Back
 OJAP  Vol.9 No.1 , March 2020
Seasonal Variation of Potential Source Locations of Atmospheric Particulates over the Indo-Gangetic Plain of India
Abstract: Ambient particulate matter (PM2.5 and PM10) concentrations were measured during two different seasons (summer and winter) at three different locations of Gurugram which is located in the Indo-Gangetic plain of India. The ambient concentrations of both PM2.5 and PM10 were higher during winter season (PM2.5: 261 μg·m-3; PM10: 440 μg·m-3) when compared to summer period (PM2.5: 114 μg·m-3; PM10: 202 μg·m-3). Potential Source Contribution Function (PSCF) analysis suggests significant seasonal variation in potential contributing locations of ambient PM2.5 over the study area. The PSCF analysis suggests that cross country transport of PM2.5 from Pakistan and Afghanistan significantly attributed to higher concentrations of PM2.5 at the study locations; whereas, PM2.5 emitted from locations in the south-western direction of the study sites attributed to the ambient PM2.5 concentrations at the study site during summer seasons. Further study is required to measure percentage contribution from different sectors and locations to the ambient particulate concentrations at the study site to develop sector specific mitigation plan.

1. Introduction

Rapid urbanisation and industrialization have deteriorated the quality of air in many regions around the world. Atmospheric particulate matter (PM) comprises of particles emitted from both natural sources and anthropogenic activities. These particles act as the major deterministic factor of the quality of ambient air. They are important component of the atmosphere with direct or indirect impact on the climate [1]. An airborne particle ranges from few nm (10−9 m) to few hundred µm in diameter. Particulate matter mainly comprises of PM10 (coarse size fraction: <10 µm) and PM2.5 (fine size fraction: <2.5 µm). Sources of PM include both primary emission and chemical transformation of precursor gases producing secondary particles contributed from industries, power plants, automobiles and other combustion activities.

Atmospheric particles play crucial role in radiation balance of the Earth’s atmosphere by scattering and/or absorbing the incoming solar radiation and outgoing long wave radiation. The scattering and absorption coefficient of particle depends on the physiochemical property of the particle. Following this coefficient, atmospheric particles produce regional haze, discoloration, loss of texture and visibility at a particular region [2]. The conditional aspects of temperature, low wind speeds and low mixing heights during winter favor the formation of secondary atmospheric particles which together with the primary particles increase the concentration of PM and reduce the visibility [3].

In addition to its effects on the radiation balance of the atmosphere, PM2.5 severely affects human health by penetrating deep into the respiratory system depending on their diameter and leads to pulmonary disorder, cardiac arrest, brain stroke etc. [4]. Combining welfare costs and costs of lost labour due to air pollution puts India’s GDP loss at more than 8.5% ($560 Billion) in 2013 [5]. Long distance transport of atmospheric pollutants also attributes to the atmospheric PM concentration of a particular region apart from the localised sources [6]. Misawa et al., [7] have reported that the trans-boundary movement of atmospheric particulates from the main Asian content increases the PM2.5 concentration in the west coast of Japan. Episodic elevated levels of sulphate concentration in the western part of US during spring season were attributed to the emission in the southeast Asia and trans-pacific movement of atmospheric particulates [8]. Southeast Asia region was reported as the major contributor of black carbon and holds responsible for formation of “Arctic Haze” [9]. Study has attributed high concentration of PM10 at Seoul Korea to eastern region of China [10]. Air mass back trajectories analysis has recently reported higher atmospheric concentration of PM2.5 during winter in the Shijiazhuang region of China has potential sources from Beijing-Tianjin region, Shandong Province, northern Russia and northwestern Mongolia [11]. High ambient level of PM and temporal variation atmospheric particulates in the Southern European belt, particularly in Mediterranean basin, was attributed to long range transport of air mass from North Africa [12].

In India, along with rapid urbanisation and industrialization, uncontrolled biomass burning, poor management of surface soil, unpaved road etc. have resulted in alarming increase of atmospheric PM particularly in the northern and northwestern region of the country [13]. Higher concentration of atmospheric particulate over the Indo-Gangetic plain (IGP) region during winter affects the visibility [14]. Research on Aerosol Optical Depth (AOD) has identified the IGP as hot spots for anthropogenic aerosols in South-Asia [15]. Large spatio-temporal variation in the climate was observed during the summer to winter months in the IGP region [16]. Mean ambient concentrations of PM10 and PM2.5 during the winter season (October-March) in IGP region were recorded in the range of 238 - 548 µg/m3 and 236 - 389 µg/m3 respectively [17]. Nine Indian cities (of which eight are in the IGP region) are listed in the top twenty high ambient PM2.5 concentration cities of the world [5]. Significant increment in the fine fraction of the PM during the winter season in the IGP region, mostly arises due to unfavourable meteorological conditions which result into transport of pollutants eastwards in the Indian subcontinent [18].

