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Article

Seasonal and Spatial Variations in Particulate Matter, Black Carbon and Metals in Delhi, India’s Megacity

by
Pramod Kumar
1,2,
Anchal Garg
2,3,
Khyati Sharma
2,
Uzma Nadeem
4,
Kiranmay Sarma
2,
Naresh Chandra Gupta
2,
Ashutosh Kumar
5,* and
Alok Kumar Pandey
1,*
1
Department of Environmental Sciences, Deshbandhu College, University of Delhi, Kalkaji, New Delhi 110019, India
2
University School of Environment Management, Guru Gobind Singh Indraprastha University, Delhi 110078, India
3
Earth System Science Department, Stanford University, Stanford, CA 94305, USA
4
Department of Environmental Studies, Mata Sundri College for Women, University of Delhi, Delhi 110002, India
5
Department of Electrical Engineering, University of Notre Dame, Notre Dame, IN 46637, USA
*
Authors to whom correspondence should be addressed.
Urban Sci. 2024, 8(3), 101; https://doi.org/10.3390/urbansci8030101
Submission received: 9 June 2024 / Revised: 28 July 2024 / Accepted: 29 July 2024 / Published: 31 July 2024
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Abstract

:
This study explores the spatial patterns of particulate matter (PM) in the megacity of Delhi. A GRIMM aerosol spectrometer is used to analyze different aerodynamic diameters (PM10, PM2.5, PM1.0), inhalable, thoracic, and alveolic particles, and black carbon (BC) at six prominent locations in Delhi during summer and winter. Additionally, metals (Pb, Fe, Ca, Al, Zn), along with silicon and sulfur, are analyzed using an ED-XRF spectrometer over the sampling locations during the summer season. The sampling site data are interpolated using the Kriging method to generate spatial maps to explore the air pollution problem in Delhi. East Delhi is observed to be the most polluted site, while Guru Gobind Singh Indraprastha University (GGSIPU) is the least polluted site. We further observe a high correlation between Al-Fe, Al-Ca, Zn-Pb, Ca-Fe, and S-Zn, indicating their common source of emission. Aerosols are also found to be highly enriched with metals like Al, S, Fe, Zn, and Pb, suggesting strong anthropogenic sources of these metals. Construction activities, resuspended dust, an increased number of vehicles, faulty agricultural practices, and soil could be recognized as major sources of the particulate concentration in an urban area like Delhi.

1. Introduction

In recent years, urban ambient air pollution has become a principal public health concern. According to some studies [1,2,3,4], air pollution is associated with several illnesses and early deaths worldwide. According to estimates, there were 6.67 million deaths due to air pollution in 2019 (including ambient, household, and ambient ozone pollution); this number was more than the combined number of deaths from water pollution (1.36 million) and lead pollution (0.90 million) together [5]. Middle-income and low-income countries are unreasonably affected by these deaths. As the most polluted capital city in 2021, Delhi in particular stands out as a major hotspot for air pollution [6]. The major sources of pollution in Delhi and the surrounding National Capital Region (NCR) are vehicle exhaust, building activities, garbage and biomass burning, industrial emissions, etc. [7,8,9,10,11].
Particulate matter (PM) is the key focus of concern in terms of Delhi’s air pollution, as it has considerable health consequences. Scientific studies have recognized a connection between exposure to air pollution and a range of human health conditions, such as chronic obstructive pulmonary disorder (COPD), ischemic heart disease (IHD), lung cancer (LC), stroke, asthma, acute lower respiratory infection (ALRI), coronary heart disease (CHD), cancer, premature mortality, mental health problems, etc. These findings have been well documented by other researchers worldwide [12,13,14,15,16,17,18,19,20,21,22,23]. The toxicity of PM varies depending on its size and the specific molecules or elements that are adhered to its surface. PM10 (particulate matter with a diameter of ≤10 µm), PM2.5 (particulate matter with a diameter of ≤2.5 µm), and PM1.0 (particulate matter with a diameter of ≤1 µm) have various health impacts in both the short term and long term [24,25,26,27,28]. PM10 is classified as a bigger particle, whereas PM2.5 and PM1.0 are smaller particles, specifically impacting the heart and blood vessels, with PM2.5 and PM1 having a more noticeable effect on cardiovascular health [29,30]. In addition, black carbon (BC), which is mostly emitted from combustion processes, has the potential to transport heavy metals, hence increasing the health hazards. Lead (Pb) and zinc (Zn) found in particles have been found to cause cancer and harm several bodily systems when inhaled [31,32,33,34,35].
This study aims to quantify the levels of particulate matter (PM10, PM2.5, PM1.0), inhalable particles, alveolic particles, thoracic particles, black carbon (BC), and related metals (Al, Si, S, Fe, Zn, Pb) in six diverse locations in Delhi. These locations include shopping centers, industrial areas, and residential areas. The measurements have been conducted throughout both the summer and winter seasons. Particles are classified according to their size and the specific area of the respiratory system where they can reach. As per the European Committee for Standardization [36], particles ranging from 10 to 100 μm are categorized as the “inhalable fraction”, indicating that they can be breathed in through the nose or mouth. Particles with a size ranging from 4 to 10 μm are known as the “thoracic subfraction” because they can reach the thoracic portion of the lungs. Particles smaller than 4 μm are referred to as the “alveolic fraction” because they can enter the alveoli in the lungs [37].
Since air pollution in Delhi is mostly a seasonal problem and exposure to fine particulate matter differs seasonally, it is important to examine the spatial and temporal arrangement of aerosol properties and their association with harmful metals in the polluted megacity of Delhi during the winter and summer seasons. This study is also significant in that it collects and analyzes on-the-ground air quality data from sampling areas that are not covered by the Central Pollution Control Board’s real-time data analysis via Continuous Ambient Air Quality Monitoring Stations (CAAQMSs).

