Rama K. Krishna  , Abhilash S. Panicker, Aslam M. Yusuf, Beig G. Ullah

Indian Institute of Tropical Meteorology, Pune 411008, India


Received: April 27, 2018
Revised: July 12, 2018
Accepted: July 26, 2018

Download Citation: ||https://doi.org/10.4209/aaqr.2018.04.0128  

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Cite this article:

Krishna, R.K., Panicker, A.S., Yusuf, A.M. and Ullah, B.G. (2019). On the Contribution of Particulate Matter (PM2.5) to Direct Radiative Forcing over Two Urban Environments in India. Aerosol Air Qual. Res. 19: 399-410. https://doi.org/10.4209/aaqr.2018.04.0128


  • Measurements of Particulate matter (PM2.5) is carried out over two Indian regions.
  • Radiaitve forcing of PM2.5 is estimated over the regions.
  • Impact of albedo found to dominate the hygroscopic effect in forcing estimates.


Radiative forcing by particulate matter (PM2.5) has been estimated for a period of one year (January–December 2015) over Delhi and Pune (polluted urban metro cities in India). In situ observations of PM2.5 and black carbon (BC) over both the cities were obtained from the ground-based System of Air Quality Forecasting and Research (SAFAR) network of stations. Observations have shown that PM concentrations over Pune had a strong diurnal cycle as compared to Delhi in all the seasons. Also, comparisons of the mode values and seasonal frequency distributions (FDs) over Pune and Delhi showed that pollution levels over Delhi were consistently above National Ambient Air Quality Standards (NAAQS). The mean monthly PM2.5 values ranged from 61.5 to 162.9 over Delhi and from 17.4 to 74.05 over Pune. The BC mass contribution to PM2.5 was found to be 10% to 25% over Pune. However, the contribution of BC to PM2.5 was up to 35% over Delhi. Radiative forcing due to PM2.5 (PRF) over both the sites was estimated using the Optical Properties of Aerosols and Clouds (OPAC) model along with the Santa Barbara DISORT Atmospheric Radiative Transfer (SBDART) model. The PRF in the atmosphere was between +7.73 Wm–2 and +14.51 Wm–2 over Delhi and between +3.12 Wm–2 and +12.15 Wm–2 over Pune. Sensitivity experiments showed that the impact of the increase in the hygroscopicity of the aerosols on the PRF was overshadowed by the net changes in albedo.

Keywords: PM2.5; AOD; Albedo; Radiative forcing.


Atmospheric aerosols controls Earth’s climate by influencing the radiation budget at regional and global scales. Aerosols (particulate matter (PM)) can been classified as PM10, PM2.5, and PM1 based on their aerodynamic diameter. They directly influence the climate by scattering and absorbing the incoming solar radiation (Charlson and Schwartz, 1992; Tiwari et al., 2016). Aerosols also influence the climate indirectly by modifying the cloud microphysics (Twomey, 1974; Panicker et al., 2016). Direct aerosol radiative forcing generally is estimated in the shortwave region (0.3–3 µm) and is calculated at the surface and at the Top of the Atmosphere (TOA) to find the net effect in the atmosphere. While the aerosol forcing at the surface always shows a reduction in radiation, the forcing at TOA depends on aerosol species (positive for absorbing aerosols and negative for scattering species). The difference between TOA and surface forcing provides the net gain of radiation due to aerosols in the atmosphere, known as atmospheric aerosol forcing (Panicker and Lee, 2012; Panicker et al., 2018). Black carbon (BC) is one of the most important absorbing aerosol species in the atmosphere (which is a part of PM2.5), generated mainly from incomplete combustion of fossil fuels and biomass burning (Koelmans et al., 2006; Penner et al., 1993; Cooke and Wilson, 1996). Approximately 20% of BC is reported to be emitted from burning of biofuels, 40% from fossil fuels, and 40% from open biomass burning on a global basis (Ramanathan and Carmichael, 2008). To quantify the climatic effects of PM, it is necessary to determine their local dynamics. Since PM can have high spatial and temporal variability, it is necessary to characterize the PM properties at multiple sites (Lagudu et al., 2011; Kumar et al., 2012). Over South Asia, the extensive study of aerosol direct RF has been made using the single column as well as 3D models and field experiments to understand the climate implications (Ramanathan et al., 2005; Lau et al., 2006; Moorthy et al., 2009; Lawrence and Lelieveld, 2010; Bollasina et al., 2011; Vinoj et al., 2014). Experiments such as Indian Ocean Experiment (INDOEX) (Jayaraman et al., 1998; Satheesh et al., 1999) reported clear-sky aerosol surface forcing of about –29 Wm–2 over the tropical Indian Ocean. Several studies have been carried out on the radiative forcing of aerosols in regional scale over India (Pandithurai et al., 2004; Panicker et al., 2008; Babu et al., 2002; Tiwari et al., 2013b; Srikanth et al., 2007; Bibi et al., 2017a; etc.). However, most of the studies reported were based on a single point observation representing the whole area. SAFAR is a program intended to measure the air pollutants at micro-environmental level over important metro cities. Hence in this study, we are incorporating PM2.5 observations from rural/semi-urban and urban locations under SAFAR and the average forcing due to these different environments, which are a true representative of Pune and Delhi and are presented in this paper. The increase in emissions from vehicular and energy sectors are the main sources of growing ambient concentration levels of PM2.5 (Gupta et al., 2007). The major sources of PM10 and PM2.5 in Delhi were reported as windblown dust from unpaved roads, fuel combustion, biomass burning and long-range transport (Tiwari et al., 2009). PM concentrations over Pune have been sparsely reported by the Central Pollution Control Board (CPCB, 2010) of India. Long-term exposure to ambient PM2.5 has potential health risks including premature death (Brauer et al., 2012; Pope et al., 2002). From the above viewpoint, our present study is an attempt to utilize the PM2.5 and BC observed data over Delhi and Pune to estimate the PRF. A brief description of the study area and methodology are provided in Section 2. Detailed results on PRF are discussed in Section 3. Results are summarized in Section 4.

