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Article

Estimation of Particulate Matter (PM10) Over Middle Indo-Gangetic Plain (Patna) of India: Seasonal Variation and Source Apportionment

by
Ningombam Linthoingambi Devi
1,
Ishwar Chandra Yadav
2,* and
Amrendra Kumar
1
1
Department of Environmental Science, School of Earth, Biological and Environmental Science, Central University of South Bihar, Gaya 824236, India
2
Department of Environmental Studies, NCWEB Hansraj College, University of Delhi, New Delhi 110007, India
*
Author to whom correspondence should be addressed.
Atmosphere 2024, 15(8), 878; https://doi.org/10.3390/atmos15080878
Submission received: 30 May 2024 / Revised: 16 July 2024 / Accepted: 17 July 2024 / Published: 23 July 2024
(This article belongs to the Special Issue Biomass Combustion and Emission Analysis)
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Abstract

:
Despite extensive research on particulate matter (PM) pollution in India’s Indo-Gangetic Plain (IGP), source apportionment remains challenging. This study investigates the effect of particulate matter (PM10)-associated water soluble inorganic ions (WSIIs) on ambient air concentration across the middle IGP from January to December 2018. Moreover, the seasonal fluctuation and chemical characterization of PM10 were assessed for the year 2018. The results revealed a high concentration of PM10 (156 µg/m3), exceeding the WHO and National Ambient Air Quality Standard (NAAQS) limits. The highest PM10 levels were observed during autumn, winter, summer, and the rainy season. The study identified SO42− and NH4+ as the most common WSIIs, constituting 46% and 23% of the total WSIIs. Source apportionment analysis indicated that street dust, biomass burning, and vehicle and industrial emissions together with secondary formation significantly contributed to IGP’s PM pollution. Additionally, the investigation of air mass back trajectory suggests that air quality in IGP is largely influenced by eastern and western Maritime air masses originated from the Arabian Sea, the Bay of Bengal, Gujarat, Afghanistan, Pakistan, and Bangladesh.

1. Introduction

Global air pollution has become an urgent problem specifically for the large cities. It endangers human health, welfare, and the global climate system. Air pollution is known to have a variety of detrimental effects, ranging from upsetting the hydrological cycle to raising global temperatures [1,2,3]. Furthermore, air pollution poses serious health risks and causes of mortality as well as specific diseases [4]. It ranks fourth among risk factors for mortality and was the cause of 12% of deaths worldwide in 2019 [5]. Approximately 80% of the world’s population lives in developing nations with middle- to low-income economies, where air pollution is the primary cause of 90% of illnesses and fatalities. India’s rapid economic growth, high industrial rates, urbanization, population expansion, and increased automobile emissions have all significantly declined the country’s air quality. This harms both the environment and human health. According to WHO [6], particulate matter (particles having an aerodynamic diameter of 10 μm or less) is considered as one of the primary indicators of air quality.
According to WHO [7], exposure to air pollution is thought to be the third leading cause of death worldwide, accounting for over 7 million premature deaths. Addressing air pollution, which is the second most significant risk factor for noncommunicable diseases, is a key to protecting public health [8]. In 2017, it was estimated that exposure to air pollution caused 1.24 million fatalities in India, with ambient particulate matter exposure accounting for 54% of these deaths [9]. An evaluation of air quality is necessary to comprehend the effects of particulate matter (PM) on the environment and human health [10,11,12]. Because of its potential to harm human health, PM10 has drawn much attention. It has been shown that the presence of water-soluble ions in the particles is responsible for a considerable amount of PM10’s harmful health impacts [13,14]. PM10, a combination of solid and liquid particles suspended in the atmosphere, is produced by both natural and manmade sources. These particles can come from a variety of sources, including natural dust, building sites, automobile emissions, and industrial activities. Major constituents of PM are WSIIs, of which ammonium, nitrate, and sulfate are the predominant ions [15,16,17]. These ions are essential to atmospheric chemistry. Ionic species are important markers for determining the source characteristics of air quality.
The Indo-Gangetic Plain (IGP) of India, which hosts 9% of the world’s population, is identified as one of the most heavily polluted regions worldwide. The IGP in South Asia is characterized by a huge and diversified aerosol load due to many emission sources, its distinct terrain and position, regional meteorology, socioeconomic development, and human behavior [18,19,20,21,22]. The increased aerosol loading over the area, which is attributed to various anthropogenic activities, shows substantial spatiotemporal variability and considerable seasonal heterogeneity in its characteristics [23,24,25,26,27,28]. The potential health risks of particulate matter for humans and the quality of the air have led to extensive study on the subject in recent years on all continents, including the IGP region [29,30,31,32,33,34]. Previous studies showed that the formation of secondary inorganic particles and other anthropogenic sources over the IGP, India, is caused by soil dust, agricultural activities, burning of biomass, vehicle emissions, and industries [31,32,33,35]. Few concurrent long-term measurements of PM’s chemical characteristics have been conducted over a wide range of the IGP. Most of the published research on the chemical characteristics of aerosol over the IGP region is based on single-location observations and experiments. Variations in inorganic secondary particles are more relevant from the standpoint of human health and warrant more investigation [30,36,37]. The goal of the current research is to understand the molecular characteristics and sources of PM10 over the middle Indo-Gangetic Plain (Patna). To further understand the pattern of PM-related air pollution in a highly populated area, seasonal variation in PM10 was evaluated. This research may be useful in examining the long-term trends of PM10 in the middle Indo-Gangetic Plain (Patna) and the resultant health impacts. The findings from this study will advance our understanding of the composition, sources, and potential health impacts of PM10 in this specific urban environment. Furthermore, they will provide valuable insights to policymakers and public health experts for devising effective strategies to reduce PM10 exposure and associated health risks in residential areas.

