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Aerosol Science and Engineering

, Volume 2, Issue 4, pp 153–164 | Cite as

OM/OC Ratio of Polar and Non-Polar Organic Matter during Wintertime from Indo-Gangetic Plain: Implications to Regional-Scale Radiative Forcing

  • Prashant Rajput
Original Paper

Abstract

Ambient PM2.5 (particulate matter with aerodynamic diameter ≤ 2.5 μm) samples have been collected in two winter campaigns: I during 2nd December 2008‒27th February 2009 (n = 24) and II during 3rd December 2010‒11th February 2011 (n = 15). The PM2.5 mass varied significantly from 35 to 220 and 80 to 244 μg m−3 during I and II campaigns, respectively. Based on similar inter-annual variability (statistical two-tailed t test) of PM2.5, K+/PM2.5 (0.010), EC/PM2.5 (0.04) and OC/EC (~ 6) ratio, it has been inferred that the strength of combustion sources, viz. biomass burning and fossil fuel combustion remained more or the less constant during I and II campaigns (OC: organic carbon; EC: elemental carbon). However, significant difference in OC/PM2.5 and WSOC/OC ratios between I and II campaigns indicated a significant change in organic aerosol composition attributable to fog processing vis-à-vis fog scavenging (WSOC: water-soluble organic carbon). The OM/OC (organic mass-to-organic carbon) ratio of polar and non-polar organics averaging at 2.0 and ~ 1.2 (and the overall OM/OC ratio at 1.7) look quite similar during both the campaigns. Principal component analysis (PCA) resolved total source contribution up to 81.4% of which ~ 64% was attributed to mixed contribution from biomass burning emission and secondary transformations, 25% of the resolved source fraction to fossil fuel combustion and 11% of the resolved source fraction to the mineral dust. These results have implications to better parameterization of organic aerosols in chemical transport model and accurate estimation of their influence on regional-scale radiative forcing.

Keywords

Organic aerosols OM/OC ratio Fog processing IGP 

1 Introduction

Carbonaceous aerosols can be broadly categorized into the elemental carbon (EC) and organic matter (OM). The OM contributes significantly (~ 20–90%) to the fine particulate matter loading in the troposphere (Kanakidou et al. 2005; Rajput et al. 2017); of which a substantial fraction is water-soluble (Kaul et al. 2011; Rajput et al. 2016c; Ram and Sarin 2010; Rastogi et al. 2016; Saxena and Hildemann 1996; Weber et al. 2007). The EC is produced as a primary aerosol component due to incomplete combustion of fossil fuel and biomass burning, whereas the OM can also be formed from physicochemical transformations of VOCs in the atmosphere (Kawamura et al. 2005; Saxena and Kulshrestha 2016; Seinfeld and Pandis 2006). Briefly, organic aerosols forming in the atmosphere via chemical reactions of VOCs (volatile organic compounds) with the oxidants (O3, OH radical and NOx) followed by phase transfer to the particulate matter are widely referred to as the secondary organic aerosols (SOA). In a sharp contrast, the organics (biogenic, terragenic or combustion derived) retaining their source signature are referred to as the primary organic aerosols (POA) (Rajput et al. 2017; Seinfeld and Pandis 2006). The SOAs, predominantly occurring in accumulation mode (0.1‒2.5 μm), act significantly as the cloud condensation nuclei (CCN) (Lathem et al. 2013). Abundance and mixing of different aerosol constituents are important factors in governing the atmospheric radiative forcing (Rajeev et al. 2016; Sinha et al. 2013; Srivastava and Ramachandran 2013).

Indo-Gangetic Plain (IGP) experiences many prolonged haze and fog episodes during wintertime (December–February) (Kumar et al. 2017; Rajput et al. 2018). During wintertime, the shallower boundary layer height, stagnant air mass and predominant emissions from anthropogenic combustion sources (stationary: biomass burning, coal combustion and industrial emission, mobile: vehicular emissions) are important factors causing severe pollution episodes over the IGP (Chakraborty et al. 2017; Rajput et al. 2016a, b). There are several previous studies which have been reported from the site (Patiala, in IGP) to understand the characteristics and atmospheric impact of air pollutants from different types of post-harvest biomass burning emissions. For example, a study (Rajput et al. 2011) has reported the utility of cross plot of polycyclic aromatic hydrocarbons (PAHs) isomer ratios to fingerprint distinct emission from large-scale paddy residue burning versus wheat residue burning over the IGP. In another study (Rajput et al. 2014a), the US-EPA (US Environmental Protection Agency) 16-PAHs profile from different geographical locations over northern India has been reported. A previous study (Rajput and Sarin 2014), developed an analytical protocol with solvent-based extraction (Accelerated solvent extraction) technique to determine the OM/OC (organic mass-to-organic carbon) ratio of polar and non-polar organic matter in aerosols from two distinct post-harvest biomass (paddy and wheat residue) burning emissions. In another study (Rajput et al. 2014b), the emission budget of OC, EC and PAHs from agricultural waste burning in IGP was assessed from field-based measurements in conjunction with mapping of open crop residue fire active zone (using MODIS satellite fire-count data). Singh et al. (2014) reported the variability in mass concentrations, optical properties and mass absorption efficiency (MAE) of black carbon and elemental carbon during different emission scenarios in IGP. In this study, one of the main objectives was to assess the OM/OC ratio of polar and non-polar organic matter during wintertime over Patiala. The study site at Patiala is situated in upwind IGP (referring to upwind of major industrial polluting sources in IGP).