2. Materials and Methods

2.1. Study Area

The scope of this study is centered on Delhi and its adjacent areas. New Delhi has grown to become one of India’s major metropolises in recent years. The city’s population has experienced substantial growth, reaching over 16.8 million as per the 2011 census (the most recent census). An increase in the number of industries and cars has overlapped with this population growth, and both are significant causes of Delhi’s air pollution. Delhi’s weather is classified as semi-arid, with noticeable changes in the seasons. Delhi’s summers, which last throughout April, May, and June, are notoriously hot. Most of the rainfall over the region is between July and September, i.e., monsoon season. Winter is characterized by chilly temperatures, which persist throughout December to February. The most common wind direction in Delhi is predominantly north-westerly; however, it changes to south-easterly during the monsoon months. Delhi’s urban expansion has spread to nearby regions such Ghaziabad and Noida in the east, Faridabad in the south, and Gurgaon in the southwest in recent years. These areas have experienced tremendous development as centers of industry and commerce, which has exacerbated the air pollution problems faced by the city. The combined effect of these advancements has elevated air quality to a crucial concern for environment and human health in New Delhi and its surrounding regions.
This study identified six key locations in New Delhi for monitoring air quality, each representing a different aspect of the city’s environmental and urban dynamics (Figure 1).
The selection of these sites was based on the aim of offering a thorough representation of the dispersion of particulate matter in various metropolitan areas of New Delhi. An overview of the sample sites is provided below:
(1)
Guru Gobind Singh Indraprastha (GGSIP) University Campus (28°41.01′ N; 77°17.00′ E): GGSIP is in Dwarka, in proximity to the international airport of Delhi and the Gurugram area, which is known for its rapid urbanization.
(2)
Indian National Army (INA) Market (28°34.01′ N; 77°12.01′ E): INA is in a busy commercial area; this market is a significant hub for shopping and cuisine in Delhi.
(3)
Janakpuri (28°37.01′ N; 77°04.01′ E): This residential and commercial area is characterized by open spaces with minimal vegetation. The Janakpuri site is far away from major industrial zones such as Mayapuri.
(4)
East Delhi (28°41.01′ N; 77°17.00′ E): East Delhi is located on the east bank of the Yamuna River and is categorized by its high population density and the presence of small-scale industries. The air pollution levels in the East Delhi area are significantly higher.
(5)
Mayapuri (28°37.01′ N; 77°06.02′ E): Mayapuri is an area in West Delhi that was previously known for its small-scale industries. However, over time, it has undergone a significant transformation and is now used as a residential area. The existence of metal manufacturers and automotive service facilities increases the local air pollution here.
(6)
Chawri Bazar (28°39.01′ N; 77°13.00′ E): Chawri Bazar is one of the oldest markets in Delhi. This area experiences intense traffic that contributes to the high air pollution levels.
The sites were chosen to represent the different environmental conditions in Delhi, encompassing residential, industrial, and commercial hubs. Table 1 presents the land-use activity at the sampling sites, together with their corresponding relative traffic density.
The objective of this study is to understand the spatial distribution of particulate matter and metals across various urban environments during the winter and summer seasons.

2.2. Instrumentation and Sampling Methodology

This study utilized a comprehensive method for collecting aerosol samples, with a specific emphasis on measuring the amounts of particulate matter and black carbon. Sampling was carried out at eight-hour intervals (from 09:00 a.m. to 05:00 p.m.) in both summer and winter months for the 2016 summer and 2016–2017 winter seasons to capture the changes in air quality that occur with the seasons. The methodology utilized two specialized instruments: the GRIMM portable aerosol spectrometer and the aethalometer. The selection of these instruments was based on their exceptional accuracy in measuring distinct particle sizes and kinds, as well as their capability to deliver instantaneous results.

2.2.1. GRIMM Aerosol Spectrometer

The GRIMM Model 1.108 aerosol spectrometer was used to measure various sizes of particulate matter, including PM1.0, PM2.5, and PM10, as well as the fractions that can be inhaled, affect the thoracic region, and reach the alveoli. This device functions based on the idea of light scattering, utilizing a semiconductor laser as the light emitter. The laser beam undergoes scattering due to its interaction with particles. The dispersed light is subsequently gathered at a 90° angle and directed toward a photodiode receiver. This setup enhances the detection of exceedingly minute particles, with a minimum size of 0.25 µm and 0.3 µm.
The spectrometer draws air samples at a controlled flow rate of 1.2 L per minute, collecting particles on a 47 mm polytetrafluoroethylene (PTFE) filter paper. This allows for both gravimetric analysis of the aerosol mass concentration and subsequent chemical analysis of the collected sample. The device is fitted with a pristine air system that is filtered with a protective covering to prevent the laser-optic assembly from being contaminated by dust. The particle concentrations are recorded in two different modes: a rapid mode with 6 s intervals and a normal mode with 60 s intervals. The data can be collected either by a detachable data card or the built-in RS-232 serial port.