2. Materials and Methods

2.1. Study Area

More details about the study site and sampling of PM have been discussed elsewhere [38,39]. Patna, the capital city of the Indian state of Bihar, situated on the southern bank of the river Ganges, is one of the most polluted cities in India nowadays after Delhi and Kolkata (Figure 1) [38,40]. It lies on an average elevation of 53 m (174 ft) above MSL. It is a good representation of the metropolitan environment but it lacks distinct source points, such as a bustling junction, bus stop, or train station. The climate can be characterized by a sweltering and dry summer and freezing winter; the temperature during summer can reach 43 °C, and in winter can drop below 8 °C. The city’s population is currently about 2 million; it is severely congested and overcrowded. There is a noticeable rise in PM10 due to distressing rates of population and vehicle expansion as well as frequent traffic jams. Although vehicles and industries (such as brick kilns) are the two most significant contributors to Patna’s ambient PM levels, contributions from other pollution sources, such as roadside dust, transboundary migrations, solid waste disposal, and local sources, are also common sources.

2.2. Sampling Strategy

The fate of PM in the air is highly variable and depends on several factors. Few sources of PM10 are season- or time-specific, while others are space-specific. To capture the fate of PM10 in the Patna region, we decided to conduct PM sampling for the period of one year. PM10 samples (N = 120) were collected on a quart fiber filter (QFF) (0.45 mm pore size and 47 mm diameter, make: Whatman, India) during January–December 2018 from Patna city, representing four seasons, i.e., winter (December–February), summer (March through May), rainy (June to September), and autumn (October through November). PM10 samples were collected using a high-volume sampler (APM 550 M, Envirotech Pvt. Ltd., New Delhi, India) operated at a flow rate of 1.0 m3 h−1. The QFF was prebaked in an oven at 350–400 °C for 6 h before sampling to avoid contamination. In order to remove the moisture, all QFFs were desiccated for 24 h before sampling. The mass concentrations of PM10 were measured gravimetrically by weighing the QFF before (blank filter) and after (total filter) sampling using an analytical balance (Make-Sartorious AG, BS224 S, Goettingen, Germany) with a reading precision of 0.1 mg. Under standard circumstances (25 °C and 1 atm), each sample’s air volume was calculated using the temperature and pressure measured during sampling. Then, the air samples were placed into plastic covers with aluminum foil and kept in a refrigerator at 4 °C until chemical analysis.