In this study, the OM/OC ratio of polar and non-polar organic matter has been documented for the first time during wintertime. Basically, in the radiative forcing models, the OM is required as an input parameter instead of the OC value. It has been demonstrated earlier that input of polar OM and non-polar OM values into the radiative forcing model provides a more robust estimate of radiative forcing due to the organic matter (Ming et al. 2005). Considering that aspect, we had established earlier an analytical protocol for the solvent extraction and quantification of OM/OC ratio for polar and non-polar fractions of organic matter (Rajput and Sarin 2014). In that study, we had provided the OM/OC ratio of polar and non-polar organic aerosols from two large-scale agricultural waste burning emissions in IGP: paddy residue burning during October‒November and wheat residue burning during April‒May. In this study, we report on the OM/OC ratio of polar and non-polar organic matter during wintertime, an information hitherto unknown in the literature from IGP. These results have implications to accurate parameterization of polar and non-polar organic matter, and in turn, could lead to better estimation of radiative forcing due to polar and non-polar OM during wintertime over the IGP (Ming et al. 2005).

2 Methodology

2.1 Site Description and Aerosol Sampling

Sampling site at Patiala (30.2°N; 76.3°E; 250 m amsl) is located upwind of major industrial polluting sources in IGP (Fig. 1). The site is situated in the states of Punjab, which is the predominant producer of wheat and rice in India (Gupta et al. 2004). In Punjab, more than 85% of the total land area is occupied under the agricultural activities. Due to the proximity of high mountainous region (foot hills of Himalaya), the site experiences cold winters. This is one of the reasons for large-scale bio-fuel burning during wintertime in northern India (Rajput et al. 2011). In this study, the PM2.5 sampling was conducted during the hazy and foggy period of 2nd December 2008–27th February 2009 and 3rd December 2010–11th February 2011 at the site. Aerosol sampling (daytime: 0900‒1700 local time) was carried out with a high-volume air sampler (flow rate: ~ 1.2 m3 min−1) onto the pre-baked (350 °C for ~ 6 h) quartz fiber filters (PALLFLEX™, 2500QAT-UP, 20 cm × 25 cm) at about 15 m height above ground level (on the terrace of the Department of Physics building; Punjabi University, Patiala, Punjab, India).
Fig. 1

Figure showing sampling location at Patiala in upwind Indo-Gangetic Plain (IGP: represented by dark green-shaded region)

2.2 Ambient Meteorology

Ambient temperature, relative humidity, visibility index and meteorological winds have been monitored at the site during the field campaign. The ambient temperature, wind, visibility and relative humidity (RH) records (provided by Indian meteorological Department, Punjabi University Campus, Patiala) for the study period exhibited a near similar inter-annual variability pattern. The average ambient temperature varied overall from 6 to 25 °C during 2nd December 2008‒27th February 2009 (I campaign) and from 5 to 24 °C during 3rd December 2010–11th February 2011 (II campaign). The corresponding relative humidity (RH) varied from 46 to 97% and 45 to 99%, respectively. The prevailing winds were north westerly and moderate in intensity (~ 4 m s−1). Atmospheric boundary layer height (National Oceanic and Atmospheric Administration: NOAA) was shallower (~ 1 km) during the entire study period (Stein et al. 2015).

2.3 Chemical Analysis in Laboratory

2.3.1 Determination of Aerosol Mass and EC-OC Analysis

The gravimetric and all chemical analyses were carried out soon after aerosol collection. Briefly, the blank and aerosol-laden filters were equilibrated for ~ 12 h at relative humidity of 37 ± 3% and temperature of 24 ± 2 °C, prior and post to the sampling. The difference in gravimetric masses (prior and post to sampling) has been utilized to determine the PM2.5 mass. The concentration of EC-OC has been measured using NIOSH (National Institute for Occupational Safety and Health) protocol on Sunset Lab carbon analyzer (Model 2000, USA) (Birch and Cary 1996; Rajput et al. 2011). Analytical accuracy of 3% in determining the total carbon (TC = OC + EC) was ascertained by analyzing known amounts of potassium hydrogen phthalate (KHP; n = 8). Furthermore, several aerosol punches (n = 15) were de-carbonated in HCl fumes for assessing the peak positioning and quantifying the content of carbonate carbon (CC) in the sample (Cachier et al. 1989). Area under the CC peak in thermograph was integrated manually and corrected for OC concentrations in aerosols as reported in an earlier publication (Rajput et al. 2014c). Filter-based blank concentrations of OC (3.6 ± 0.6 μg/cm2; n = 6) has been determined and the average value was subtracted from the measured concentration in aerosol samples, after normalizing to the full-filter area.

2.3.2 Quantification of Water-Soluble Components: WSOC and WSIS

For the quantification of water-soluble organic carbon (WSOC), 1–2 punches (3.14 cm2 each) of sampled filters were extracted with 30/40 mL of Milli-Q water (resistivity: 18.2 MΩ cm). The resulting water extract was filtered through glass fiber filter (Millipore) using a glass syringe (capacity: 10.0 mL) into a pre-cleaned amber-colored glass vial. Subsequently, the filtered extract was analyzed for the WSOC within 12 h from the time of extraction on a total organic carbon analyzer (Shimadzu model TOC 5000A), using a non-dispersive infrared detection (NDIR) technique. The linear calibrations for the TC and IC were achieved (R2 > 0.99) on TOC analyzer using potassium hydrogen phthalate (KHP) and sodium carbonate–bicarbonate mixture (Na2CO3 + NaHCO3) as the external standards, respectively. The measurement protocol for the WSOC included determination of: (i) water-soluble total carbon (WSTC) by injecting a 100 μL of water extract in the furnace (hold temperature: 680 °C) and (ii) water-soluble inorganic carbon (WSIC) by treating a 250 μL of water extract with HCl in situ. The concentration of WSOC has been determined from these measurements (WSOC = WSTC − WSIC). Replicate injections (3–5 times) of water extract showed that the uncertainty on WSOC analysis was within ± 3%. The analytical protocol for the determination of WSIS on an Ion-chromatograph (Dionex®) equipped with suppressed conductivity detector (ED-50) is similar to that reported in a previous study from our group (Rengarajan et al. 2007). Average blank concentrations of WSOC (1.35 μg/cm2) and of individual WSIS (K+: 0.01 μg/cm2; Na+: 0.03 μg/cm2; NH4+: 0.03 μg/cm2; Ca2+: 0.003 μg/cm2; Mg2+: 0.0 μg/cm2; Cl: 0.06 μg/cm2; NO3: 0.03 μg/cm2 and SO42−: 0.06 μg/cm2) have been deduced from the values measured for aerosol samples. The percentage ratio of these species in blank filter as compared to those in aerosol samples ranged from 1‒5% for WSOC, 0‒3.5% for K+, 1.6‒6.5% for Na+, 0‒1% for NH4+, 0‒2.4% for Ca2+, 0% for Mg2+, 0.2‒48% for Cl, 0‒0.2% for NO3 and 0‒0.4% for SO42−. It is worthwhile mentioning here that the high blank-to-aerosol sample ratio for Cl is due to very low signal of Cl in aerosol samples collected from northern India and not because of its high concentration in blank filters.