2.2.2. Aethalometer for Black Carbon Measurement

An aethalometer (Model AE-51) measured the black carbon (BC) concentrations. This instrument is intended to analyze BC precisely at a wavelength of 880 nm, focusing on the absorption properties of BC particles. The AE-51, a portable device for measuring the BC concentration in real-time, has a detection limit of approximately 0.1 µg/m3. The aethalometer’s design and working wavelength make it especially effective for precisely measuring black carbon, a prominent contaminant in urban atmospheres. The setup and initialization steps include ensuring that the AE51 is fully charged before use, ensuring that the filter strip is properly and securely installed, and then turning on the power button, after which the device will perform a self-test and initialization routine. In the configuration, we set the sampling parameters such as the sampling interval (e.g., 5 min), flow rate, and measurement duration. After that, we placed the AE51 in the desired sampling location. The device starts drawing air through the filter and measuring black carbon concentrations. Once sampling was complete, we connected the AE51 to a computer using the provided USB cable. The particle size categories used for analyzing black carbon are typically based on particulate matter.

2.3. Trace Metals Analysis Using Energy-Dispersive X-ray Fluorescence (ED-XRF)

The energy-dispersive X-ray fluorescence (ED-XRF) technique was used to measure the amount of trace metals in the PM samples that were collected on polytetrafluoroethylene (PTFE) filter papers. ED-XRF is widely used due to its non-destructive methodology for detecting a wide variety of fundamental elements, such as Mg, Na, Al, P, Si, K, S, Ti, Ca, Mn, Cr, V, Ni, Zn, Cu, Fe, Sr, As, Ba, Pb, and Br.
ED-XRF involves the stimulation of atoms in a sample using a radioisotope or X-ray tube, occasionally with specified targets to improve the sensitivity. As a result of this stimulation, distinct X-ray fluorescence signals are emitted for each element, which are then detected by a semiconductor detector. The signal intensity, which is directly proportional to the concentration of the element, is recalculated using calibration curves to measure the concentration directly. The method entails the use of ionizing radiation to induce the removal and replacement of electrons within atomic shells, resulting in the emission of distinctive X-rays that are unique to each element. To show an intensity spectrum, these X-rays are converted into electrical impulses, converted to digital form, and examined. Element identification by qualitative methods involves assessing the energy levels and peak positions in the spectrum, whereas quantitative evaluation focuses on measuring the concentration of each element.
Preprocessing ED-XRF data is crucial to ensure accurate results, which involves first acquiring raw spectra from the ED-XRF instrument and applying smoothing algorithms to reduce statistical noise. After that, it is possible to detect and identify peaks corresponding to different elements using a threshold-based peak detection method. The particle size categories used for analyzing black carbon are typically based on the aerodynamic diameter i.e., PM10, PM2.5, PM1.0.

2.4. Spatial Analysis through Kriging Technique

We used the Kriging technique to generate spatial maps for the pollution using the collected air quality sampling data for different pollutants. Kriging is a geostatistical interpolation technique within a geographic information system (GIS) for examining spatial data. It is a local and stochastic interpolation method, and it is superior to deterministic methods in assessing prediction error with estimated variances [38]. This advanced technique generates maps by deriving predictive values for unmeasured locations based on regionalized variable theory, which assumes homogeneous spatial variation. Kriging uses the distance along with the direction between sample points to explain surface variations but has limitations with outliers and non-stationarity. The process involves (a) creating variograms and covariance functions to estimate statistical dependence based on autocorrelation models, and (b) predicting unknown values.
We have used the Kriging/Cokriging method under geostatistical analysis to generate Figure 2 and Figure 3, in which PM10/PM2.5/PM1.0 serve as the primary datasets, along with the wind (u and v components) as a secondary dataset, to decrease the uncertainty in the north and south region of Delhi where we have no sampling locations. A similar approach, i.e., Kriging incorporated with wind has been used to investigate the PM2.5 concentration in Xinxiang, China [39].
We used the indicator Kriging type and probability as an output surface type. Indicator Kriging uses thresholds to create binary data and then uses ordinary Kriging for this indicator data later. Ordinary Kriging provides interpolated or kriged values from equations that minimize the variance of the estimation error. The coefficients of the linear combination known as weights depend on (i) the distance between the sample point and the estimated point and (ii) the spatial structure of the variable. Ordinary Kriging is based on the assumption that the mean of the process is constant and invariant within the spatial domain. This is expressed as follows:
z(x) = μ + ε(x)
where μ is an unknown constant and generally considered the mean value of the regionalized variable; and z(x) is the value of the regionalized variable at any location x with a stochastic residual ɛ(x) with a zero mean and unit variance [40]. The maps generated through Kriging interpolation highlight areas of concern and enable better decision-making for public health and environmental policies.

