# Aerosol chemistry, transport, and climatic implications during extreme biomass burning emissions over the Indo-Gangetic Plain

Nandita Singh, Tirthankar Banerjee, Made Raju, Karine Deboudt, Meytar Sorek-Hamer, Ram Singh, Rajesh Mall

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## To cite this version:

Nandita Singh, Tirthankar Banerjee, Made Raju, Karine Deboudt, Meytar Sorek-Hamer, et al. Aerosol chemistry, transport, and climatic implications during extreme biomass burning emissions over the Indo-Gangetic Plain. Atmospheric Chemistry and Physics, 2018, 18 (19), pp.14197-14215. 10.5194/acp-18-14197-2018. hal-04290035

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# Aerosol chemistry, transport, and climatic implications during extreme biomass burning emissions over the Indo-Gangetic Plain

Nandita Singh<fnref n="1" />, Tirthankar Banerjee<fnref n="1" />,<fnref n="2" />, Made P. Raju<fnref n="3" />, Karine Deboudt<fnref n="4" />, Meytar Sorek-Hamer<fnref n="5" />, Ram S. Singh<fnref n="2" />,<fnref n="6" />, and Rajesh K. Mall<fnref n="1" />,<fnref n="2" />

**Correspondence:** Tirthankar Banerjee (tirthankaronline@gmail.com, tb.iesd@bhu.ac.in)

Received: 4 May 2018 – Discussion started: 12 June 2018

Revised: 28 August 2018 – Accepted: 11 September 2018 – Published: 8 October 2018

**Abstract.** The large-scale emissions of airborne particulates from burning of agricultural residues particularly over the upper Indo-Gangetic Plain (IGP) have often been associated with frequent formation of haze, adverse health impacts, and modification in aerosol climatology and thereby aerosol impact on regional climate. In this study, short-term variations in aerosol climatology during extreme biomass burning emissions over the IGP were investigated. Size-segregated particulate concentration was initially measured and submicron particles (PM₁.₁) were found to dominate particulate mass within the fine mode (PM₂.₁). Particulate-bound water-soluble ions were mainly secondary in nature and primarily composed of sulfate and nitrate. There was evidence of gaseous NH₃ dominating neutralization of acidic aerosol species (SO₄<fnref n="2" />⁻) in submicron particles, in contrast to crustal-dominating neutralization in coarser particulates. Diurnal variation in black carbon (BC) mass ratio was primarily influenced by regional meteorology, while gradual increase in BC concentration was consistent with the increase in Delta-C, referring to biomass burning emissions. The influence of biomass burning emissions was established using specific organic (levoglucosan), inorganic (K⁺ and NH₄⁺), and satellite-based (UV aerosol index, UVAI) tracers. Levoglucosan was the most abundant species within submicron particles (649 ± 177 ng m⁻<fnref n="3" />), with a very high ratio (> 50) to other anhydrosugars, indicating exclusive emissions from burning of agriculture residues. Spatiotemporal

distribution of aerosol and a few trace gases (CO and NO₂) was evaluated using both spaceborne active and passive sensors. A significant increase in columnar aerosol loading (aerosol optical depth, AOD: 0.98) was evident, with the presence of absorbing aerosols (UVAI > 1.5) having low aerosol layer height (~ 1.5 km). A strong intraseasonality in the aerosol cross-sectional altitudinal profile was even noted from CALIPSO, referring to the dominance of smoke and polluted continental aerosols across the IGP. A possible transport mechanism of biomass smoke was established using cluster analysis and concentration-weighted air mass back trajectories. Short-wave aerosol radiative forcing (ARF) was further simulated considering intraseasonality in aerosol properties, which resulted in a considerable increase in atmospheric ARF (135 W m⁻<fnref n="2" />) and heating rate (4.3 K day⁻<fnref n="1" />) during extreme biomass burning emissions compared to the non-dominating period (56 W m⁻<fnref n="2" />, 1.8 K day⁻<fnref n="1" />). Our analysis will be useful to improve understanding of short-term variation in aerosol chemistry over the IGP and to reduce uncertainties in regional aerosol-climate models.