2.3.3 Determination of OM and OC in Polar and Non-Polar Fractions of Organic Matter

The OM/OC ratio in aerosol samples has been reported previously elsewhere utilizing different solvent extraction approaches (El-Zanan et al. 2009; Japar et al. 1984). In this study, a portion of each aerosol sample was extracted sequentially with 30–40 mL of hexane followed by dichloromethane (DCM): acetone (30–40 mL; 1:1, v/v) solution on ASE (Accelerated Solvent Extraction 200, Dionex). Extracts were evaporated till near dryness under a gentle stream of N2 gas on an evaporator (Turbo Vap LV II, USA). Subsequently, the final volume was made to 500 μL with the appropriate solvent; hexane was added to non-polar fraction residue while acetone was added to the polar residue. From each of these fractions, a 20 μL aliquot was deposited onto the separate pre-combusted tissuquartz filter (1.5 sq. cm) for the determination of OC-NP (non-polar OC) and OC-P (polar OC) on carbon analyzer (Sunset lab). Likewise, a 100 μL aliquot was deposited and dried under a gentle stream of N2 for the gravimetric determination of OM-NP (non-polar OM) and OM-P (polar OM). The mass and carbon content of non-polar (OM-NP, OC-NP) and polar (OM-P, OC-P) fractions have been quantified post to the assurance of complete solvent evaporation (i.e. no measurable solvent memory effect) by drying the deposited filter punches at room temperature in an inert atmosphere (N2) for about 24 h. Organic mass (OM-NP) and organic carbon (OC-NP) in the hexane extract represent characteristics of non-polar organic fraction; while OM-P and OC-P measured in DCM: acetone extract represent the characteristics of polar organics. The solvents (DCM, hexane and acetone) used herein for the extraction and sample preparation were HPLC grade (Chromasolv® Plus, Sigma-Aldrich; purity ≥ 95%).

Quality assurance of the data was assessed routinely by monitoring the OC directly in composite aerosols and independently by sequential extraction with hexane (OC-NP) followed by 1:1 (v/v) mixture of DCM: acetone (OC-P) (Fig. S1). Linear regression analysis between the extracted OC (= OC-NP + OC-P) and the total OC (i.e. OC in composite aerosols) exhibit a slope of 0.95 ± 0.04 (R2 = 0.95; Fig. S1). This suggested that the two set of measurements agree well within an analytical uncertainty of ± 5%. Furthermore, the quality control of the data was monitored in every batch of the analysis. Briefly, a 100 μL of hexane and acetone were deposited on separate blank filter punches (1.5 sq. cm) and dried (for ~ 24 h) under nitrogen atmosphere (as carried out with the aerosol extracts deposit). Subsequent determination of OC in these filters, also ascertained that no measurable memory effect due to solvents influenced the determination of OC, and hence, the OM in aerosol samples. For details on solvent extraction using accelerated solvent extraction (ASE) technique, reference is made to a previous literature (Rajput and Sarin 2014).

3 Results and Discussion

3.1 Inferences from Visibility Index

Meteorological data set of visibility index is plotted along with the field-based measurements of PM2.5 for the two campaigns (carried out in successive winters) from Patiala in northern India (Fig. 2a, b). During the I campaign (2nd December 2008–27th February 2009), the visibility was always lower than 5 km. Moreover, out of the 24 sampling events, six events showed much less atmospheric visibility (< 1 km; Fig. 2a). In this context, meteorological data set revealed that the preceding nights (nights before those six sampling events) were foggy (fog generally persists until 10 am over the IGP). And therefore, those six sampling events were identified as the post-foggy day, whereas the remaining 18 events represent the non-post-foggy day. Thus, majority of the sampling events represent non-post-foggy day during I campaign. However, during the II campaign (3rd December 2010–11th February 2011), it can be seen from Fig. 2b that most of the sampling events (except one event) represent the post-foggy day (visibility < 1 km). The PM2.5 mass concentration varied from 35‒220 μg m−3 during I campaign and from 80 to 244 μg m−3 during the II campaign (Fig. 2a, b; Table 1). Since the number of samples collected on post-foggy and non-post-foggy day in a given year is not enough, it has been considered to discuss the results on inter-annual variability basis in the following sections.
Fig. 2

Visibility index (vis) and PM2.5 variability during: a I campaign and b II campaign in wintertime at Patiala. Also shown the post-foggy and non-post-foggy events during sampling

Table 1

Statistical two-tailed t test for different parameters in aerosols over northern India (Patiala) for two winter seasons