2.3. Chemical Characterization

Detailed procedures for the chemical characterization of PM10 have been documented previously [39]. Briefly, a portion (2.8 cm2) of QFFs was cut and extracted with 50 mL of Milli-Q water (specific resistivity 18.2 MΩ) in an ultrasonication bath for 30 min. The extract was filtered through a 0.2 μm pore size cellulose acetate filter (Make-Whatman, India). Later, ion chromatography analyzed the filtrate for WSIIs (Ca2+, Mg2+, Na+, K+, NH4+, Cl−, SO42−, and NO3−) (Metrohm, 881 Compact IC). For anions (Column-Metrosep A sup 5–250 mm), 0.34 gm sodium carbonate and 0.084 gm of sodium bicarbonate were dissolved in 1 L Milli-Q water and the extract was filtered with the help of 0.42 µm filter paper and then transferred to the respective mobile phase bottle.
Similarly, for cations (Column-Metrosep C4-150 mm), 5.8 mL concentrated nitric acid (HNO3) were dissolved in 100 mL Milli-Q water (1 Molar), and 0.117 gm pyridine dicarboxylic acid were dissolved in 100 mL of slightly hot water and mixed with 1.7 mL of 1M nitric acid, then made up to 1 L. The mobile phase extract was filtered with the help of 0.42 µm filter paper and transferred to the respective mobile phase bottle. Seven blank filters were also analyzed, and the ion concentrations were corrected for the respective blank concentrations. Therefore, the sample quantities that exceeded the detection limits were quantified, while the blank quantities were corrected by subtracting the mean blank amounts from the sample amounts.

2.4. Quality Assurance and Quality Control (QA/QC)

In order to minimize background contamination, five blanks samples were tested together with original samples. The blank samples were exposed and open at the sampling site in the same manner as the original QFF samples. The concentrations of WSIIs in QFF sample were estimated by subtracting the blank values. The method detection limit (MDL) was estimated as means plus 3 times signal-to-noise ratio (S/N) obtained from the lowest spiked calibration standard. The measurements in this investigation were adjusted for background contamination.

2.5. Statistical Analysis

The statistical analysis of measured data was performed using SPSS software (version 21). Statistical summaries (mean, median, and range) of mass concentration and chemical species of PM10 were made using a Microsoft Excel sheet. The Spearman’s rank correlation coefficient among chemical species of PM10 was performed to assess their relationship based on their rank.

2.6. Source Apportionment Study

Principal component analysis (PCA) and the molecular diagnostic ratio (MDR) are two significant methods frequently used for source assessment studies. MDRs, or the ratios of specific pairs of particular compounds, are frequently employed as organic tracers and WSII source category markers. However, the wide variety of combustion conditions and environmental degradation processes that are known to create substantial variability in the emission and degradation of certain compounds may pose a threat to the use of MDRs as reliable source apportionment methodologies. An additional method for source allocation research is principal component analysis (PCA). PCA is a widely used factor analysis technique for the purpose of source identification and apportionment of PM10. PCA is a dimensionality reduction qualitative technique that is often used to reduce the dimensionality of large datasets, by transforming a large set of variables into a smaller one that still contains most of the information in the large set [41,42].

2.7. Air Mass Back Trajectory

The air mass trajectory-based approach is valuable for locating possible PM sources and obtaining crucial information about their origin. This technique tracks the flow of air masses using both forward and backward trajectories [43]. Examining these trajectories might identify the geographic origins of air masses and provide essential details about the sources of PM pollution. This work investigated long-range atmospheric and regional transport to the study area by generating air mass back trajectories using the HYSPLIT model and global data assimilation system (GDAS) global meteorological data. Five-day isentropic air mass back trajectories were computed 500 m above sea level (AGL) to examine the influence of air masses originating from nearby or remote areas on PM concentration.