3. Results

3.1. Spatial Distribution

3.1.1. Spatial Distribution of Particulate Matter

Spatial maps of the PM10, PM2.5 and PM1.0 in the winter and summer seasons are shown in Figure 2. During the winter season, East Delhi recorded the highest average concentrations of PM10, PM2.5, and PM1.0 at 561.8 µg/m3, 396.5 µg/m3, and 311.4 µg/m3, respectively. In contrast, the particle concentrations were found to be the lowest at GGSIPU, measuring 128.8 µg/m3 for PM10, 53.4 µg/m3 for PM2.5, and 26.9 µg/m3 for PM1.0. Surprisingly, the levels of PM10 exceeded the National Ambient Air Quality Standard (NAAQS) proposed by the Central Pollution Control Board (CPCB) of India by approximately 10 times at their highest point and 2 times at their lowest point. Furthermore, the PM2.5 concentrations exceeded the NAAQS by a wide margin. The minimum PM2.5 concentrations were almost 1.5 times higher, while the maximum average concentrations were nearly 10 times higher. The elevated levels of PM1.0, a sub-micron particulate matter, are concerning even though there is no formal NAAQS for PM1.0.
East Delhi had the highest average PM10 concentration of 227.7 µg/m3 during the summer season, while GGSIPU had the lowest PM10 value of 109.7 µg/m3. Mayapuri had the greatest amounts of PM2.5 and PM1.0, with 110.7 µg/m3 and 84.8 µg/m3, respectively. On the other hand, GGSIPU had the lowest levels, with PM2.5 at 46.3 µg/m3 and PM1.0 at 19.4 µg/m3.

3.1.2. Spatial Distribution of Inhalable, Thoracic and Alveolic Particles

Figure 3 depicts the spatial arrangement of inhalable, thoracic, and alveolic particles across Delhi. During the winter season, East Delhi had the greatest average concentrations of these particles (inhalable: 693.8 µg/m3, thoracic: 587.3 µg/m3, alveolic: 441.1 µg/m3), while the lowest values were seen at GGSIPU (inhalable: 199.8 µg/m3, thoracic: 141.2 µg/m3, alveolic: 79.5 µg/m3). During summer, East Delhi showed higher levels of inhalable and thoracic particles (473 µg/m3 and 255 µg/m3, respectively), where GGSIPU presented the lowest concentrations (inhalable: 166.9 µg/m3, thoracic: 120.1 µg/m3).
However, for alveolic particles, Mayapuri recorded the highest summer concentration (136.2 µg/m3), while GGSIPU continued the lowest (70.2 µg/m3), highlighting the significant spatial variability in particulate pollution within Delhi.

3.1.3. Spatial Distribution of Black Carbon

Figure 4 depicts the spatial distribution of black carbon in Delhi, showing the substantial variations between the winter and summer seasons. The investigation reveals major variations in pollution levels between the winter and summer seasons. In the winter season, East Delhi has elevated levels of black carbon, with an average concentration of 41.4 µg/m3. In contrast, GGSIPU stands out for its notably cleaner air, with the lowest observed value of 9.2 µg/m3. During the summer season, pollution levels are normally lower, although there are still significant levels of pollution. Chawri Bazar has the highest average pollution level at 32.3 µg/m3, whereas GGSIPU continues to have the cleanest air with a level of 4.5 µg/m3. The data presented demonstrates substantial differences in air quality throughout different locations of Delhi, which can be related to the effect caused by distinct pollution sources, such as vehicle emissions in areas with high population density like East Delhi and Chawri Bazar.

3.1.4. Spatial Distribution of Heavy Metals

This study also involved an in-depth analysis of metals (Pb, Fe, Ca, Al, Zn), silicon, and sulfur in the ambient air of Delhi during the summer season. We have analyzed the levels of only these metals because of the adverse health effects of these metal exposures. Spatial distribution maps have been generated (Figure 5) to illustrate the concentration levels of these elements in different parts of the city. The concentration ranges for Zn and Pb exhibited significant variation, with values ranging from 0.0 to 29 µg/m3 and 0.0 to 0.19 µg/m3, respectively. The East Delhi area had the highest reported values of both Zn and Pb, while the lowest levels of Zn were found at GGSIPU Dwarka and the lowest levels of Pb were found at INA Market. The concentrations of aluminum were reported to range from 1.7 to 4.76 µg/m3, with the highest levels recorded in Janakpuri and the lowest levels in East Delhi. The Si levels varied between 1.13 and 7.69 µg/m3, with the highest concentration observed in East Delhi and the lowest concentration observed at INA Market. The GGSIPU Dwarka site exhibited the lowest quantities of most elements, save Si, suggesting that it is the most pristine place among those studied. The increased silicon (Si) levels seen in Dwarka are probably associated with construction activities taking place in the vicinity. The concentrations of sulfur (S) varied between 1.64 and 4.14 µg/m3 throughout Delhi, with Janakpuri and East Delhi possessing the highest and lowest levels, respectively. Janakpuri had the highest values of Ca (0.66–2.26 µg/m3) and Fe (0.32–0.95 µg/m3), while GGSIPU Dwarka had the lowest values. The regional changes in the metal concentrations can be related to factors such as the density of vehicles, traffic congestion, construction activities, industrial emissions, land-use patterns, and the existence of green areas. GGSIPU Dwarka, renowned for its well-established infrastructure and abundant plant life, typically exhibited reduced levels of pollutants. Conversely, East Delhi, characterized by dense traffic, recorded elevated levels of S, Zn, and Pb.
Janakpuri had the highest observed amounts of aluminum (Al), calcium (Ca), and iron (Fe), whereas INA Market had the greatest concentration of silicon (Si). Si was the most abundant element in the ambient air, followed by Al, S, Ca, Fe, Zn, and Pb, in that specific sequence (Si > Al > S > Ca > Fe > Zn > Pb). This extensive analysis emphasizes the necessity of implementing focused pollution control strategies, taking into account the distinct sources and attributes of each area within the metropolis. The metal concentrations observed in the present research work show resemblances to those documented in prior investigations, but with some variations. Our findings closely correspond to the levels reported in [41,42,43]. It is important to mention that the quantities we recorded are lower than those reported in [44]. They detected much higher amounts of Ca (18.32 µg/m3), Fe (16.43 µg/m3), Al (13.34 µg/m3), and Pb (0.441 µg/m3).