Parameters

I Campaign

II Campaign

t value

Difference

p value

2nd Dec 2008‒27th Feb 2009

3rd Dec 2010‒11th Feb 2011

(N = 24)

(N = 15)

PM2.5

131 ± 51 (35‒220)

158 ± 52 (80‒244)

1.6

Not significant

> 0.05

OC/PM2.5

0.24 ± 0.03 (0.19‒0.30)

0.18 ± 0.03 (0.12‒0.24)

6.0

Significant

< 0.05

K+/PM2.5

0.013 ± 0.007 (0.002‒0.025)

0.010 ± 0.003 (0.006‒0.016)

1.6

Not significant

> 0.05

EC/PM2.5

0.05 ± 0.02 (0.02‒0.09)

0.04 ± 0.01 (0.02‒0.05)

1.8

Not significant

> 0.05

OC/EC

5.9 ± 2.0 (2.5‒10.1)

5.4 ± 1.6 (2.6‒8.5)

0.8

Not significant

> 0.05

WSOC/OC

0.60 ± 0.10 (0.45‒0.86)

0.72 ± 0.07 (0.64‒0.82)

4.1

Significant

< 0.05

OM-NP/OC-NPa

1.2 ± 0.2 (0.9‒1.4)

1.1 ± 0.2 (0.9‒1.4)

1.5

Not significant

> 0.05

OM-P/OC-Pa

2.0 ± 0.3 (1.7‒2.7)

2.0 ± 0.3 (1.7‒2.6)

0.0

Not significant

> 0.05

(OM/OC) total a

1.7 ± 0.2 (1.4‒2.2)

1.7 ± 0.2 (1.5‒2.1)

0.0

Not significant

> 0.05

aData points: N = 20 during I campaign and N = 15 during II campaign

3.2 Inter-Annual Variability of PM2.5 and Carbonaceous Species: EC, OC and WSOC

Inter-annual variability of PM2.5, OC/PM2.5, K+/PM2.5 and EC/PM2.5 is shown in Fig. 3a–d. Average data and associated variability of these parameters for I and II campaigns are listed in Table 1. From Fig. 3a, it is obvious that the PM2.5 level decreased during the end of February (I campaign: 2nd December 2008‒27th February 2009; II campaign: 3rd December 2010‒11th February 2011) when the boundary layer height (retrieved from NOAA) increased from < 500 m to ~ 1 km. The other important observation from the figure relates to the finding that even when the boundary layer height was nearly similar (e.g., during the month of December), the PM2.5 showed high variability spreading over a factor of 2 or so (Fig. 3a). Thus, it seems that the source variability could be an important factor causing a large variability in PM2.5 values. Average mass concentrations of PM2.5 were 131 ± 51 and 158 ± 52 μg m−3 during I and II campaigns, respectively (Table 1). Statistical two-tailed t test analysis suggests that PM2.5 concentrations look similar during the two campaigns (Table 1; t = 1.6, p > 0.05). Thus, it can be inferred that high intra-winter variability of PM2.5 is attributable to high variability in its source strength. However, when looking into its inter-annual variability, the source strength appears to be more or the less constant during the studied winters.
Fig. 3

Temporal variability of: a PM2.5, b OC/PM2.5, c K+/PM2.5 and c EC/PM2.5 during wintertime at Patiala (upwind IGP). Boundary layer height (retrieved from NOAA) variability during aerosol sampling is also shown in top figure

The OC/PM2.5 varied from 0.19 to 0.30 (avg. ± SD: 0.24 ± 0.03) and from 0.12 to 0.24 (0.18 ± 0.03) during I and II campaigns, respectively (Fig. 3b; Table 1). The difference in OC mass fraction for the two campaigns was found to be statistically significant (t = 6.0; p < 0.05). Thus, it is important to reiterate here that PM2.5 showed insignificant difference, whereas the OC mass fraction was found to be quite different during the two campaigns. These observations plausibly suggest that some mechanism is altering the organic aerosol composition during the two winter seasons. Furthermore, the K+/PM2.5 showed insignificant inter-annual variability (averaging ~ 0.10; Fig. 3c, Table 1). Fine K+ has been widely used as a tracer of biomass burning emission (Andreae 1983; Rajput et al. 2014b). It is worthwhile mentioning that near similar mass fraction of K+ during the two campaigns indicates that the source strength of biomass burning remained nearly constant during the study period (Table 1). Likewise, EC/PM2.5 also showed insignificant inter-annual variability (averaging ~ 0.04; Fig. 3d, Table 1). It is imperative to mention here that the EC is produced from combustion sources: biomass burning and fossil fuel combustion. Thus, near identical mass fractions of K+ and EC during the two campaigns revealed that the source strength of biomass burning as well as the fossil fuel combustion was nearly constant.