3. Results and Discussion

3.1. Concentration and Seasonal Variation of PM10

In this study, the annual mass concentrations of PM10 ranged from 90.4 to 238 μg/m3 (median 156 μg/m3). The average annual concentration of PM10 measured in this study exceeded the annual recommended limit of the National Ambient Air Quality Standard (NAAQS) of India (60 μg/m3) and WHO (15 μg/m3). The mass concentration of PM10 in this study was compared with previous studies reported globally (Figure 2 and Table 1). The average PM10 mass in this study is consistent with those reported in Varanasi (168 ± 73 μg/m3) [44] and Kanpur (166.25 ± 58.00 μg/m3) [45,46]. The PM10 concentration in Patna during sampling time showed a lower concentration than in Pakistan (Karachi), with mean concentrations of 437 µg/m3 [47] and Lahore (406 µg/m3) [47]. However, PM10 concentration in this study was higher than in some of the Indian cities like Ahmedabad (171 µg/m3) and Mumbai (128 µg/m3) [48,49]. A box and whisker plot showing monthly and seasonal concentration of PM10 in Patna during 2018 is shown in Figure 2. Winter season showed the highest PM10 concentration followed by autumn, summer, and rainy season, and ranged from 113 to 238 µg/m3 (median 185 µg/m3), 130 to 193 µg/m3 (median 175 µg/m3), 127 to 213 µg/m3 (median 157 µg/m3), and 90 to 172 µg/m3 (median 121 µg/m3), respectively. The highest level of PM10 measured in this study is consistent with those detected in most of the Indian cities (Table 1) [31,50]. The PM10 concentration in this study is consistent with that reported in Islamabad, Pakistan (177 µg/m3) [51]. PM10 concentration shows seasonal solid and monthly variation in Patna (Figure 2). The highest PM10 concentration was observed (208 μg/m3) in December, and the lowest concentration (106 μg/m3) was observed in August. PM10 concentrations were low from June through September because of high temperatures, high wind speed, long periods of solar radiation, and mixing height, which favors air pollution dispersion. Furthermore, the scavenging of aerosol particles by rainfall under the influence of monsoon can lower the PM10 concentrations.

3.2. Concentration and Seasonal Variation of WSIIs

Water-soluble inorganic species (mass concentrations of cations and anions) comprise a large part of aerosol particles and play an important role in the atmosphere. In this study, the WSIIs ranged from 6.17 to 216 μg/m3 (median 26.6 μg/m3). The sum total of WSIIs in PM10 (26.6 µg/m3) measured in this study is consistent with WSIIs reported in some of the Indian cities (Table 2) [31,52,56,61]. The monthly and seasonal variation of WSIIs measured in PM10 in this study is shown in Figure 3. Winter season had relatively higher levels of WSIIs in PM10, followed by autumn, summer, and rainy seasons. The chemical profile of the WSIIs in PM10 is shown in Figure 4. Among the WSIIs, SO42− was the most abundant chemical among WSIIs and accounted for 46% of total WSIIs and 9.20% of PM10 mass. Next to SO42−, NH4+ was the second highest chemical among WSIIs, followed by NO3− and Na+, and these accounted for 23%, 13%, and 5% of total WSIIs, respectively.

3.3. Relationship between PM and WSIIs

The interrelationship among WSIIs constituents of PM10 was tested using the SPSS software version 21 and is illustrated in Table 3. The strongly correlated WSIIs suggest identical sources of emission, while poorly correlated WSIIs specify different sources. The results showed that most WSIIs were positively linked to each other. K+ and SO42− were positively linked to each other (r = 0.378, p < 0.05) in PM10, indicating a similar anthropogenic source, possibly from biomass burning; however, Mg2+ and SO42− show a poor correlation between them. Similarly, NH4+ and K+ showed a significant correlation (r = 0.416, p < 0.05), indicating emission from wood-burning activities [62,63] because K+ is mainly emitted from wood burning due to cooking or heating purposes. Ca2+ and Mg2+ were positively correlated (r = 0.213, p < 0.05), suggesting their emission from natural soil dust, especially in PM10 (Table 3). A moderate correlation between NO3− and SO42− (r = 0.494, p < 0.05) indicated their similar sources from coal combustion. The ratios of NO3−/SO42− are commonly used to indicate the sources of these two ionic species. NO3−/SO42− ratios > 1 indicate the more significant contribution of NO3− through mobile or vehicular emission, while a NO3−/SO42− ratio < 1 suggests ample contribution of SO42− from industrial activity [64]. In this study, the NO3−/SO42− ratios ranged from 0 to 7.5 (1.2 ± 1.8) in the rainy season, indicating a mixture of mobile source and industrial emission. On the other hand, PM10 shows a ratio range from 0 to 3.6. The NO3−/SO42− ratios in summer ranged from 0 to 70.5 (4.3 ± 15.6), autumn from 0 to 4.2 (0.7 ± 1.4), and winter from 0.3 to 1.33 (0.82 ± 0.42) in PM10, respectively.