3.2. Seasonal Variations

3.2.1. Seasonal Variations in Particulate Matter (PM10, PM2.5, and PM1.0)

The quantitative analysis revealed a noteworthy seasonal fluctuation in the particulate matter concentrations at various locations in Delhi. The PM10 concentrations during the winter season were considerably higher than in the summer. At Chawri Bazar, the increase was 1.75 times, at GGSIPU, it was 1.17 times, at East Delhi, it was 2.46 times, at Mayapuri, it was 1.70 times, at Janakpuri, it was 2.13 times, and at the INA site, it was 1.07 times. Similarly, the concentrations of PM2.5 were significantly higher during the winter season. There was a 1.74-fold increase at Chawri Bazar, a 1.15-fold increase at GGSIPU, a substantial 4.43-fold increase at East Delhi, a 1.69-fold increase at Mayapuri, and a 3.40-fold increase at Janakpuri. However, the INA site showed almost identical concentrations in both the winter and summer seasons. The difference in concentrations between winter and summer was even more significant for PM1.0. At Chawri Bazar, the winter levels were 1.77 times higher than summer levels. At GGSIPU, the difference was 1.38 times. In East Delhi, the difference was an unusual 5.40 times. In Mayapuri, the difference was 1.66 times. And in Janakpuri, the difference was a remarkable 5.65 times. Conversely, the INA site experienced a limited decline throughout the winter, with concentrations being 0.96 times lower than those in summer. These data highlight the notable difference in particulate matter levels between seasons, indicating the impact of many human-made and environmental elements that contribute to fluctuations in the quality of air in urban areas.

3.2.2. Seasonal Variations in Inhalable, Thoracic and Alveolic Particles

In the context of the particulate matter size fractions, a distinct seasonal variation in the concentration levels was quantified across various urban locales. Specifically, the concentration of inhalable particles during the winter season was found to be elevated, surpassing the summer levels by a factor of 1.87 in Chawri Bazar, 1.19 in GGSIPU, 1.46 in East Delhi, 1.76 in Mayapuri, 1.77 in Janakpuri, and 1.10 in the INA site (Figure 6). The winter concentrations of thoracic particles were higher than the summer concentrations in several locations. Specifically, there was a 1.76-fold rise in Chawri Bazar, a 1.17-fold increase in GGSIPU, a 2.30-fold increase in East Delhi, a 1.71-fold increase in Mayapuri, a 2.07-fold increase in Janakpuri, and a 1.07-fold increase at the INA site. The winter concentrations of thoracic particles were higher than the summer concentrations in several locations. Specifically, there was a 1.76-fold rise in Chawri Bazar, a 1.17-fold increase in GGSIPU, a 2.30-fold increase in East Delhi, a 1.71-fold increase in Mayapuri, a 2.07-fold increase in Janakpuri, and a 1.07-fold increase at the INA site.
The seasonal differences for alveolic particles were almost similar to the thoracic particles, i.e., higher concentrations during winter. Chawri Bazar experienced a 1.70-fold increase, GGSIPU had a 1.13-fold increase, East Delhi had a 3.29-fold increase, Mayapuri had a 1.69-fold increase, Janakpuri had a 2.46-fold increase, and the INA site had a slight increase of 1.02-fold. Figure 4 represents the seasonal changes in aerosol particles, including PM, BC, and inhalable, thoracic, and alveolic particles. The results highlight the substantial influence of seasonal elements on the levels of particulate matter, emphasizing the need for a detailed comprehension of air quality trends to manage the environment and formulate policies effectively.

3.2.3. Seasonal Variations in Black Carbon

A significant increase in the black carbon (BC) concentrations was reported during the winter season compared to summer in the comparison investigation. More precisely, the levels of the winter concentrations at GGSIPU and East Delhi were much higher, surpassing the values observed during summer by factors of 2.04 and 2.40, respectively. However, the increases in Mayapuri, Janakpuri, and INA were fairly modest, where the winter concentrations exceeded summer levels by factors of 1.02, 1.07, and 1.17, respectively. Interestingly, Chawri Bazar exhibited an unusual pattern, as the concentrations of BC (black carbon) during winter were lower compared to those during summer.
The clear seasonal fluctuations in the BC levels highlight the complex relationship between local emissions, weather circumstances, and atmospheric processes that affect the behavior of particulate matter in urban areas. The significant presence of particulate pollution in East Delhi can be mostly attributed to the area’s dense vehicular population and regular traffic congestion, together with the kind of land-use activities, such as residential and industrial zones. On the other hand, the lower levels of concentration observed at GGSIPU may be attributed to the ample presence of flora in the vicinity, which can enhance air quality.