The mean residence time of PM (1 to 10 μm) in the atmosphere is between 10 - 100 hrs when there is no precipitation. However, it is difficult to establish a clear relationship between the emission sources and its impact on atmospheric PM concentration under unfavourable meteorological conditions as in India. Since identification of emission sources of a particular geographical region is of primary concern and circumstances arises when it becomes difficult to draw conclusion from regional sources contributing to air pollution. Therefore inversion technique approach is undertaken to apportion the emission sources. Potential Source Contributing Function (PSCF) is one of the inversion atmospheric modelling techniques based on backward wind trajectory assists in apportioning the sources contributing to a specific geophysical location [19]. Such models are mostly utilised for air quality management study at spatial scale ranging from metropolitan region to widely distributed continental location. Based on the principle of conditional portability, PSCF model shows the probability field describing the potential source strength of a particular geographical area (i.e. the grid cells) owing to likely source contribution. Studies have also suggested regarding the accuracy of backward wind trajectory is dependent on available meteorological data like humidity, wind speed, wind direction etc.

Results from PSCF analysis of PM10 concentration during different seasons throughout the year in Agra city have shown the aerosol transport pathways are mainly from urbanised areas and cities in north westerly direction of the city [20]. In India, there are drawbacks in establishing clear relationship between the emitting sources and its impact on ambient PM2.5 concentration. During the present study an effort was made to establish the potential sources contributing to the ambient PM2.5 level in the city of Gurugram which is located at the southwest corner of Delhi, within the IGP.

2. Methodology

2.1. Site Description

Gurugram is one of the major business, industrial and technology hub of India located in the state of Haryana. It is located at 28˚27'22"N and 77˚1'44"E Southwest of Delhi and is within the National Capital Region (NCR) of India (Figure 1). The Gurugram is leading Indian automobile manufacture industry. There are

Note: Site 1 is located at the eastern boarder of the city with much higher greenery and open spaces. Site 2 was located beside a busy motor way while site 3 was in a residential area near to a busy office complex.

Figure 1. Sampling locations of the present study.

about 500 different small, medium and large scale industries in and around the city. The population density of the city is 1187 per sq Km. The city is typically hot and humid and annual average temperature varies with an average maximum temperature 43˚C in June while winters are cold and foggy with few sunny days, and with a December daytime average of 3˚C. There are four distinct seasons—spring (February-March) summer (April-August), autumn (September-October) and winter (November-January) along with monsoon season. The western disturbance brings some rain in winter that further adds to the chill. Spring and autumn are mild and pleasant seasons with low humidity.

Ambient air samples were collected from three different locations in the Gurugram. Site 1 is located at the eastern boarder of the city with much higher greenery and open spaces than other two. Site 2 and 3 are located at the heart of the city. Site 2 was located beside a busy motor way while site 3 was in a residential area near to a busy office complex (Figure 1).

2.2. Data Collection

Ambient air samples were collected at all three locations throughout summer (April-May) and winter (November-December) seasons of 2017.

Fine particulate Samplers (APM 550, Envirotech, India) were used for monitoring of ambient PM10 and PM2.5. 24-h samples were collected in a quartz filter paper to measure the PM10 and PM2.5 concentrations using the gravimetric method. The APM 550 system is a manual method for sampling fine particles (PM2.5 fraction) and is based on impactor designs standardized by USEPA for ambient air quality monitoring. Ambient air enters the sampler unit through an omni-directional inlet designed to provide a clean aerodynamic cut-point for particles greater than 10 microns whereas particles in the air stream finer than 10 microns proceed to a second impactor that has an aerodynamic cut-point at 2.5 microns. The air sample and fine particulates exiting from the PM2.5 impactor are passed through a 47 mm diameter quartz filter membrane that retains the fine particulate matter. The sampling rate of the system is held constant at 1 m3/hr by a suitable critical orifice while a standard system is supplied with a Dry Gas Meter to provide a direct measure of the total air volume sampled. Meteorological parameters (ambient temperature, humidity, rainfall, wind speed and wind direction) were also recorded during the period of study using automated weather stations (AWS).

Analysis of Variance (ANOVA) was performed with the collected dataset of PM10 and PM2.5 using SPSS24. ANOVA values were used to calculate the Fisher’s Least Significant Difference (LSD) test at p < 0.05.