3.4. Source Apportionment Study

The source apportionment of PM10 in Patna was performed using multiple approaches, such as molecular diagnostic ratio (MDR) and principal component analysis (PCA), and these are discussed below.

3.4.1. Molecular Diagnostic Ratio (MDR)

The SO42−/Na+ ratio < 1 indicates the dust storms, while >1 indicates the anthropogenic activities. In this study, SO42−/Na+ ratios in PM10 were high in the month of October (22.7), November (24.9), and December (18.3) (Table 4), suggesting anthropogenic source. A Cl−/Na+ ratio < 10 indicates the source of biomass and coal combustion burning. Cl−/Na+ ratios were observed to be predominantly low during November (1.06) and December (1.12). A K+/Na+ ratio < 10 indicates the source of dust, and >10 indicates the burning of crop residues. In this study, K+/Na+ ratios were observed in October (1.25) and December (1.04). Ca2+/Na+ and Mg+2/Na+ ratios indicate the source of dust. Dust storms tremendously increase the Ca2+ concentration in the atmosphere [65,66]. In this study, Ca+2/Na+ and Mg+2/Na+ were observed during October (3.34), November (2.52), and December (1.71); and August (1.30) and November (0.61), respectively.

3.4.2. Principal Component Analysis (PCA)

PCA was performed on the WSIIs dataset separately in each season to investigate the sources of PM10. The results of PCA analysis irrespective of seasons are presented in Table 5. In winter, two principal components (PCs) were extracted with an eigenvalue greater than one and a cumulative variance of 75.65%. PC 1 accounted for 58.96% of the variation in WSIIs data and was positively loaded with Na+ (0.845), K+ (0.728), Ca+ (0.897), Cl− (0.869), SO42− (0.515), and NH4+ (0.839). K+ is an indicator of biomass burning. A high contribution of SO42−, NH4+, and Na+ are identified as secondary aerosol formation. High SO42− and NH4+ are also released from an industrial source, such as a brick kiln [57,58,59]. PC 2 comprised 16.69% of the variance in data and was highly loaded with K (0.534), Mg (0.920), and NO3− (0.842). NO3− is associated with vehicle emissions, while high Mg is linked with soil and road dust [39].
In the summer, three PCs were extracted with an eigenvalue greater than 1, with a cumulative variance of 73.88%. PC 1 accounted for 36.87% and was highly loaded with Na+ (0.872), SO42− (0.525), and NH4+ (0.903). SO4 and NH4 in the atmosphere are generally assumed to be secondary fine particles produced from gas to particle conversion of NH3 and SO2; hence, this source can be identified as the secondary formation [67,68,69]. PC 2 explored 19.97% of the total variance in data and was positively loaded with Ca (0.838) and NO3− (0.907). PC 3 explained 17.05% data variation and was highly loaded with K (0.663) and Cl− (0.857). Cl− is believed to be associated with multiple sources, such as coal burning, biomass burning, and sea salts [70].
In the rainy season, three PCs were extracted with an eigenvalue greater than 1, with a cumulative variance of 70.80%. PC 1 contained 36.27% of the total variance and was loaded with Ca2+ (0.663), Mg2+ (0.829), and K+ (0.894). High loading of Ca, Mg, and K demonstrate contribution from marine aerosols [71,72]. PC 2 accounted for 17.67% of data variance, with favorable loading on Na (0.747), K (0.528), and NO3 (0.701). PC 3 explained 16.86% of data variance and was highly loaded with SO42− (0.849) and NH4 (0.685), suggesting secondary formation. SO42− can also result from vehicular emission.
In autumn, only two main PCs were extracted with an eigenvalue greater than one and a cumulative variance of 76.96%. PC 1 accounted for 58.81% of data variation and was positively linked with Na+ (0.942), K (0.795), Ca (0.527), Cl− (0.817), SO42− (0.677), and NO3− (0.762). The dominance of Cl− and NO3− indicates secondary formation. The reaction of NOx with hydroxyl radicals leads to the formation of nitrate. PC 2 comprised 18.15% of data variation and was highly loaded with K+ (0.533), Mg2+ (0.815), and SO42− (0.715).
Overall, the principal causes of air pollution in Patna, according to the PCA analysis, are mostly emissions from mixed sources such street dust, coal combustion, biomass burning, vehicular emissions, and industrial emissions such as brick kiln and secondary aerosol formations. This finding is consistent with a previous study that identified similar sources in PM2.5 from Patna [39].