Site Description and Data

The sampling of PM was carried out in different SAFAR locations over Delhi (28.37°N, 77.12°E; altitude: 300 m) and Pune (18.5°N, 73.8°E; altitude: 550 m). Delhi is the National Capital Region of India with 25 million population, making it the second most populous city in India, and also it is one of the most polluted urban environments over the globe (Tiwari et al., 2013a). According to Lodhi et al. (2013), main sources of the particulate pollution over Delhi is contributed by anthropogenic emissions from the power plants, vehicles, industrial sector, seasonal agriculturalbiomass burning and dust storm events over the region. Delhi is characterized with a semi-arid type of climate with high summer-monsoon (July–September) rainfall and dry period during the pre-monsoon (March–June) and post-monsoon (October–November) seasons, while winter (December–February) is chilly and foggy (Tiwari et al., 2010; Srivastava et al., 2014b). Wind speeds in general are low (2–5 m s–1), blowing from southeast directions during monsoon to northwest during the rest of the year, influencing aerosol sources, types and transport pathways (Lodhi et al., 2013). Pune is a rapidly expanding metropolitan city with a population over 4 million. Pune receives annual rainfall at an average of 76 cm. Almost 80% of the annual rainfall takes place during monsoon (June–September). During post-monsoon/winter, winds are normally from east-northeastcrossing across the northeast and central India, and dispersion of particulate pollutants are comparatively less due to the low ventilation coefficients (Raju et al., 2014). The location map of measurement sites are depicted in Fig. 1.

Fig. 1. Observations site of Delhi and Pune.Fig
. 1. Observations site of Delhi and Pune.

The data used in this study are obtained from the SAFAR network for a period of one year (January–December 2015). SAFAR network consists of Air Quality Monitoring Stations (AQMS) and Automatic Weather Stations (AWS) set up across Delhi and Pune in different environments, which continuously monitors air pollutants and meteorological parameters. More details of the SAFAR network, air quality and weather monitoring setups used in this study are available elsewhere (http://safar.tropmet.res.in/). PM was continuously monitored using Beta Attenuation Monitor (BAM-1020; Met One Instruments, Inc., USA). The instrument allows the particles of concentration with a cut-off aerodynamic diameter of 2.5 µm and less and hence this set of equipment measures PM2.5 particulates only with a resolution of 0.1 µg m3 (Ali et al., 2013; Panicker et al., 2016). The present study uses the measurements of PM2.5 over eight different environments over Delhi and Pune. The main monitoring stations are placed at strategically selected locations by including conditions with vehicular transport, industrial pollution, biomass burning, residential burning etc. BC data was also obtained from the SAFAR network measured by an Aethalometer (Surendran et al., 2013). The calibration and correction methodology of Aethalometer are described elsewhere (Allen et al., 1999; Babich et al., 2000; Weingartner et al., 2003; Arnott et al., 2005; Corrigan et al., 2006). Apart from these visibility and relative humidity data obtained from India Meteorological Department (IMD), water vapor data sets from the Moderate Resolution Imaging Spectroradiometer (MODIS), ozone from Ozone Monitoring Instrument (OMI) satellites, Albedo data from MODIS-BRDF (Bidirectional Reflectance Distribution Function) multispectral wavelength (https://modis-land.gsfc.nasa.gov/brdf.html) over both the sites also were used in this study.