3.3 OC/EC and WSOC/OC Ratio

The OC/EC ratio varied from 2.5 to 10.1 (5.9 ± 2.0) and from 2.6 to 8.5 (5.4 ± 1.6) during I and II campaigns, respectively (Fig. 4a). Statistical two-tailed t test suggests that the OC/EC ratio was similar during the two campaigns (t = 0.8; p > 0.05; Table 1). However, it is really interesting to observe that the WSOC/OC ratio was quite different during the two campaigns (Fig. 4b). The WSOC/OC ratio averaged at 0.60 ± 0.10 and 0.72 ± 0.07 during I and II campaigns, respectively (t = 4.1; p < 0.05; statistically different). WSOC/OC ratio overall varied from 0.45 to 0.86 and constituted ~ 64% of the total OC (WSOC/OC: 0.64 ± 0.11; Fig. 3b). It would be important here to compare the OC/EC and WSOC/OC ratio with the data set from a preceding activity of large-scale post-harvest agricultural waste burning of paddy residue. A previous study has reported that relatively high OC/EC ratio averaging at 10.6 ± 1.6 for paddy residue burning emission (Rajput et al. 2014b). However, the EC/TC ratio of 0.16 ± 0.05 (winter, this study) was relatively high as compared to that reported from post-harvest biomass (paddy residue) burning emission (EC/TC ratio: 0.10 ± 0.04) (Rajput et al. 2014b). The difference in mass fractions of EC and OC in this study during wintertime as compared to that reported from large-scale biomass burning (paddy residue) emission in IGP is attributable to contribution from other sources during wintertime. It is important to mention here that the overall WSOC/OC ratio in this study (0.64) is quite similar to that for paddy residue burning emission (0.63 ± 0.15). Previous studies have suggested that significant contribution of WSOC to OC can be attributable to large-scale biomass burning emission and/or SOA (secondary organic aerosol) formation (Rajput et al. 2013, b; Weber et al. 2007).
Fig. 4

Wintertime variability of: a OC/EC and b WSOC/OC ratio in this study

3.4 Influence of Fog on Organic Aerosol Composition

It is also important to reiterate here that the atmospheric condition while aerosol sampling in I campaign (December 2008‒February 2009) was mostly representing the non-post-foggy day. In a sharp contrast, atmospheric condition in II campaign (December 2010‒February 2011) was by-and-large representing the post-foggy day. Thus, with this background information, it would be really interesting to assess the cause for change in organic aerosol composition as indicated by the differences in OC/PM2.5 and WSOC/OC ratios during the two wintertime campaigns. A scatter plot of WSOC/OC versus OC/PM2.5 ratio for both the campaigns is shown in Fig. 5. The OC/PM2.5 ratio centered at 0.24 ± 0.03 and 0.18 ± 0.03 during I and II campaigns, respectively. In other words, the OC mass fraction was about 6% higher during the I campaign (Table 1; Fig. 5). In a sharp contrast, the WSOC/OC ratio was higher in the II campaign (WSOC/OC: 0.60 ± 0.10) as compared to that in the I campaign (0.72 ± 0.07; Table 1). If we couple now these information’s with the atmospheric condition as discussed above, it can be inferred that during the post-foggy days (by-and-large in II campaign), the WSOC/OC ratio was higher but OC/PM2.5 was lower as compared to that during the non-post-foggy day condition (mostly in I campaign). A quite similar observation has been reported earlier based on HR-TOF-MS (high-resolution time-of-flight aerosol mass spectrometer) from central IGP location at Kanpur (Chakraborty et al. 2015). Their study has observed a decrease of ~ 5% (nearly similar to that observed herein of 6% difference) in OC mass fraction (in PM1) during the post-foggy events as compared to that during non-post-foggy events. Furthermore, their study based on higher O/C ratio has also reported a similar observation that organic aerosols are relatively more oxidized during the post-foggy events than non-post-foggy events (Chakraborty et al. 2015). Moreover, a recent study (Rajput et al. 2018) provides a direct evidence that fog processing of organic aerosols enhances the WSOC/OC ratio significantly. Another study has reported earlier that more oxidized and hygroscopic organic aerosols are expected to be preferentially scavenged by fog as compared to primary biomass burning derived organic aerosols (Gilardoni et al. 2014). Thus, the observation of decrease in OC mass fraction during post-foggy events is attributable to fog scavenging of organic aerosols, whereas the increase in WSOC/OC ratio relates to fog processing of organic aerosols. Finally, it can be summarized here that fog plays a crucial role in altering the atmospheric organic aerosol composition.
Fig. 5

Influence of fog interaction (fog scavenging versus fog processsing) in altering organic aerosol composition as inferred from scatter plot of WSOC/OC versus OC/PM2.5 ratio during wintertime

3.5 Polar and Non-Polar Organic Aerosols: OM/OC Ratio

As stated earlier that the organic aerosols can be broadly sub-divided into polar and non-polar organics. The knowledge of their individual mass and composition has significance on better parameterization of organic aerosols and accurate estimation of their influence on radiative forcing (Kanakidou et al. 2005; Ming et al. 2005; Saxena and Hildemann 1996). Linear regression analysis and mass fractions of polar and non-polar OC (OC-P and OC-NP) to the total OC are shown in Fig. 6a, b. Accordingly, the OC-NP and OC-P constituted 38 ± 3 and 52 ± 4% to the total OC (R2 ≈ 0.8). Thus, carbon content of polar organics is more than the non-polar organics. Likewise, assessing the mass contribution revealed that the OM-NP contribution was 28%, whereas the OM-P contributed 72% to the total OM (Fig. 6c, d). Average mass concentrations of OM-NP and OC-NP (non-polar organics) were 11.7 ± 5.8 and 10.1 ± 4.7 μg m−3, respectively; the average OM-NP/OC-NP ratio was 1.2 ± 0.1 (R2 = 0.92; Fig. 6e). For the polar organics, the mass concentrations of OM-P (38.6 ± 12.7 μg m−3) and OC-P (19.1 ± 6.3 μg m−3) were relatively high than those for the non-polar organics. The OM-P/OC-P ratio was also higher and averaged at 1.9 ± 0.2 (R2 = 0.83; Fig. 6f). The OM-NP/OC-NP ratio represents the non-polar organic mass to non-polar organic carbon ratio. Likewise, the OM-P/OC-P represents a conversion factor for the polar organics. It is also important to assess the fractional contributions of polar and non-polar organic mass to PM2.5. In this study, the polar and non-polar organic mass (OM-P and OM-NP) contributed 28 ± 7% and 8 ± 3% (avg. ± σ), respectively, to the PM2.5.
Fig. 6