2.3. Air Parcel Back Trajectories and PSCF Analysis

Air parcel backward trajectories simulation was conducted using the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) 4.0 Model developed by NOAA/ARL. Five days backward trajectory analysis was conducted on each sampling day at an altitude of 500 m above ground level (AGL) at an interval of 4 hrs (i.e. 06, 10, 14, 18 and 22 UTC) to compute the potential source location of long range transport of PM from different regions. The calculated backward trajectories were applied in performing a PSCF analysis of PM. All hourly end points from the back-ward trajectories were classified into 1˚ × 1˚ latitude and longitude grid cells. There are number of various methods for analysing back trajectory statistics, popular ones including—Concentration Weighted Trajectory (CWT) and Potential Source Contribution Function (PSCF). The PSCF values for the grid cells were calculated by counting the number of trajectory endpoints that terminated within each cell by the following equation [21],

P S C F i j = m i j / n i j

where P S C F i j is the conditional probability value grid cell ij, m i j is the number of endpoints for the same cell corresponding to the PM particles concentrations higher than a threshold criterion value and n i j is the total number of endpoints in the grid cell. To reduce the effect of small values of n i j , the PSCF values were multiplied by a weighted average function [22].

3. Results and Discussion

3.1. Seasonal Variation of PM10 and PM2.5 Concentration

The concentrations of PM10 and PM2.5 show significant variation in daily average concentration during summer and winter seasons of the study. The ambient PM10 concentrations were recorded in the range of 128 to 267 µg/m3 with an average of 202 µg/m3 during summer; while it was recorded in the range of 232 to 671 µg/m3 with an average of 440 µg/m3 during the winter season (Table 1). On the other side, ambient PM2.5 concentration was recorded in the range of 80 to 157 µg/m3 with an average of 114 µg/m3 during summer and that during the winter was recorded as 261 µg/m3 (Table 1). Although the ambient concentrations of both PM10 and PM2.5 were higher than their respective National Ambient Air Quality Standards (PM10: 60 µg/m3; PM2.5: 40 µg/m3) throughout the study period, the concentrations of both PM10 and PM2.5 were remained significantly higher during the winter months compared to summer (Table 1). Comparatively higher ambient concentrations of particulate matter during the winter season over the study area might be attributed to lowering of the boundary layer and development of stable atmospheric condition. Tiwari et al. [23] reported that the higher concentration of ambient particulate matter during winter season was attributed to low temperature and lesser wind speed in the study area compared to the surroundings. Additionally, earlier study has reported that the post-harvest crop residue burning in nearby agricultural lands also contributes to the ambient PM2.5 concentration at the study area during the monitoring period [24].

Among three study sites the concentration of both PM10 and PM2.5 were remained significantly higher at site 2 throughout the study period (Table 1). Site 2 was located near a busy motor way. Significantly higher ambient concentration of both PM10 and PM2.5 at site 2 might be associated with comparatively higher traffic concentration at the site than others. Additionally, nearby commercial vehicle parking might also have contributed to higher concentration of ambient particulates in the area.

3.2. PSCF Analysis

Present study indicates that the contributions across the international boundary to the ambient PM2.5 of the study area during the winter months were higher than that of the summer month (Figure 2), although the potential area of contribution varied during two different seasons. Prevailing wind trajectories during the winter season suggests potential source regions of ambient PM2.5 concentrations at the monitoring sites were located towards the north-west direction in the north-east Pakistan and central Afghanistan (Figure 2). However, during the

Table 1. Seasonal variation of PM2.5 and PM10.

Mean of daily observations (n = 60); Values in the parenthesis indicates ±SE; In a column mean followed by a column letter is not significantly different by LSD test (p < 0.05).

Figure 2. PSCF analysis of PM2.5 at the sampling location during (a) summer and (b) winter 2017.

summer season potential source regions of ambient PM2.5 concentration at the monitoring sites were located at Rajasthan and Gujarat areas (Figure 2). Earlier studies have linked the ambient particulate matter concentration over the study area during summer to dust storms in Rajasthan and Katch area [25]. Kulshrestha et al., [26] have also attributed the aerosol load in the study area during summers to Thar desert and Middle East Asia regions.

4. Conclusion

The study has demonstrated that the average ambient PM2.5 and PM10 concentrations were higher during both winter and summer seasons than the NAAQ standards. However, the ambient concentrations of both PM2.5 and PM10 were significantly higher during the winter seasons compared to summer. The present study also suggests, although there was influence of local sources on the ambient concentrations of PM2.5 and PM10, but long distance across the international boundary sources played a significant role during the winter season. During summer, dusts from Rajasthan and Gujarat also played potential role to increase the ambient concentrations of PM2.5 at the sampling sites. However, source apportionment study is required to identify the specific sectorial contributions to the ambient concentrations of pollutants.