To estimate the PRF, the PM mass concentration has been bifurcated into water-soluble (WS) and water-insoluble (WIS) components according to prevailing mass concentration conditions as suggested by Hess et al. (1998) and Panicker et al. (2010). The WS and WIS mass concentrations were converted to number density and were used in the OPAC model to obtain aerosol optical properties such as Aerosol Optical Depth (AOD), singlescattering albedo (SSA) and asymmetric parameter (ASP) as explained in Hess et al. (1998).

OPAC model simulates the aerosol optical properties, such as AOD, SSA, and ASP at 60 different wavelengths, at 8 different relative humidity conditions for a given aerosol chemical composition. In this study, we have modeled the aerosol optical properties (AOD, SSA, and ASP) at different RH levels corresponding to the ambient RH values obtained from the India Meteorological Department (IMD) observations for the entire year 2015 for individual cities. Also, the scale heights, which were derived by taking the ratio between AOD and extinction coefficient (σ) as explained in Hayasaka et al. (2007), were adjusted in OPAC model. σ was derived using the visibility observations from IMD as suggested by Panicker et al.(2010). The WS and WIS combinations in OPAC were adjusted to tune OPAC derived AOD to match with the AOD obtained from authentic sun photometer (at 550 nm) observations (from Aerosol Robotic Network) over the sites (Fig. 2). It was adjusted with a ±5% error and hence the resultant aerosol optical properties which are calculated from water-soluble and insoluble PM are reasonable to simulate radiative fluxes and further to estimate radiative forcing.

Fig. 2. Comparison of measured AOD/SSA (AERONET) and OPAC derived AOD/SSA over (a) Delhi and (b) Pune region.Fig
. 2. Comparison of measured AOD/SSA (AERONET) and OPAC derived AOD/SSA over (a) Delhi and (b) Pune region.

SBDART model is facilitated with vertically inhomogeneous, non isothermal plane-parallel media, and is shown to be computationally efficient in reliably resolving the radiative transfer equation. The OPAC derived AOD, SSA, and ASP were used in the SBDART model (Ricchiazzi et al., 1998) to obtain the fluxes at the surface and TOA. Fluxes also were derived for “no aerosol” condition and difference in aerosol and no aerosol cases were taken to get PRF. We also used the MODIS-BRDF albedo, water vapor and ozone (from OMI) data over both the sites along with aerosol data sets in SBDART. The default tropical aerosol profile and tropical model profile of temperature and humidity (McClatchey et al., 1972) also were used for estimating PRF. The atmospheric heating rate is calculated for each month using the following equation (Pandithurai et al., 2004; Panicker et al., 2014; Srivastava et al., 2014a; Bibi et al., 2017b):

where ∂T/∂t is the atmospheric heating rate in K day–1, g is the acceleration due to gravity (9.8 m s–2), cp is the specific heat capacity of air at constant pressure (i.e., 1006 Jkg–1 K–1) and ΔP (300 hPa) is the atmospheric pressure difference between top and bottom layers of the atmosphere where most aerosols are present which contribute to local heating (Pathak et al., 2010). 