3.3. Correlation Analysis

In this study, we performed an in-depth correlation analysis to understand the links between different metallic elements, particulate matter (PM), black carbon (BC), metal pollutants (Pb, Fe, Ca, Al, Zn), silicon, and sulfur in the air of New Delhi. The correlation coefficients, as presented in Table 2 and Table 3, have been calculated using Pearson’s correlation matrix (p < 0.05), providing valuable information about the potential causes and interactions of these atmospheric pollutants.
A substantial correlation has been observed among the different metals, indicating their shared origin. Table 2 shows a strong link between Al and Ca (r = 0.93), Al and Fe (r = 0.99), and Ca and Fe (r = 0.96), which suggests the common origin of these elements. The strong positive association (r = 0.98) between Pb and Zn was mainly caused by road dust. The significant association (r = 0.80) between S and Zn suggested that there are common sources, such as wood combustion and automotive traffic, that are not related to the Earth’s crust. The results of this research are consistent with earlier investigations conducted by [43,44,45,46], all of which reported associations between these metals.
This research uncovered clear seasonal trends. During the winter season, BC showed a significant positive relationship with smaller particles (PM2.5: r = 0.82, PM1.0: r = 0.83, alveoli: r = 0.78), and a moderate positive relationship with larger particles (PM10: r = 0.71, inhalable: r = 0.62, thoracic: r = 0.70). This implies that during the winter season, the sources of particulates and BC, such as combustion activities and biomass burning, are more widespread, resulting in a greater emission of smaller particles. The research conducted by [47,48,49,50] supports our findings and also demonstrates connections between BC and PM.
A strong correlation (r > 0.80) has been observed between the elements Zn and Pb, as well as between particulate matter and BC, especially concerning smaller particles. This indicates the tendency for these aerosols to bind heavy metals onto their surfaces. The association between metals and PM is in line with the results of other research, including the studies conducted by [43,51]. Wintertime observations revealed a substantial association between S and all the particle sizes, whereas a modest–high correlation was seen between S and BC, indicating higher combustion activities. On the other hand, summer showed less strong to moderate correlations, suggesting that there are changes in the strength of the source and atmospheric processes during the different seasons.

4. Discussion

The concentrations of PM10 and PM2.5 exceeded the National Ambient Air Quality Standards (NAAQSs) proposed by the Central Pollution Control Board (CPCB) of India by approximately 2–10 times and 1.5–10 times, respectively. Previous studies show health hazards, especially respiratory and cardiopulmonary problems, with such high exposure to particulate matter [17,21,24]. Although there is no NAAQS for PM1.0, the high concentration of PM1.0 reported in this study and the associated health risks show the urgency of ambient air quality guidelines both nationally and internationally. The high exposure concentration of particulate matter highlights the urgent requirement for appropriate air quality management, along with approaches to mitigate the risks to health. Location-wise, East Delhi has the highest levels of particulate matter, while the lowest are at the GGSIPU campus.
The significant presence of particulate pollution in East Delhi can be mostly attributed to the area’s dense vehicular population and regular traffic congestion, together with the kind of land-use activities, such as residential and industrial zones. On the other hand, the lower levels of concentration at GGSIPU may be attributed to the ample presence of flora in the vicinity, which can enhance air quality.
The regional changes in the metal concentrations can be related to factors such as the density of vehicles, traffic congestion, construction activities, industrial emissions, land-use patterns, and the existence of green areas. GGSIPU Dwarka, renowned for its well-established infrastructure and abundant plant life, typically exhibited reduced levels of pollutants. Conversely, East Delhi, characterized by its dense traffic, recorded elevated levels of S, Zn, and Pb in the metropolis. The metal concentrations observed in the present research work show resemblances to those documented in prior investigations, but with some variations. Our findings closely correspond to the levels reported in [41]. They observed that the concentrations of Pb and Fe in Delhi were 0.66 µg/m3 and 15 µg/m3, respectively, which are close to our findings. The study conducted by [42] found that the concentrations of Fe ranged from 0.03 to 2.950 µg/m3, Zn ranged from 0.003 to 2.350 µg/m3, and Pb ranged from 0.003 to 4.00 µg/m3 in Islamabad, Pakistan, which is also comparable to our findings. These findings align with the data we have collected. Our findings align with the amounts reported in [43] for Al (0.014–0.248 µg/m3), Fe (0.005–0.074 µg/m3), Zn (0.00–0.0006 µg/m3), and Pb (0.00–0.0007 µg/m3). This comparison emphasizes the fluctuation in the metal concentration levels observed in various studies and geographic areas, emphasizing the necessity of ongoing monitoring and analysis to comprehend the changing trends of air quality and pollution.
The differences in the particulate matter, black carbon, and metal concentrations between winter and summer arise due to a variety of factors, such as the sources of emissions, atmospheric conditions, meteorological influences, human activities, and local vs. regional influences [8,9,10,34,47,50,51].
In winter, increased residential heating means more use of coal and wood for heating, while during summer, there are potentially higher emissions from wildfires, increased industrial activity, and possibly higher vehicular emissions due to increased travel [10,32,42]. On the other hand, atmospheric conditions in terms of the winter temperature inversions can trap pollutants close to the ground, leading to higher concentrations of black carbon in the lower atmosphere, while during summer, the increased vertical mixing in the atmosphere can disperse pollutants more effectively, potentially lowering the ground-level concentrations [52,53]. However, if similar trapping occurs in summer due to different meteorological conditions, the differences might not be stark. Winter generally has less precipitation, leading to less washout of black carbon from the atmosphere, while during summer, increased precipitation can remove black carbon from the atmosphere through wet deposition. As evident from the COVID-19 pandemic, human activities are mainly responsible for the emission of pollutants in the ambient air [54]. Also, human activities, such as heating in winter or increased use of air conditioning in summer, contribute differently to black carbon emissions depending on the region and lifestyle.