Acknowledgements

Authors are thankful to the administration of The Energy and Resources Institute, New Delhi for providing funding support for this publication.

Cite this paper: Rahman, M. , Sharma, V. , Kundu, S. and Datta, A. (2020) Seasonal Variation of Potential Source Locations of Atmospheric Particulates over the Indo-Gangetic Plain of India. Open Journal of Air Pollution, 9, 1-10. doi: 10.4236/ojap.2020.91001.
References

[1]   Kaufman, Y.J., Boucher, O., Tanré, D., Chin, M., Remer, L.A. and Takemura, T. (2005) Aerosol Anthropogenic Component Estimated from Satellite Data. Geophysical Research Letters, 32.
https://doi.org/10.1029/2005GL023125

[2]   Malm, W.C. and Day, D.E. (2000) Optical Properties of Aerosols at Grand Canyon National Park. Atmospheric Environment, 34, 3373-3391.
https://doi.org/10.1016/S1352-2310(00)00108-4

[3]   Massie, S.T., Torres, O. and Smith, S.J. (2004) Total Ozone Mapping Spectrometer (TOMS) Observations of Increases in Asian Aerosol in Winter from 1979 to 2000. Journal of Geophysical Research: Atmospheres, 109.
https://doi.org/10.1029/2004JD004620

[4]   Pope, C.A., Burnett, R.T., Thun, M.J., Calle, E.E., Krewski, D., Ito, K. and Thurston, G.D. (2002) Lung Cancer, Cardiopulmonary Mortality, and Long-Term Exposure to fine Particulate Air Pollution. The Journal of the American Medical Association, 287, 1132-1141.
https://doi.org/10.1001/jama.287.9.1132

[5]   World Health Organization (2016) Ambient Air Pollution: A Global Assessment of Exposure and Burden of Disease.

[6]   Grivas, G., Chaloulakou, A. and Kassomenos, P. (2008) An Overview of the PM10 Pollution Problem, in the Metropolitan Area of Athens, Greece. Assessment of Controlling Factors and Potential Impact of Long Range Transport. Science of the Total Environment, 389, 165-177.
https://doi.org/10.1016/j.scitotenv.2007.08.048

[7]   Misawa, K., Yoshino, A., Takami, A., Kojima, T., Tatsuta, S., Taniguchi, Y. and Hatakeyama, S. (2017) Continuous Observation of the Mass and Chemical Composition of PM2.5 Using an Automatic Analyzer in Kumamoto, Japan. Aerosol and Air Quality Research, 17, 444-452.
https://doi.org/10.4209/aaqr.2016.07.0290

[8]   Jaffe, D., Bertschi, I., Jaeglé, L., Novelli, P., Reid, J.S., Tanimoto, H., Westphal, D.L., et al. (2004) Long-Range Transport of Siberian Biomass Burning Emissions and Impact on Surface Ozone in Western North America. Geophysical Research Letters, 31.
https://doi.org/10.1029/2004GL020093

[9]   Koch, D., Bond, T.C., Streets, D. and Unger, N. (2007) Linking Future Aerosol Radiative Forcing to Shifts in Source Activities. Geophysical Research Letters, 34.
https://doi.org/10.1029/2006GL028360

[10]   Oh, H.R., Ho, C.H., Kim, J., Chen, D., Lee, S., Choi, Y.S., Song, C.K., et al. (2015) Long-Range Transport of Air Pollutants Originating in China: A Possible Major Cause of Multi-Day High-PM10 Episodes during Cold Season in Seoul, Korea. Atmospheric Environment, 109, 23-30.
https://doi.org/10.1016/j.atmosenv.2015.03.005

[11]   Chen, F., Zhang, X., Zhu, X., Zhang, H., Gao, J. and Hopke, P.K. (2017) Chemical Characteristics of PM2.5 during a 2016 Winter Haze Episode in Shijiazhuang, China. Aerosol and Air Quality Research, 17, 368-380.
https://doi.org/10.4209/aaqr.2016.06.0274

[12]   CAFE (2004) Clean Air for Europe (CAFE) Working Group on Particulate Matter, Second Position Paper on Particulate Matter. Final Draft.