Temporal Variability of PM2.5

The monthly variations of PM2.5 have been analyzed using the in-situ measurements from SAFAR network as explained in Section 2. Fig. 3 depicts the monthly variations of PM2.5 during 2015. The mean PM2.5 concentrations were calculated by averaging the observations of eight monitoring stations over Delhi and Pune. The PM2.5 concentrations had ranged between 61.5 µg m3 to 162.95 µg m3 and showed minimum during monsoon (67.25 ± 39.85 µg m3) and maximum during winter (145.8 ± 50.1 µg m3) over Delhi. The respective post-monsoon and pre-monsoon concentrations were observed to be 133.86 ± 40.02 µg m–3 and 115.7 ± 68.8 µg m3 over Delhi. The PM2.5 concentrations observed here were found to be lower than that reported in the previous study over Delhi (Tiwari et al., 2013b). Tiwari et al. (2013b) identified that concentration of PM2.5 is high during the post-monsoon period attributed to enhanced biomass burning over the region. However, in present study, we found higher PM2.5 values in winter, could be due to low-level inversions leading to trapping of pollutants within the boundary layer. The lower concentrations of PM2.5 in monsoon season may be due to rain and washout mechanisms. The enhancement in PM during post-monsoon is associated with the transport of aerosols due to crop burning in the surrounding regions of Haryana and Punjab 
during the season (Tiwari et al., 2013b). We further analyzed the diurnal cycle of PM2.5 throughout the year during different seasons. The pattern showed a higher variation (in terms of their standard deviation) during pre-monsoon and post-monsoon seasons over Delhi (Fig. 4(a)). PM2.5concentrations showed three major peaks over Delhi. Morning peak observed at around 5–7 IST is associated with fumigating effect due to the breaking of boundary layer as explained in Safai et al. (2007). The peak at 10 IST could be due to large traffic during office hours and the evening peak (around 22 IST) could be associated with the nocturnal cooling and associated lowering of boundary layer, leading to trapping of pollutants (Fig. 4(a)).

Fig. 3. Monthly variations of PM2.5 concentrations over Delhi and Pune during 2015.Fig
. 3. Monthly variations of PM2.5 concentrations over Delhi and Pune during 2015.

Fig. 4. Diurnal Variability of PM2.5 concentrations over Delhi, Pune and their difference during 2015.Fig. 4. Diurnal Variability of PM2.5 concentrations over Delhi, Pune and their difference during 2015.

The annual mean mass concentrations of PM2.5 over Pune during the study period were observed to be 41.04 ± 18.1 µg m3 (ranging between 17.40 µg m3 to 74.03 µg m3 in different months). The minimum concentration was during the monsoon season (22.06 µg m3), while maximum values were observed during winter (64.29 µg m3). The seasonal variability was as low as 10 µg m3 in terms of standard deviations from their daily concentrations over Pune region (it was on average ~45 µg m3 over Delhi). It may be noted that the PM concentrations over Delhi were 1.7–4-fold higher compared to Pune in different months. This mainly is attributed to the larger dust loading over northern parts of India during pre-monsoon season and also due to enhanced vehicular transport and lower ventilation of pollutants (Pandithurai et al., 2008) compared to Pune. Diurnal variability of PM2.5 over Pune in different months showed a peak in the morning around 9–10 IST attributed to the high traffic during the office hours. The second peak observed in the night (around 21–23 IST) could be associated with the settling of pollutants at lower levels due to inversions (Fig. 4(b)).

BC is a highly absorbing aerosol species and is a major component of PM2.5. We also analyzed the contribution of BC to PM2.5 concentrations over both the sites. The monthly cycle of BC from SAFAR network over both Delhi and Pune were found to be matching well with the previous results (Safai et al., 2007; Tiwari et al., 2013a). Fig. 5 monthly variation showed the minimum concentration of 10.8 µg m3 and maximum of 32.1 µg m3 respectively during August and May over Delhi. However seasonal variation showed higher concentration in winter (25.2 µg m3) and minimal concentration in monsoon (16.1 µg m3) seasons. Many previous studies (Tripathi et al., 2005; Tiwari et al., 2013; Bibi et al., 2017b, c) also reported the similar feature of higher values during the winter season over adjacent regions. BC mass concentration ranged between 1.59 µg m3(June) to 9.06 µg m3 (December) over Pune. The seasonal mean BC mass concentration was high in the winter season (7.58 µg m3) and was low during monsoon season (2.2 µg m3). BC mass contribution to PM2.5 found to be 6–20% over Pune. However, the contribution of BC to PM2.5was up to 30% in Delhi (Fig. 6). The study by Islam et al. (2014) over Bangladesh reported a 5–35% of BC concentration in PM2.5. Liu et al.(2016) reported that BC to PM2.5 ratio is 4.6% over Beijing. Marinoni et al. (2010) reported that contribution of BC to fine aerosol mass at Nepal was found 10.8%. Hyvarinen et al. (2009) showed a ~4% average BC to PM2.5 ratio at Mukteshwar, India. While Putaud et al. (2004) found a contribution in the range of 5–10% BC to PM2.5 at European sites.