5. Conclusions

This study performed a detailed analysis of the spatial distribution of aerosol samples (PM10, PM2.5, PM1.0, BC, inhalable, thoracic, alveolic) and metal elements (Al, Fe, Ca, Zn, Pb, S) during the summer and winter seasons at six different locations in the ambient air of Delhi. The main objective of this study is to analyze the intricate connections and fluctuations between particulate matter (PM), black carbon (BC), and metal components by identifying the spatial distribution and seasonal variability, and by using statistical and geographic information system (GIS) tools. This study evaluated the effectiveness of a Kriging model in predicting the pollution concentrations at unmonitored locations in Delhi using ground-based monitoring datasets. The study examined how local factors affect pollution concentrations, in addition to urban background levels. Using Kriging interpolation, we found substantial variations in the pollution levels across different locations and seasons. The particulate matter concentrations were highest in East Delhi, while GGSIPU had the lowest levels. During the winter season, the pollution levels were higher than in the summer. There were increased concentrations of particulate matter, inhalable particles, thoracic particles, and alveolic particles. A high association was found between fine particles and BC, indicating that particulates in the 0.3–2.5 μm size range accounted for over 99% of the total. Furthermore, the study also examined the relationships between metals (Zn-Pb; Al-Ca; S-Zn; Al-Fe) in various particle fractions, demonstrating the presence of shared emission sources. Aerosols were found to be rich in Al, S, Fe, Zn, and Pb, reflecting strong anthropogenic influences and distinct land-use patterns at the sites. When comparing the concentrations observed in our study areas with the National Ambient Air Quality Standard (NAAQS) for Pb (0.50 µg/m3), none of the sites exceeded the permissible limit, indicating that Pb is not a significant concern in these areas at present. Several factors influence air quality, including meteorological conditions, location, and the strength of emission sources. Pollutant concentrations vary across a city and are not monitored in some areas. This study’s findings can help highly polluted cities like Delhi identify possible air quality hotspots and adopt control measures. Improving our knowledge of the compositions, characteristics, and origins of air pollutants in Delhi is crucial. This will help us advance our research and develop effective measures to address the local conditions based on strong scientific evidence. It is also essential to consider and deal with these spatial and seasonal variables to reduce the negative effects on health and the environment caused by these pollutants. This will ultimately improve the overall well-being of urban people and the environment.

Author Contributions

Conceptualization, P.K. and N.C.G.; methodology, P.K.; software, K.S. (Khyati Sharma) and K.S. (Kiranmay Sarma); validation, P.K., A.G., K.S. (Kiranmay Sarma) and U.N.; formal analysis, K.S. (Khyati Sharma) and K.S. (Kiranmay Sarma); investigation, P.K., A.K., A.K.P. and K.S. (Kiranmay Sarma); resources, P.K., A.G. and A.K.P.; data curation, P.K. and A.G.; writing—original draft preparation, P.K., A.G. and K.S. (Khyati Sharma); writing—review and editing, all authors; visualization, K.S. (Kiranmay Sarma) and A.K.P.; supervision, N.C.G. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

The authors state that all the data will be made available to readers upon reasonable request.

Acknowledgments

One of the authors, Pramod Kumar, gratefully acknowledges the scholarship provided by GGSIP University, New Delhi, under the Indraprastha Research Fellow (IPRF) program.

Conflicts of Interest

The authors declare no conflicts of interest.