[13]   Pant, P., Shukla, A., Kohl, S.D., Chow, J.C., Watson, J.G. and Harrison, R.M. (2015) Characterization of Ambient PM2.5 at a Pollution Hotspot in New Delhi, India and Inference of Sources. Atmospheric Environment, 109, 178-189.
https://doi.org/10.1016/j.atmosenv.2015.02.074

[14]   Kulshrestha, U.C., Raman, R.S., Kulshrestha, M.J., Rao, T.N. and Hazarika, P.J. (2009) Secondary Aerosol Formation and Identification of Regional Source Locations by PSCF Analysis in the Indo-Gangetic Region of India. Journal of Atmospheric Chemistry, 63, 33-47.
https://doi.org/10.1007/s10874-010-9156-z

[15]   Choudhry, P., Misra, A. and Tripathi, S.N. (2012) Study of MODIS Derived AOD at Three Different Locations in the Indo Gangetic Plain: Kanpur, Gandhi College and Nainital. Annales Geophysicae, 30, 1479-1493.
https://doi.org/10.5194/angeo-30-1479-2012

[16]   Ram, K., Sarin, M.M., Sudheer, A.K. and Rengarajan, R. (2012) Carbonaceous and Secondary Inorganic Aerosols during Wintertime Fog and Haze over Urban Sites in the Indo-Gangetic Plain. Aerosol and Air Quality Research, 12, 359-370.
https://doi.org/10.4209/aaqr.2011.07.0105

[17]   Tiwari, S., Srivastava, A.K., Bisht, D.S., Bano, T., Singh, S., Behura, S., Padmanabhamurty, B., et al. (2009) Black Carbon and Chemical Characteristics of PM10 and PM2.5 at an Urban Site of North India. Journal of Atmospheric Chemistry, 62, 193-209.
https://doi.org/10.1007/s10874-010-9148-z

[18]   Singh, R.P. and Kaskaoutis, D.G. (2014) Crop Residue Burning: A Threat to South Asian Air Quality. Eos, Transactions American Geophysical Union, 95, 333-334.
https://doi.org/10.1002/2014EO370001

[19]   Zhang, F., Cheng, H.R., Wang, Z.W., Lv, X.P., Zhu, Z.M., Zhang, G. and Wang, X.M. (2014) Fine Particles (PM2.5) at a CAWNET Background Site in Central China: Chemical Compositions, Seasonal Variations and Regional Pollution Events. Atmospheric Environment, 86, 193-202.
https://doi.org/10.1016/j.atmosenv.2013.12.008

[20]   Gogikar, P. and Tyagi, B. (2016) Assessment of Particulate Matter Variation during 2011-2015 over a Tropical Station Agra, India. Atmospheric Environment, 147, 11-21.
https://doi.org/10.1016/j.atmosenv.2016.09.063

[21]   Han, Y.J., Holsen, T.M., Hopke, P.K. and Yi, S.M. (2005) Comparison between Back-Trajectory Based Modelling and Lagrangian backward Dispersion Modelling for Locating Sources of Reactive Gaseous Mercury. Environmental Science & Technology, 39, 1715-1723.
https://doi.org/10.1021/es0498540

[22]   Xu, X. and Akhtar, U. (2010) Identification of Potential Regional Sources of Atmospheric Total Gaseous Mercury in Windsor, Ontario, Canada Using Hybrid Receptor Modelling. Atmospheric Chemistry and Physics, 10, 7073-7083.
https://doi.org/10.5194/acp-10-7073-2010

[23]   Tiwari, S., Tiwari, S. and Singh, A.K. (2015) A Study of Outdoor and Indoor Exposure to Particulate Matters on Students of Banaras Hindu University and City Side over Varanasi, India. Earth Science India, 9, 79-99.

[24]   Pipal, A.S., Jan, R., Bisht, D.S., Srivastava, A.K., Tiwari, S. and Taneja, A. (2014) Day and Night Variability of Atmospheric Organic and Elemental Carbon during Winter of 2011-12 in Agra, India. Sustainable Environment Research, 24, 107-116.

[25]   Dipu, S., Prabha, T.V., Pandithurai, G., Dudhia, J., Pfister, G., Rajesh, K. and Goswami, B.N. (2013) Impact of Elevated Aerosol Layer on the Cloud Macrophysical Properties Prior to Monsoon Onset. Atmospheric Environment, 70, 454-467.
https://doi.org/10.1016/j.atmosenv.2012.12.036

[26]   Kulshrestha, A., Satsangi, P.G., Masih, J. and Taneja, A. (2009) Metal Concentration of PM2.5 and PM10 Particles and Seasonal Variations in Urban and Rural Environment of Agra, India. Science of the Total Environment, 407, 6196-6204.
https://doi.org/10.1016/j.scitotenv.2009.08.050

 
 
Top