Fig. 5. Monthly variations of BC concentration over Delhi and Pune for the year 2015.Fig
. 5. Monthly variations of BC concentration over Delhi and Pune for the year 2015.

Fig. 6. Monthly variations of BC to PM2.5 mass fraction ratio over Delhi and Pune for the year 2015.Fig. 6. Monthly variations of BC to PM2.5 mass fraction ratio over Delhi and Pune for the year 2015.

Frequency Distribution of PM2.5

Seasonal frequency distribution (FD) of hourly PM2.5 concentrations with a bin width of 20 µg m3 over Pune and Delhi is shown in Fig. 7. It showed a right side skewed distribution with different mode values over different seasons. The peak distribution of PM2.5 concentrations observed at Pune was within limits of National Ambient Air Quality Standards (NAAQS), i.e., 60 µg m3. The mode of the PM2.5 concentrations slightly found to cross the NAAQS in winter (62.7) over Pune. The mode of FD in pre-monsoon season was found to be 47.1. Similarly, it was 37.5 in post-monsoon and 14.4 during monsoon seasons over Pune. FD of PM2.5 over Delhi showed a Gaussian pattern in different seasons and the concentration levels in all the seasons were always above the NAAQS (except during the monsoon season). This illustrates the prevalence of poor quality of air during pre-monsoon, post-monsoon and winter seasons. Apart from the local uplifting of dry and loose soil by convection, the maximum concentration level in the pre-monsoon season over Delhi is mainly contributed by wind-blown dust (Trivedi et al., 2014). Theenhancement in PM during the post-monsoon season could be due to crop residue burning from the northern part of India. The wintertime high values of PM are attributed to low ventilation due to temperature inversions. The dominance of lower concentration levels during monsoon is due to washout effect of rain mainly on coarse-mode particles.

Fig. 7. Frequency Distribution of PM2.5 over Delhi and Pune with different seasons and their mode values.Fig. 7. Frequency Distribution of PM2.5 over Delhi and Pune with different seasons and their mode values.

Direct Radiative Forcing

The major aerosol optical parameters such as AOD, SSA, and ASP were derived using OPAC model as explained in Section 2.1. Fig. 2 depicts the comparison of monthly averaged OPAC-derived AOD and SSA with AERONET observations. The OPAC-derived values of AOD and SSA found to be matching well with the observations.

AOD showed consistently high values over Delhi compared to Pune. SSA values showed variation from 0.89 to 0.99 over Delhi and 0.81 to 0.95 over Pune during the observational period. Many previous studies reported the radiative forcing estimates over Delhi and Pune using the in-situmeasurements of aerosols over single location representing the city (e.g., Panicker et al., 2010; Srivatstav et al., 2012; Tiwari et al., 2013). However, the present study extends the estimates of PRF by using average data sets of PM2.5 from different environments of SAFAR network located in both the cities.

Fig. 7 depicts the PRF estimates based on the monthly values of PM2.5 (segregated as WS and WIS components as explained in Section 2). The PRF found to be ranging between –20.10 Wm–2 to –35.61 Wm–2 at the surface, –11.90 Wm–2 to –22.41 Wm–2 at the TOA and +7.73 Wm–2to +14.51 Wm–2 in the atmosphere over Delhi. Monthly values of PRF found to be higher in November (–35.61 Wm–2 at the surface, –22.41 Wm–2 at TOA and +13.23 Wm–2 at atmosphere) over Delhi. The high aerosol loading from crop burning during post-monsoon (October–November) could be the reason for high PM values and hence high forcing during November (Bisht et al., 2015). Over Pune, the PRF values found to be ranging between –14.17 Wm–2 to –39.11 Wm–2 at the surface, –8.38 Wm–2 to –20.91 Wm–2 at TOA and +3.12 Wm–2 to +19.20 Wm–2 in the atmosphere (Fig. 8). The highest atmospheric forcing over Pune was found in January (19.20 Wm–2) and low during July (3.12 Wm–2). The radiative forcing reported over similar environments in Pakistan (Iftikhar et al., 2018) and Iran (Gharibzadeh et al., 2017) showed higher forcing compared to the present study.

Fig. 8. PRF at the surface, TOA, and the atmosphere over (a) Delhi and (b) Pune along with their corresponding atmospheric heating rate.Fig
. 8. PRF at the surface, TOA, and the atmosphere over (a) Delhi and (b) Pune along with their corresponding atmospheric heating rate.