3.5. Backward Air Mass Trajectory

In this study, a five-day air mass back trajectory analysis was performed to examine the influence of air mass originating from a nearby or remote area on aerosol composition. The trajectories were calculated for an air mass arriving at our study site at 500 m above ground level, as shown in Figure 5. The back trajectory analysis indicates that the air mass in Patna city primarily originated from marine sources and was transported from the Arabian Sea, Bay of Bengal, Gujarat, Afghanistan, Pakistan, and Bangladesh. Therefore, the influence of air mass from both land and sea may have contributed to this study’s high levels of PM10.

4. Conclusions

The contamination and seasonal distribution of PM10 in ambient air were investigated to understand the pollution level in Patna. The results indicated severe contamination of PM10 in the air, exceeding the annual recommended limit of WHO and NAAQS India. The annual average PM10 concentration in Patna was slightly higher than some of India’s megacities but was consistent with class-II cities. The winter season had a relatively higher level of PM10 than the rest of the seasons. SO4 was identified as the most prominent compound among WSIIs. The outcome of the principal component analysis indicated that the combined sources of PM pollution in Patna include vehicle emissions, industrial emissions, street dust, burning of biomass, and coal combustion. The back trajectory analysis suggests that the air mass at Patna city mainly originated from marine settings and was transported from the Arabian Sea and Bay of Bengal, Gujarat, Afghanistan, Pakistan, and Bangladesh. The results obtained in this study can be used to prepare PM10 profiling of the IGP. As the urban quality of Indian cities, including Patna, is deteriorating, continuous air quality monitoring of PM is warranted.

Author Contributions

N.L.D.: Conceptualization, methodology, and formal analysis. I.C.Y.: Original draft preparation, writing, reviewing and editing. A.K.: data acquisition and analysis. All authors have read and agreed to the published version of the manuscript.

Funding

The authors gratefully acknowledge the financial support for this work provided by the Science and Engineering Research Board, Department of Science and Technology (EMR/2016/000052).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.

Conflicts of Interest

The authors declare no conflicts of interest.