Characteristic features of organic aerosols in this study: a, b mass fractions of OC-NP and OC-P to total OC, c, d mass fractions of OM-NP and OM-P to total OM and e, f OM-NP/OC-NP and OM-P/OC-P ratios

Large-scale anthropogenic emissions from fossil fuel combustion and biomass burning in conjunction with the shallower boundary layer height (< 1 km) and stagnant air mass (wind speed ≈ 3 m/s) cause heavy air pollution over the entire IGP (for geographical distribution of IGP the reference is made to Fig. 1) (Rajput et al. 2013, 2018; Ram et al. 2010; Singh and Gupta 2016). To understand the atmospheric processes and mitigation of air pollution, several studies keep focusing on aerosol composition and characteristics during the wintertime in IGP. It is worthwhile mentioning that previous studies from this region estimated OM based on the theoretical OM/OC value provided in the literature (Turpin and Lim 2001). The main reason behind this assumption is the lack of OM/OC ratio measurements over the region. Looking carefully into the data set of the published literature, it has revealed that most of the regional sites have OC and WSOC measurements. Therefore, one can calculate the WIOC (= OC − WSOC; WIOC: water-insoluble organic carbon) with their measurements. Now, here an attempt has been made to convert the WIOC and WSOC values to their corresponding mass. A similar approach has been suggested earlier for high altitude Himalayan site from Nepal Climate Observatory-Pyramid (NCO-P) (Decesari et al. 2010). The linear regression analyses between WIOC vs. OC-NP and WSOC vs. OC-P yielded slopes ≈ 0.95 (Fig. 7a, b). This observation suggested that during wintertime the WSOC represented by-and-large the OC-P and WIOC represented the OC-NP. Based on the above analogy, it is important to construct OM for WIOC and WSOC. As stated earlier, we have gravimetrically determined OM-NP and OM-P. The linear regression analyses between WIOC vs. OM-NP and WSOC vs. OM-P yielded two set of equations (Fig. 7c, d; p < 0.05) as given below:
$${\text{OM - NP }} = \, \left( {0.62 \, \pm \, 0.12} \right) \times {\text{WIOC }}{-} \, 0.08$$
(1)
$${\text{OM - P }} = \, \left( {1.78 \, \pm \, 0.16} \right) \times {\text{WSOC }} + \, 4.55.$$
(2)
Fig. 7

Linear regression analyses (p < 0.05) between: a OC-NP vs. WIOC, b OC-P vs. WSOC, c WIOC vs. OM-NP and d WSOC vs. OM-P

It is expected that the above reported Eqs. (1, 2) could assist future studies in constructing the organic mass for polar and non-polar organic matter.

The total OM and OC average concentrations observed were 50.3 ± 17.3 and 29.2 ± 10.3 μg m−3, respectively. The total OM/OC ratio exhibited variability from 1.4‒2.2 (avg. ± σ: 1.7 ± 0.2) during wintertime (OM = OM-NP + OM-P and OC = OC-NP + OC-P) (Fig. 8). It is important to mention here that OM/OC ratio of pure hydrocarbons from anthropogenic emission is theoretically expected close to 1.2 (Turpin and Lim 2001). However, incorporation of heteroatoms (such as O, N and P) in the compound enhances OM, and thus, increases the OM/OC ratio. Oxidation of organic species (i.e., SOA formation) could enhance OM/OC ratio via incorporation of heteroatoms such as O and N (Rajput and Sarin 2014; Turpin and Lim 2001). However, primary emissions particularly from biomass burning may also contain heteroatoms including N and P. Theoretical approach suggests that OM/OC ratio of ambient aerosols can be as high as 2.0–4.0, depending on the presence of myriads of organic species (Turpin and Lim 2001). During wintertime, the OM/OC ratio of 1.7 determined in this study (Fig. 8) suggests for significant contribution of heteroatoms in the organic matter composition.
Fig. 8

Temporal variability of organic mass-to-organic carbon (OM/OC) ratio during wintertime over northern India (at Patiala)

3.6 Inferences from Anthropogenic Inorganic Species

Average data set for both the winter campaigns is given in Table 2. This table provides mass concentration of individual species (including inorganic species: NH4+, NO3 and SO42−) in PM2.5. Prior to discussion on the concepts from ionic species data, it is considered relevant to elaborate the definition of some of the important ratios. For example, mass ratio of two species is the ratio of their mass concentrations (µg m−3). Charge ratio is studied to assess the extent of neutralization. It is widely represented either as molar ratio or equivalent ratio. In this study, the charge ratio has been represented utilizing the equivalent ratio. Equivalent ratio can be calculated by dividing the number of equivalents (mass concentration/eq. wt.) of one species by that of the other, as shown below:
$${\text{Equivalent ratio of}} \; \frac{X}{Y} = \frac{{{\text{Mass }}\;{\text{concentration }}\;{\text{o}}f\; X}}{{{\text{Equivalent }}\;{\text{wt}} . \;{\text{of}}\; X }} \times \frac{{{\text{Equivalent }}\;{\text{wt}} .\; {\text{of}} \;Y}}{{{\text{Mass }}\;{\text{concentration}}\; {\text{of}}\; Y}}$$
(3)
Table 2

Average composition of PM2.5 during wintertime (December–February) at Patiala in the Indo-Gangetic Plain

Parameters

Abundance

PM2.5

141 ± 52 μg m−3

% composition

 OM

36

 EC

4.2

 ΣWSISa

31

 UMb

28.8

% of individual WSISa

 NH4+

7

 K+

1

 NO3

9

 SO42−

13

 WSIS-rest

1

aWSIS refers to water-soluble inorganic species (sum of: NH4+, Na+, K+, Mg2+, Ca2+, Cl, NO3 and SO42− concentrations)

bUnaccounted mass (UM) refers to contributions from mineral dust and aerosol moisture content