To unravel the changes in forcing in PM2.5 due to different chemical composition and albedo conditions, we carried out sensitivity analysis over both the sites and depicted in Table 1. The changes in albedo (vegetative and sand albedo) found to induce a change of ~3–15% in PRF at the surface and ~6–26% at TOA over Delhi. However, the change in PRF due to albedo sensitivity was ~11–23% at the surface and ~20–48% at TOA over Pune. It is also observed that the changes in forcing to sand and vegetation albedo showed high variability over Pune as compared to Delhi. A 10% change in WS mass of PM2.5 found to induce a change of 10% in AOD over Delhi and Pune. Further, the same change (change of 10% in WS component) found to induce a change of ~5–7% at the surface and ~6–9% at TOA in PRF over Delhi. The same change however induced ~1–7% at the surface and ~5–9% at TOA in PRF over Pune (Table 2). The above sensitivity experiments illustrate that the impact of hygroscopicity on PRF is less dominant compared to changes in albedo.

Table 1. Sensitivity analysis by varying the WS and WIS mass concentration by 10%.

Table 2. Sensitivity analysis by varying the Albedo parameters with sand, vegetation, and MODIS BRDF.

The aerosol heating rates also were estimated over Delhi and Pune as explained in Section 2. Atmospheric heating rates over Delhi ranged between 0.47 to 0.92 K day–1, whereas over Pune it ranged between 0.17 to 0.75 K day–1 during the observational period.


  1. Radiative forcing by PM2.5 (PRF) was estimated over two Indian metro cities (Delhi and Pune) using the in-situ measurements of PM2.5 from the SAFAR network.
  2. The results show that the monthly variations of PM2.5 (and BC) were consistently high over Delhi as compared to Pune. This difference may be due to the long-range transport of crop residue, lower ventilation of pollutants because of temperature inversions, and enhanced anthropogenic activities as well as higher dust loading over Delhi than Pune.
  3. The PRF was estimated at the surface, in the atmosphere, and at the TOA over both the regions using the OPAC model in conjunction with the SBDART model.
  4. The estimates from the SBDART model simulations indicated that the PRF ranged from –20.09 Wm–2 to –35.61 Wm–2 at the surface and from –11.90 Wm–2 to –22.41 Wm–2 at the TOA, inducing an atmospheric forcing of 7.732–14.51 Wm–2 over Delhi.
  5. The PRF over Pune ranged from –14.17 Wm–2 to –39.11 Wm–2 at the surface and from –8.38 Wm–2 to –20.91 Wm–2 at the TOA. The aerosol atmospheric absorption over Pune ranged from +3.12 Wm–2 to +19.20 Wm–2 in different seasons.
  6. The sensitivity of the albedo (for vegetation and sand) induced a change of ~3–15% at the surface and ~6–26% at the TOA in the PRF over Delhi.The same changes induced a greater change in the PRF (~11–23% at the surface and ~20–48% at the TOA) over Pune.
  7. A change of 10% in the WS component of the PM2.5 induced a change of ~5–7% at the surface and ~6–9% at the TOA in the PRF over Delhi. The change in the PRF over Pune due to changes in hygroscopicity (changes in WS content) ranged ~1–7% at the surface and ~5–9% at the TOA. Hence, we conclude that the impact of the increase in the hygroscopicity of the aerosols on the PRF was overshadowed by the net changes in albedo.
  8. Atmospheric heating rates over Delhi were in the range of 0.47–0.92 K day–1 in different months, whereas the variation over Pune was 0.17–0.75 K day–1 during the observational period.


Authors are grateful to Director, IITM, for his continuous support for this research. Thanks to SAFAR network for providing the data to carry out this research. Thanks to Dr. P.C.S. Devara for providing the AOD data (AERONET). Thanks to the IMD for providing the AOD and meteorological data, also thanks to GIOVANNI for making available datasets. Thanks to Dr. K. Ravi Kumar, Dr. G. Hari Kishan, Mr. K. Sandeep and Dr. R. Srinivas for scientific discussions. Also, authors would like to thank the anonymous reviewers for their valuable comments and suggestions to improve the quality of the paper.

Aerosol Air Qual. Res. 19 :399 -410 . https://doi.org/10.4209/aaqr.2018.04.0128  

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