## 1 Introduction

Aerosols are studied systematically in terms of their potential to influence the transfer of radiant energy and distribution of latent heat, by which they modify the Earth's weather

<footnote n="1">¹Institute of Environment and Sustainable Development, Banaras Hindu University, Varanasi, India</footnote>

<footnote n="2">²DST-Mahamana Centre of Excellence in Climate Change Research, Banaras Hindu University, Varanasi, India</footnote>

<footnote n="3">³High Altitude Cloud Physics Laboratory, Indian Institute of Tropical Meteorology, Pune, India</footnote>

<footnote n="4">⁴Laboratoire de Physico-Chimie de l'Atmosphère, Université du Littoral Côte d'Opale, Dunkirk, France</footnote>

<footnote n="5">⁵NASA Ames Research Center, Moffett Field, CA, USA</footnote>

<footnote n="6">⁶Department of Chemical Engineering and Technology, Indian Institute of Technology (BHU), Varanasi, India</footnote>
and climate. Aerosols are also associated with nutrient re-
cycling and governing atmospheric chemistry (Kanakidou et
al., 2018). Aerosol interaction with radiation mainly consti-
tutes its radiative forcing of climate change (Bellouin et al.,
2005; Bond et al., 2013) while it also modifies the climate by
means of cloud formation processes (Seinfeld et al., 2016).
Aerosol–radiation interaction necessitates understanding of
spectrally varying aerosol optical properties, which are asso-
ciated with particle size distribution, chemical composition,
morphology, and mixing states. The representation of aerosol
processes in global and regional climate models varies con-
siderably and thereby estimates of aerosol–radiation inter-
action still contain significant uncertainties (Myhre et al.,
2013). This necessitates extensive regional investigation in
terms of aerosol composition and properties for improved
parametrization of aerosol schemes in the regional and global
climate model.

The Indo-Gangetic plain (IGP) in South Asia is especially
unique in terms of aerosol loading and diversity that varies
over the seasons (Singh et al., 2017a, b; Sen et al., 2017;
Sayer et al., 2014; Kumar et al., 2018). The IGP is often pro-
jected to be one of the most vulnerable region in terms of
aerosol-induced negative health impacts (Apte et al., 2015)
and therefore numerous observational and modeling studies
were performed for better characterization of aerosols (Sen et
al., 2017; Moorthy et al., 2008, and references therein). Re-
cently, Singh et al. (2017a) concluded the presence of spa-
tial and seasonal variations in aerosol sources over South
Asia, with vehicular emissions, followed by industrial emis-
sions and secondary aerosols, contributes most to fine par-
ticulates. Additionally, episode-specific emissions like from
biomass burning (Wan et al., 2017; Rajput et al., 2011, 2014;
Rajput and Sarin, 2014) and the use of fire crackers (Ku-
mar et al., 2016) also induce sudden large-scale changes in
aerosol properties and necessitate extensive investigation for
better representation in regional aerosol models. Post-harvest
agricultural residue burning, especially over the upper IGP,
is projected to release 400 Gg of particulate-bound organic
aerosol (OA) and 40 Gg of black carbon (BC; Rajput et al.,
2014), almost entirely (90%) from the burning of rice husks
(Rajput et al., 2011). The OA mostly constitutes the fine par-
ticulate mass (20%–90%) and is reported to be hydrophilic
in nature (Rajput and Sarin, 2014), therefore having the po-
tential to act as cloud condensation nuclei molecules or at
the most compete with sulfate particles (Singh et al., 2017b).
Nevertheless, the presence of such a huge amount of OA may
lead to either a reduction in mean evaporation and modify re-
gional precipitation or may reduce cloud formation processes
by inducing additional heat in the system (Riipinen et al.,
2011; Sun and Ariya, 2006). Biomass burning aerosols also
impact the Earth's surface albedo by depositing on glaciers.
The net radiative forcing of biomass burning aerosols by
aerosol–radiation interactions is close to neutral, i.e., –0.0
(–0.20 to +0.20) W m⁻², having a gradient with negative
forcing from OA and positive forcing from BC (Myhre et al.,

2013). Biomass burning aerosols even evolve due to oxidation (Jimenez et al., 2009; Vakkari et al., 2014), from gas-phase precursors to semi-volatile secondary OA (SOA) and finally to highly volatile oxidized gases (e.g., CO and CO₂), thus warranting molecular characterization and specific understanding in terms of composition, atmospheric chemistry, transport, and radiative forcing (Singh et al., 2017b).