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Atmosphere 15 00878 g001
Figure 1. Map of India showing Patna and sampling site adapted from Devi et al. [40].
Figure 1. Map of India showing Patna and sampling site adapted from Devi et al. [40].
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Figure 2. Box and whisker plot showing seasonal (top) and monthly (bottom) variation of PM10 in Patna during 2018. The central box represents the concentration from 25th to 75th percentile. The middle bold line represents the median value. The asterisk/stars are extreme outliers that are >3-times beyond the interquartile range.
Figure 2. Box and whisker plot showing seasonal (top) and monthly (bottom) variation of PM10 in Patna during 2018. The central box represents the concentration from 25th to 75th percentile. The middle bold line represents the median value. The asterisk/stars are extreme outliers that are >3-times beyond the interquartile range.
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Figure 3. Box and whisker plot showing monthly and seasonal variation of WSIIs in PM10 from Patna The central box represents the concentration from 25th to 75th percentile. The middle bold line represents the median value. The asterisk/stars are extreme outliers that are >3-times beyond the interquartile range. The concentration on the Y-axis is in logarithmic scale.
Figure 3. Box and whisker plot showing monthly and seasonal variation of WSIIs in PM10 from Patna The central box represents the concentration from 25th to 75th percentile. The middle bold line represents the median value. The asterisk/stars are extreme outliers that are >3-times beyond the interquartile range. The concentration on the Y-axis is in logarithmic scale.
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Figure 4. Relative abundance of WSIIs in PM10 samples from Patna.
Figure 4. Relative abundance of WSIIs in PM10 samples from Patna.
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Figure 5. Five-day air mass back trajectories arriving at Patna.
Figure 5. Five-day air mass back trajectories arriving at Patna.
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Table 1. Comparison of PM10 concentration (µg/m3) in this study with previous studies reported worldwide.
Table 1. Comparison of PM10 concentration (µg/m3) in this study with previous studies reported worldwide.
Sampling LocationSampling
Periods		WinterSummerRainyAutumnReferences
PatnaJanuary 2018–December 2018185157121175This study
AhmedabadDecember 2006–January 2007171---[48]
Brahmaputra VelleyDecember 2010–October 201496.152.622.145.3[52]
ChennaiOctoer 2011–May 2012170150158-[53]
DelhiJanuary 2010–December 2011241193140-[54]
HissarDecember 2004169---[31]
Prayagraj254---
KanpurOctober 2008---243[55]
Manali, IndiaNovember 2014–May 2016131107103115[56]
PatnaJanuary–December 201510998.36364.1[57]
VaranasiApril–July 2011-210131-[44]
DelhiDecember 2008–November 2009182211140-[58]
IslamabadJanuary–February 2016177---[51]
Hong KongNovember2000–February 200178.9---[59]
GuangzhouAugust–September 2004--138-[60]
Table 2. Comparison of WSIIs (µg/m3) in PM10 measured in this study with previous studies reported worldwide.
Table 2. Comparison of WSIIs (µg/m3) in PM10 measured in this study with previous studies reported worldwide.
LocationsSampling YearSeasonK+Na+Ca+2Mg+2SO4−2NO3−NH4+Cl−References
PatnaJanuary–December 2018Winter1.312.131.950.3620.5818.5110.391.01This study
Summer1.071.601.140.196.553.204.300.52
Rainy 0.971.210.840.5310.551.384.650.29
Autumn0.910.822.250.2417.397.029.210.30
KanpurApril–July 2011Summer----6.544.974.112.68[61]
Brahmaputra ValleyDecember–October 2014Winter2.12.140.660.095.022.142.201.10[52]
Summer1.271.970.860.133.051.101.021.21
Rainy0.661.470.380.051.150.490.430.84
Autumn0.961.590.570.081.680.650.611.12
AhemadabadDecember–January 2007Winter1.40.946.10.313.87.23.70.5[48]
HisarDecember 2004Winter2.60.733.50.3612.3314.566.60.4[31]
Allahabad2.40.665.70.617.669.266.230.16
ManaliNovember 2014–May 2016Winter2.705.663.291.796.075.094.54.41[56]
Summer2.513.563.071.265.943.243.513.44
Rainy2.392.832.231.024.344.163.713.32
Autumn2.934.382.321.275.565.274.243.53
DelhiJanuary 2010–December 2011Winter1.72.34.80.711.614.19.65.0[54]