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Urbansci 08 00101 g001
Figure 1. Study area map showing the locations of the sampling sites used for the seasonal aerosol and metal monitoring in New Delhi.
Figure 1. Study area map showing the locations of the sampling sites used for the seasonal aerosol and metal monitoring in New Delhi.
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Figure 2. Spatial map of PM10, PM2.5 and PM1.0 over the winter and summer seasons in New Delhi. (a) PM10, (b) PM2.5, (c) PM1.0.
Figure 2. Spatial map of PM10, PM2.5 and PM1.0 over the winter and summer seasons in New Delhi. (a) PM10, (b) PM2.5, (c) PM1.0.
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Figure 3. Spatial distribution of inhalable, thoracic and alveolic particles over the winter and summer seasons in New Delhi. (a) Inhalable, (b) Thoracic, (c) Alveolic.
Figure 3. Spatial distribution of inhalable, thoracic and alveolic particles over the winter and summer seasons in New Delhi. (a) Inhalable, (b) Thoracic, (c) Alveolic.
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Figure 4. Spatial distribution of black carbon over the winter and summer seasons in New Delhi.
Figure 4. Spatial distribution of black carbon over the winter and summer seasons in New Delhi.
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Figure 5. Spatial distribution of metals in New Delhi.
Figure 5. Spatial distribution of metals in New Delhi.
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Figure 6. Distribution of aerosols and metals during the summer and winter seasons.
Figure 6. Distribution of aerosols and metals during the summer and winter seasons.
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Table 1. Comparative general characteristics of the study sites.
Table 1. Comparative general characteristics of the study sites.
Name of Site (District)Land-Use ActivityLongitude/LatitudeRelative Traffic Density *Status of Air Pollution
East Delhi (East)Residential and industrial28°41.01′ N/77°17.00′ E+++++Medium to heavy
Janakpuri (West)Residential and commercial28°37.01′ N/77°04.01′ E++++Medium
Indian National Army (INA) Market (South)Shopping hub28°34.01′ N/77°12.01′ E+++++Medium to heavy
Mayapuri (West)Residential and industrial28°37.01′ N/77°06.02′ E+++++Medium to heavy
Chawri Bazar (North)Market28°39.01′ N/77°13.00′ E+++++Medium to heavy
Guru Gobind Singh Indraprastha (GGSIP) University (South West)Institutional28°41.01′ N/77°17.00′ E+Low
* The relative traffic density represented as ‘+’ sign in the table is represented as follows: low 1, low to medium 3, medium 4, medium to heavy 5, or heavy 7.
Table 2. Pearson coefficient relation between PM, BC, metals (Al, Ca, Fe, Zn, Pb), silicon and sulfur during the summer season.
Table 2. Pearson coefficient relation between PM, BC, metals (Al, Ca, Fe, Zn, Pb), silicon and sulfur during the summer season.
BCPM10PM2.5PM1.0InhalableThoracicAlveolicAlSiSCaFeZnPb
Summer
BC1.00
PM100.331.00
PM2.50.440.641.00
PM1.00.500.530.981.00
Inhalable0.150.860.220.071.00
Thoracic0.300.990.560.440.911.00
Alveolic0.420.890.920.840.580.841.00
Al−0.130.03−0.34−0.28−0.020.04−0.221.00
Si0.08−0.140.520.63−0.45−0.210.23−0.331.00
S0.590.620.07−0.020.760.660.370.00−0.641.00
Ca0.170.17−0.18−0.130.050.17−0.050.94−0.420.251.00
Fe−0.080.08−0.35−0.310.050.10−0.200.99−0.430.120.961.00
Zn0.260.790.280.140.950.830.59−0.31−0.320.76−0.21−0.241.00
Pb0.290.820.250.100.970.860.58−0.19−0.430.84−0.07−0.110.991.00
Categorization of color coding based upon the positive and negative correlation of different values range from −1 to +1.
(−1.00) to (−0.75) (−0.49) to (−0.25) 0.00 to 0.24 0.50 to 0.74
(−0.74) to (−0.50) (−0.24) to (−0.01) 0.25 to 0.49 0.75 to 1.00
Table 3. Pearson coefficient relation between PM, BC, metals (Al, Ca, Fe, Zn, Pb), silicon and sulfur during the winter season.
Table 3. Pearson coefficient relation between PM, BC, metals (Al, Ca, Fe, Zn, Pb), silicon and sulfur during the winter season.
BCPM10PM2.5PM1.0InhalableThoracicAlveolicAlSiSCaFeZnPb
Winter
BC1.00
PM100.711.00
PM2.50.820.971.00
PM1.00.830.971.001.00
Inhalable0.620.980.910.921.00
Thoracic0.701.000.960.960.991.00
Alveolic0.780.981.001.000.940.981.00
Al0.340.630.520.540.660.640.551.00
Si0.080.170.270.260.080.160.24−0.011.00
S0.710.920.850.870.940.930.870.82−0.041.00
Ca−0.130.290.040.090.430.310.110.65−0.240.511.00
Fe0.360.520.450.450.550.530.470.84−0.430.720.401.00
Zn0.860.930.990.980.850.920.980.490.220.82−0.050.481.00
Pb0.900.890.970.970.800.880.960.450.260.78−0.120.430.991.00
Categorization of color coding based upon the positive and negative correlation of different values range from −1 to +1.
(−1.00) to (−0.75) (−0.49) to (−0.25) 0.00 to 0.24 0.50 to 0.74
(−0.74) to (−0.50) (−0.24) to (−0.01) 0.25 to 0.49 0.75 to 1.00
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Kumar, P.; Garg, A.; Sharma, K.; Nadeem, U.; Sarma, K.; Gupta, N.C.; Kumar, A.; Pandey, A.K. Seasonal and Spatial Variations in Particulate Matter, Black Carbon and Metals in Delhi, India’s Megacity. Urban Sci. 2024, 8, 101. https://doi.org/10.3390/urbansci8030101

AMA Style

Kumar P, Garg A, Sharma K, Nadeem U, Sarma K, Gupta NC, Kumar A, Pandey AK. Seasonal and Spatial Variations in Particulate Matter, Black Carbon and Metals in Delhi, India’s Megacity. Urban Science. 2024; 8(3):101. https://doi.org/10.3390/urbansci8030101

Chicago/Turabian Style

Kumar, Pramod, Anchal Garg, Khyati Sharma, Uzma Nadeem, Kiranmay Sarma, Naresh Chandra Gupta, Ashutosh Kumar, and Alok Kumar Pandey. 2024. "Seasonal and Spatial Variations in Particulate Matter, Black Carbon and Metals in Delhi, India’s Megacity" Urban Science 8, no. 3: 101. https://doi.org/10.3390/urbansci8030101

APA Style

Kumar, P., Garg, A., Sharma, K., Nadeem, U., Sarma, K., Gupta, N. C., Kumar, A., & Pandey, A. K. (2024). Seasonal and Spatial Variations in Particulate Matter, Black Carbon and Metals in Delhi, India’s Megacity. Urban Science, 8(3), 101. https://doi.org/10.3390/urbansci8030101

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