCEP reanalysis wind data as input, computes 5 days of air mass back trajectories at three different altitudes of 500, 1,000, and 1,500 m above ground level at the receptor site (Draxler and Rolph, 2003). Back trajectories are crucial for identifying source regions, transport channels, and aerosol characteristics. About 5-day air mass back trajectory for nine selected days during the study period on 30 April 2019, 30 May 2019, 09 June 2019, 28 October 2019, 15 November 2019, 31 December 2019, 12 January 2020, 11 February 2020, and 10 March 2020 is shown in Figure 12. On the 30th of April 2019, air masses appeared to be coming from Saudi Arabia and Pakistan at high altitudes (1,500 and 1,000 m) and from Rajasthan at lower altitudes. On 30 May 2019, air masses seemed to come from high-altitude regions such as Russia, Kazakhstan, and Turkey. On 9 June 2019, an air mass at a high altitude comes from Georgia and the area around Kazakhstan. Before it gets to the sampling site at a low altitude, it goes through the Bay of Bengal. On the 28th of October 2019, the high-altitude wind comes from the Caspian Sea and Pakistan, while Uttar Pradesh is the source region for low-level wind. On 15 November 2019, all three-level winds (1,500, 1,000, and 500 m) came from Pakistan. On 31 December 2019, high-altitude wind at 1,500 m comes from Kazakhstan, 1,000 m wind comes from China, and low-level wind comes from Madhya Pradesh. On the 12th of January 2020, air masses appeared to be coming from Iraq and Saudi Arabia at high altitudes (1,500 and 1,000 m) and from Uzbekistan at lower altitudes. On 11 February 2020, the high-altitude wind seems to come from Nepal; the wind at 1,000 m comes from Rajasthan, and the low-level wind seems to come from Kazakhstan. On 10 March 2020, a low-altitude air mass arrives from Georgia, while high-altitude wind arrives from the Arabian Gulf and Syria.

FIGURE 12

Figure 12. About 5 days back trajectories derived from HYSPLIT model for Varanasi for nine typical days having large aerosol loading on (a) 30 April 2019, (b) 30 May 2019, (c) 09 June 2019, (d) 28 October 2019, (e) 15 November 2019, (f) 31 December 2019, (g) 12 January 2020, (h) 11 February 2020, (i) 10 March 2020.

Conclusions

MICROTOPS-II AOD (500 nm) and particulate matters (PM₁, PM_2.5, and PM₁₀) over Varanasi, from April 2019 to March 2020, were observed and analyzed daily, monthly as well as seasonally. Elemental analysis of five samples was also done through SEM-EDX analysis. The primary findings and outcomes of the study are presented here:

During the given period, mass concentrations of PM₁, PM_2.5, and PM₁₀ were found to be higher as compared to the NAAQS standard limit. In addition, mass concentration was found to be higher in the winter season while lower in the summer season. By calculating the ratio of PM₁ and PM_2.5 to PM₁₀, the dominancy of fine-mode particles in the winter season due to crop residue burning and vehicular emissions was found. While in the summer season, dominancy of coarse-mode particles was found due to dust storms.

Maximum AOD is obtained at a low wavelength while AOD is found to be minimum at a higher wavelength, which shows the presence of a greater concentration of fine particles than coarse particles. The seasonal variation in AOD was found to be maximum during the post-monsoon season and minimum during the monsoon season. Higher AOD at a higher wavelength shows the dominancy of coarse-mode particles during the summer season.

Aerosol optical depth produced by the MODIS Terra satellite and AOD measured by MICROTOPS II have a good correlation with R² = 0.5986. The mean values of MODIS AOD and MICROTOPS AOD were 0.72 and 0.97, respectively. The correlation between AOD and particulate matter (PM₁₀, PM_2.5, and PM₁) was found to be positive and very good.

The dominance of different elements such as F, C, O, and N occurred over five samples (Teotia and Teotia, 1994) indicates that fluorine (F) is the highest contributor to PM_2.5 particles except in November, indicating the impact of continental crust and soil contamination. Because of burning biomass in the winter, the concentration of carbon was the highest in the sample in November, and it was the second highest for the other 4 months.

The analysis of the backward trajectory of the air mass reveals that there are multiple routes for the conveyance of air masses originating from various source locations on different days, which indicates the presence of a variety of aerosols over Varanasi (Singh et al., 2014). Fine particles are a major concern for human health. So, a higher concentration of PM₁ particles may lead to heavy health risks in the future.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Author contributions

PKC: writing, original draft preparation, analysis of data, and plotting. AK: visualizing, conceptualizing, and analysis and editing. VP: conceptualizing and plotting. AKS: reviewing, supervising, and editing. All authors contributed to the article and approved the submitted version.

Funding

The work was partially supported by the Institute of Eminence (IoE) to BHU (Scheme no: 6031).

Acknowledgments

PKC is thankful to UGC, New Delhi for providing JRF.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The handling editor ST declared a past co-authorship with one of the authors AKS.

Publisher's note

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