Summer1.53.34.70.69.25.12.63.5
Autumn1.74.25.20.68.84.92.52.5
Chennai, IndiaOctober–November 2011
April–May 2012		Winter1.703.853.900.1910.575.926.543.59[53]
Summer2.075.984.990.2512.134.232.783.65
Rainy0.873.223.350.339.965.907.533.18
Varanasi, IndiaApril–July 2011Summer2.352.23.60.555.34.51.211.8[26]
Rainy1.152.72.80.454.32.60.72.4
Table 3. Spearman’s rank correlation coefficient analysis of WSIIs in PM10.
Table 3. Spearman’s rank correlation coefficient analysis of WSIIs in PM10.
PM10NaKCaMgClSO4NO3
Na0.11
K0.050.538 **
Ca0.31 **0.486 **0.412 **
Mg−0.140.120.332 **0.21
Cl0.220.422 **0.599 **0.414 **0.03
SO40.35 **0.335 **0.378 **0.378 **0.190.597 **
NO30.299 **0.382 **0.584 **0.417 **0.279 *0.599 **0.494 **
NH40.100.523 **0.416 **0.428 **0.060.536 **0.467 **0.458 **
** Correlation is significant at the 0.01 level (2-tailed); * Correlation is significant at the 0.05 level (2-tailed).
Table 4. Molecular diagnostic ratios of WSIIs utilized for source apportionment in Patna.
Table 4. Molecular diagnostic ratios of WSIIs utilized for source apportionment in Patna.
MonthsCl−/Mg2+SO42−/Mg2+Na+/Mg2+K+/Mg2+Ca2+/Mg2+Cl−/Na+K+/Na+Mg2+/Na+Ca2+/Na+SO42−/Na+SO4−2/K+NO3−/SO4−2Cl−/K+Na+/Ca2+K+/Ca2+Mg2+/Ca2+Cl−/Ca2+NO3−/Ca2+SO42−/Ca2+
January2.5341.35.913.953.670.430.670.170.626.9810.40.850.641.611.070.270.699.5611.2
February2.8342.35.233.693.850.540.710.190.748.0811.40.960.771.360.960.260.7410.510.9
March1.4621.35.983.715.610.240.620.170.943.585.770.590.391.070.660.180.262.243.81
April1.6329.16.173.514.540.260.570.160.744.738.300.250.461.360.770.220.361.636.42
May4.0051.810.78.315.690.370.770.090.534.826.230.300.481.891.460.180.702.749.11
June0.6512.11.771.711.370.370.970.560.776.837.070.210.381.301.250.730.481.838.85
July1.4621.43.843.782.430.380.990.260.635.585.660.150.391.581.560.410.601.368.81
August0.265.710.770.730.980.330.941.301.287.427.860.130.350.780.741.020.260.745.81
September0.4523.61.691.641.580.270.970.590.9414.014.40.050.281.071.040.630.290.7214.9
October0.9562.82.773.459.270.341.250.363.3422.618.10.400.280.300.370.110.102.726.77
November1.7441.01.651.634.151.060.990.612.5224.925.10.501.070.400.390.240.424.939.87
December5.1283.84.584.777.811.121.040.221.7118.317.50.781.070.590.610.130.668.4010.7
Table 5. Principal component analysis (PCA) of WSIIs in PM10 from Patna.
Table 5. Principal component analysis (PCA) of WSIIs in PM10 from Patna.
WSIIsWinterSummerRainyAutumn
PC1PC2PC1PC2PC3PC1PC2PC3PC1PC3
Na+0.8450.0020.8720.1790.2510.3670.7470.3370.942−0.141
K+0.7280.5340.267−0.0990.6630.6630.5280.1850.7950.533
Ca2+0.8970.0380.2060.8380.0180.8290.0520.1410.5270.186
Mg2+0.0090.9200.4530.710−0.0060.894−0.004−0.0900.3170.815
Cl−0.8690.358−0.2150.1030.857−0.406−0.6230.2610.8170.131
SO42−0.5150.3050.5250.3580.426−0.1360.2210.8490.6770.715
NO3−0.3480.842−0.1710.9070.039−0.1940.700−0.0370.7620.475
NH4+0.8390.3070.903−0.004−0.1910.320−0.2820.6850.072−0.888
Eigenvalue4.7171.3362.9501.5971.3642.9021.4141.3494.7051.452
Variance (%)58.9616.6936.8719.9617.0536.2717.6716.8658.8118.15
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Devi, N.L.; Chandra Yadav, I.; Kumar, A. Estimation of Particulate Matter (PM10) Over Middle Indo-Gangetic Plain (Patna) of India: Seasonal Variation and Source Apportionment. Atmosphere 2024, 15, 878. https://doi.org/10.3390/atmos15080878

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Devi NL, Chandra Yadav I, Kumar A. Estimation of Particulate Matter (PM10) Over Middle Indo-Gangetic Plain (Patna) of India: Seasonal Variation and Source Apportionment. Atmosphere. 2024; 15(8):878. https://doi.org/10.3390/atmos15080878

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Devi, Ningombam Linthoingambi, Ishwar Chandra Yadav, and Amrendra Kumar. 2024. "Estimation of Particulate Matter (PM10) Over Middle Indo-Gangetic Plain (Patna) of India: Seasonal Variation and Source Apportionment" Atmosphere 15, no. 8: 878. https://doi.org/10.3390/atmos15080878

APA Style

Devi, N. L., Chandra Yadav, I., & Kumar, A. (2024). Estimation of Particulate Matter (PM10) Over Middle Indo-Gangetic Plain (Patna) of India: Seasonal Variation and Source Apportionment. Atmosphere, 15(8), 878. https://doi.org/10.3390/atmos15080878

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