The charge balance assessment between total anions (Σ) and cations (Σ+) revealed that Σ+ equivalent ratio was 0.88 ± 0.08. Relatively low contribution of anions has been attributed to the unaccounted mineral aerosol constituent, i.e., HCO3 (not measured in this study). The information on the charge ratio of NH4+/SO42− and mass ratio of NO3/SO42− is very important to understand the chemistry of these secondary ionic species (Pathak et al. 2009). In general, the NH4+-to-SO42− equivalent ratio of 1 indicates that SO42− is completely neutralized by the NH4+. In this study, the NH4+-to-SO42− equivalent ratio > 1 (1.47 ± 0.49; as observed in this study; Fig. 9a) suggested that NH4+ was in excess. In this context, previous studies have reported that excess NH4+ can lead to uptake of NO3 in the particulate phase (Pathak et al. 2009; Rajput et al. 2016b). Furthermore, the NO3/SO42− average mass ratio was 0.71 ± 0.33 in this study (Fig. 9b). During the paddy residue burning emission in IGP, a previous study has reported average NH4+-to-SO42− equivalent ratio of 1.3 and NO3/SO42− mass ratio of 0.61 (Rajput et al. 2014c). The average NO3/SO42− mass ratio < 1 can be attributed to predominant impact from stationary source/s (biomass burning, coal combustion and industrial emission), whereas the NO3/SO42− mass ratio ≥ 1 indicates predominant impact from mobile source (vehicular emissions) (Wu et al. 2017). A recent study based on positive matrix factorization (PMF) has reported similar results with the NO3/SO42− mass ratio in IGP (Rajput et al. 2016b).
Fig. 9

Figures showing: a ammonium-to-sulfate equivalent ratio and b nitrate-to-sulfate mass ratio in this study (Patiala) and from other locations in India. Date set for other studies have been adopted from the literature: paddy residue burning (PRB) (Rajput et al. 2014c); Pune (Momin et al. 1999); Ahmedabad and Mt. Abu (Rastogi and Sarin 2005). Here, asterisks over study sites denotes PM10 sampling, otherwise it is collection of PM2.5

It is worthwhile comparing current results with those reported earlier from other regional studies over India; from an urban location at Pune (Momin et al. 1999) and semi-arid high-dust regions at Ahmedabad and Mt. Abu (Rastogi and Sarin 2005). From Pune, the average NH4+-to-SO42− equivalent ratio of 1.9 suggested that all SO42− was neutralized by NH4+, whereas the NO3/SO42− mass ratio of 0.98 indicated the predominant influence from mobile sources (vehicular emission) (Momin et al. 1999). In the semi-arid region in western India, the average NH4+-to-SO42− equivalent ratios of 0.38 at Ahmedabad and 0.36 at Mt. Abu suggested that entire SO42− was not neutralized by NH4+ (Rastogi and Sarin 2005). It is important to mention here that in their study, the uptake of left-over acidic species (SO42−) onto the surface of mineral dust was the primary cause towards achieving a complete neutralization. The NO3/SO42− average mass ratio of 0.47 at Ahmedabad and 0.27 at Mt. Abu have been reported, indicating predominant impact of stationary sources (mainly industrial activity) (Rastogi and Sarin 2005). Summing up, the NO3/SO42− mass ratio approach only indicates the influence of stationary versus mobile source, but neither assists in quantifying the sources contribution nor helps in inferring whether it is biomass burning or the fossil fuel combustion source. In this context, the source apportionment has been carried out in the following section.

3.7 Principal Component Analysis (PCA)

Before discussing source apportionment, it is reasonable to assess the correlation analysis among various chemical constituents (Table S1). It is obvious from table S1 that WSOC, NH4+, NO3, SO42− and OC-P exhibit strong correlation among each other (p < 0.05). Furthermore, K+ correlates strongly with WSOC, OC-NP, OC-P, NH4+, NO3 and SO 4 2 . Fine-K+ has been suggested previously as a tracer of biomass burning emission (Andreae 1983). A previous study has shown a strong co-variability of K+ with levoglucosan (molecular marker of biomass burning emission) (Cheng et al. 2013). Several studies have utilized K+ to assess the impact of biomass burning emission over the IGP (Rajput et al. 2014b; Ram et al. 2010). Thus, a strong correlation among both the polar and non-polar fractions of OC (OC-NP and OC-P) and their linear variability with K+, WSOC, NH4+, NO3 and SO42− indicated that major source of organic aerosols and secondary inorganic species is biomass burning emission in this study.