Several investigations were made over the IGP to un-
derstand the characteristics of biomass burning aerosols. A
few attempts were made using solely ground-based informa-
tion, e.g., aerosol emission budget (Rajput et al., 2014), or-
ganic mass-to-organic carbon ratio (Rajput and Sarin, 2014),
emissions of PAHs (Rajput et al., 2011), organic molecu-
lar tracers (Wan et al., 2017; Li et al., 2014), and radiative
forcing (Sharma et al., 2017; Alam et al., 2011); however,
few have explored remote-sensing observations to interpret
fire (Vadrevu et al., 2012) and aerosol plume characteris-
tics (Kaskaoutis et al., 2014). However, there is a need to
integrate both ground and contemporary satellite-based in-
formation so that spatiotemporal characterization of aerosols
and their climatic impacts are assessed more realistically.
In the present analysis complementary measurements from
both ground- and space-based platforms are therefore com-
bined to trace the vital signatures of extreme biomass burn-
ing emissions and their chemical evolution, transport, and
aerosol radiative forcing. Initially, chemical speciations of
size-segregated aerosols are made, supported by BC dynam-
ics and molecular tracers of biomass burning emissions; they
are then further explored in terms of their relevance to re-
gional meteorology. The spatial extent of aerosol emission
and transport was obtained using Modern-Era Retrospec-
tive Analysis for Research and Applications (MERRA) at-
mospheric reanalysis data, Global Data Assimilation System
(GDAS) archives, and NCEP/NCAR Reanalysis data. Fur-
ther, visualization from the A-Train satellite constellation,
including spaceborne passive sensors like the Moderate Res-
olution Imaging Spectroradiometer (MODIS) and the Ozone
Monitoring Instrument (OMI) and an active sensor like the
Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Ob-
servation (CALIPSO), are included. Briefly, the results are
explored to highlight three exclusive but interrelated mecha-
nisms, i.e., aerosol chemistry, regional transport, and radia-
tive forcing, as well as their intraseasonal variations over the
middle IGP, which may well be useful in regional climate
models.

## 2 Experimental methods

2.1 Site description

Ground-based aerosol measurements were made at the in-
stitutional premises of Banaras Hindu University, Varanasi
(25.26° N, 82.98° E, 82 m a.m.s.l.). The ground station typ-
ically experiences a humid subtropical climate, with no lo-
calized effects of oceans or mountains (Fig. 1). The predom-
inating wind profile is northwesterly, which is projected to
subsidize over a section of the middle IGP, coinciding well
with the ground monitoring station, thereby facilitating grad-
ual accumulation of aerosols (Kumar et al., 2018). Interest-
ingly enough, the region also experiences a significant di-
urnal variation in atmospheric boundary layer (ABL) asso-
ciated with high convective turbulence that usually redis-
tributes aerosols to a greater height (M. Kumar et al., 2015,
2017a; R. Kumar et al., 2015). Particulates emitted from
crustal sources, road dust resuspension, vehicular exhaust,
and biomass/waste burning are often reported to constitute
the regional aerosols (Singh et al., 2017a).

## 2.2 Micrometeorology, ABL, and wind field

The 24 h average meteorological parameters, e.g., tem-
perature, relative humidity (RH), and wind speed (WS),
were obtained from Weather Underground (https://www.
wunderground.com/) and validated with regional weather
monitoring station data. The ABL heights at specific coordi-
nates were retrieved from Global Data Assimilation System
(GDAS) archives hosted at NOAA Air Resources Labora-
tory, which provides simulated meteorological observations
at a gridded scale. The 3-hourly ABL data (0.5°) were aver-
aged on a daily basis in parallel with the period of particulate
measurement. The NCEP/NCAR Reanalysis data were used
to analyze the variation in 3-D wind fields at the near surface
(1000 m) with a horizontal resolution of 2.5° × 2.5°. Vector
wind composite mean (m s⁻¹) for 925 hPa was plotted for
the defined coordinate (6–38° N, 50–105° E) to understand
the synoptic pattern of the wind field.

## 2.3 Ground-based measurements

### 2.3.1 Size-segregated aerosol mass concentration

Size-segregated aerosols were collected on the pre-
combusted quartz fiber filter using an Anderson eight-stage
cascade impactor (Tisch Environmental Inc., USA). Sam-
pling was continued once a week from 1 October to 15 De-
cember 2016, continuously for 72 h (each week) to obtain
representative deposition of particulates. The instrument was
run with a fixed flow rate of 28.3 L min⁻¹, having an aero-
dynamic cutoff diameter of <0.43, 0.65, 1.1, 2.1, 3.3, 4.7,
5.8, and >9.0 µm (with 50 % collection efficiency). The in-
dividual stages of each sample were then segregated into
three groups on the basis of cutoff diameter, with (i) coarse
mode (PM≥2.1) comprising the stages with an aerodynamic
diameter >2.1 µm, (ii) fine mode (PM1.1–2.1) for the stages
with a diameter of 1.1 to 2.1 µm, and (iii) submicron mode
(PM<1.1) for the last two stages with a diameter <1.1 µm.