Principal component analysis (PCA) has potential application to quantify the emission source contribution (Jackson 1991). Basically, the PCA splits the entire data set into a set of linearly independent variables. It performs grouping of variables into representable factors (i.e., sources) while retaining most of the original information (Ho et al. 2002; Mallik et al. 2013). The PCA analysis has been performed using the SPSS software. Result of the analysis is shown in Table 3, wherein the factors along with their loadings are given. A “Kaiser–Meyer–Olkin measure of sampling adequacy” (KMO) of 0.71 (this study) indicated a strong connection between set of variables and appropriateness of factor analysis. In general, KMO value > 0.5 indicates for a reliable solution based on the factor analysis. Furthermore, the Bartlett’s test of sphericity was very significant (p < 0.05) indicating that the rotated matrix is totally uncorrelated with the identity matrix. For eigen value > 1, the resolved factors, viz. 1, 2 and 3 explained 52.1, 20.3 and 9.0% of the total variance (81.4%), respectively (Table 3). The varimax rotation with Kaiser normalization has been performed for a robust solution (Mallik et al. 2013). The first component contained most of the PM2.5 constituents (OC, WSOC, OC-NP, OC-P, K+, NH4+, SO42− and NO3). It is important to mention here that a strong linear correlation of these species with K+ has been observed in this study. Considering K+ as a tracer of biomass burning emission (with some contribution from Cl) (Andreae 1983; Lobert et al. 1999), and significantly high loading of ammonium, sulfate, nitrate and WSOC in factor 1, this factor has been assigned to mixed contribution from biomass burning emission and secondary transformations. The second factor (factor 2) containing EC predominantly was assigned to the fossil fuel combustion (Gustafsson et al. 2009). The third factor containing Ca2+ and Mg2+ was assigned to the mineral dust (Rastogi and Sarin 2005). Summing up, with the quantum of measured parameters the total source contribution that could be resolved was 81.4% of which ~ 64% was attributed to mixed contribution from biomass burning and secondary transformations, 25% of the resolved source fraction to fossil fuel combustion and 11% of the resolved source fraction to the mineral dust. From a downwind location at Kanpur in IGP, a recent study during wintertime (Rajput et al. 2018) based on the positive matrix factorization has reported the net contribution of 66% from biomass burning and secondary transformations, which is slightly higher (by 2%) as compared to that observed herein from an upwind IGP location (referring to upwind of major industrial polluting sources in IGP).
Table 3

Varimax rotated factor loading of PM2.5 constituents during wintertime at Patiala

Variable

Principal components

1

2

3

OC

0.802

0.352

0.205

EC

0.267

0.588

− 0.049

WSOC

0.835

0.071

0.113

OC-NP

0.581

0.470

0.040

OC-P

0.614

0.369

0.142

NH4+

0.820

0.360

− 0.156

K+

0.658

0.146

0.108

Na+

0.786

− 0.185

0.296

Mg2+

0.129

0.048

0.571

Ca2+

0.040

− 0.029

0.846

Cl

0.879

0.061

0.032

NO3

0.870

0.266

0.071

SO42−

0.884

0.090

− 0.029

Variance (%)

52.1

20.3

9.0

Cumulative (%)

52.1

72.4

81.4

Source

Biomass burning + secondary transformations

Fossil fuel combustion

Mineral dust

Extraction method: principal component analysis

Rotation method: Varimax with Kaiser normalization

Eigen value: > 1.0

Factor loading ≥ 0.500 are shown in bold

Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy (should be > 0.5): 0.71

Bartlett’s test of sphericity (should be < 0.05): 0.000

4 Conclusions

This study provides the data set on ambient concentrations of PM2.5, EC, OC, WSOC, water-soluble ionic species along with fractional contributions of polar and non-polar organics and organic carbon-to-organic mass conversion factor during hazy episodes in wintertime from IGP. The information on OM/OC ratio in ambient aerosols has been documented herein for the first time during wintertime from IGP. The important conclusions drawn from this study are:
  1. 1.

    During wintertime (at Patiala, upwind IGP: upwind of major industrial polluting sources in IGP), the PM2.5 mass concentration averaged at 141 ± 52 μg m−3 of which 36% was contributed by OM, whereas EC contribution was only ~ 4%.

     
  2. 2.

    Mass contribution of polar organics (OM-P) to PM2.5 was 28%, whereas non-polar organics (OM-NP) contributed ~ 8%.

     
  3. 3.

    OM/OC ratios of polar and non-polar organic fractions were 1.9 ± 0.2 and 1.2 ± 0.1, respectively. The overall OM/OC ratio in this study was 1.7 ± 0.2.

     
  4. 4.

    Two set of linear equations have been provided to construct polar and non-polar OM from WSOC and WIOC measurements, and hence, the OM/OC ratio during wintertime: (i) OM-NP = (0.62 ± 0.12) × WIOC − 0.08; (ii) OM-P = (1.78 ± 0.16) × WSOC + 4.55.

     
  5. 5.

    The OC/EC (avg.: 5.7), WSOC/OC (64%) and EC/TC ratio (0.16) represent characteristic features of carbonaceous aerosols during wintertime in upwind IGP (at Patiala).

     
  6. 6.

    Strong linear correlations of major constituents in PM2.5 with the K+ indicated predominant impact of biomass burning emission. Upon quantification with PCA, ~ 64% of the resolved source fraction was attributed to mixed contribution from biomass burning and secondary transformations, 25% of the resolved source fraction by fossil fuel combustion and 11% of the resolved source fraction by the mineral dust.

     

The present study documents the OM/OC ratio for polar and non-polar organic matter in aerosols from the IGP during wintertime. These results have implications to better parameterization of organic aerosols in chemical transport model and accurate estimation of their influence on regional-scale radiative forcing.

Notes

Acknowledgements

I thank Prof. M. M. Sarin (Geosciences Division; Physical Research Laboratory, Ahmedabad, India) and ISRO-GBP office (Bengaluru, India) for supporting this study. I also thank, Prof. Darshan Singh and Dr. Deepti Sharma for support in aerosol collection and sampling logistics, and the Indian Meteorological Department (IMD; Punjabi University, Patiala in India) for providing the relevant meteorological parameters from the sampling site. Author thanks the reviewers for providing constructive comments and Prof. Junji Cao for editorial handling of this manuscript. PR is thankful to the Council of Scientific and Industrial Research (India) for providing CSIR-Senior Research Associate fellowship (CSIR-SRA Pool No # 8934-A/2017).

Compliance with Ethical Standards

Conflict of Interest

The author states that there is no conflict of interest.

Supplementary material

41810_2018_32_MOESM1_ESM.docx (172 kb)
Supplementary material 1 (DOCX 172 kb)

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Copyright information

© Institute of Earth Environment, Chinese Academy Sciences 2018

Authors and Affiliations

  1. 1.Department of Civil EngineeringIndian Institute of TechnologyKanpurIndia

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