### 2.3.2 Black carbon mass concentration

The BC real-time mass concentration was measured using
a seven-channel Aethalometer (model AE-42, Magee Scien-
tific Inc., USA), with a constant flow rate of 3 L min⁻¹ at
5 min resolution. The Aethalometer measures the attenuated
beam of light transmitted through aerosol samples on filter
tape at seven wavelengths (370, 470, 520, 590, 660, 880, and
950 nm), while attenuation at 880 nm was considered for BC
(Bodhaine, 1995). The BC concentration is estimated based
on the concept of linearity between the light attenuation and
BC mass deposited on the quartz filter. An absorption effi-
ciency of 16.6 m² g⁻¹ (provided by the manufacturer) was
used to measure BC after correction of loading effect. The
mechanism for estimation of BC is described in Wang et
al. (2011) and Kumar et al. (2017a). BC measured at two
wavelengths, e.g., 370 nm (indicating absorption by wood-
smoke particles) and 880 nm (by both fossil fuel and wood
burning emissions), was used to compute Delta-C (BC₃₇₀nm⁻
BC₈₈₀nm). Delta-C is reported to symbolize smoke emissions
(Wang et al., 2011; Kumar et al., 2016) and, therefore, was
used as a tracer for biomass burning emissions.

### 2.3.3 Aerosol chemical constituents

#### Water-soluble ions

The particulate deposits on filter were extracted with deion-
ized water in an ultrasonic bath (Microclean-109, Oscar, In-
dia) for 30 min, and extracts were further filtered through
syringe filters (pore size 0.2 µm). The water-soluble ionic
constituents (WSICs) were analyzed using an ion exchange
chromatograph (ICS 3000, Dionex, USA). For measurement
of anions (Cl⁻, NO₃⁻, SO₄²⁻, and PO₄³⁻), the ion chro-
matograph was equipped with a micromembrane suppres-
sor (AERS-300, 4 mm; Dionex) with an IonPac analytical
column (AS11-HC × 250 mm) connected with a guard col-
umn IonPac (AG11-HC, 4 × 50 mm; Dionex). Cations (NH₄⁺,
Na⁺, K⁺, Mg²⁺, Ca²⁺) were measured through a suppressor
(CERS-300, 4 mm; Dionex) with an analytical column (Ion-
Pac CS12A-HC, 4 × 250 mm; Dionex) and a guard column
(IonPac CG11-HC, 4 × 50 mm; Dionex, USA). The back-
ground contamination was removed by subtracting the blank
filter value from sample values (Kumar et al., 2017b).

#### Trace metals

The trace metals were extracted from filter discs as per
U.S. EPA Method IO-3.2 (EPA, 1999). The filters were cut
into pieces and digested in an acid mixture solution (5.55 %
HNO₃ with 16.67 % HCl) on a hot plate for 2 h. The extracts
were filtered, stored at 4 °C, and analyzed with an atomic ab-
sorption spectrophotometer (Avanta version 2.01, GBC) for
Ca, Na, K, Cu, Mn, Fe, Cd, Cr, Pb, Ni, Co, and Zn.
<figure><img src="image_5.png" /><figcaption>**Figure 1.** Geographical location of the aerosol ground monitoring station (a) and MODIS aerosol optical depth with NCEP/NCAR composite means of wind vectors during the monitoring period (b).</figcaption></figure>

## Organic compounds

For determining the aerosol organic constituents, the filter composites of each group were extracted by ultrasonicating the filters initially with a dichloromethane-hexane mixture (1 : 1), followed by a dichloromethane-methanol mixture (1 : 1). Both solvent extracts were combined and concentrated using a vacuum rotatory evaporator and nitrogen evaporator to a volume of 100 µL (Hu et al., 2013). The extracts were derivatized by silylation with N, O-bis-(trimethylsilyl)-trifluoroacetamide and 1% trimethylchlorosilane prior to analysis. After derivatization, the residue was redissolved in hexane and analyzed with a gas chromatograph-mass spectrometer (GCMS-QP2010 Ultra, Shimadzu, Japan) equipped with a Rxi-5ms fused silica capillary column having the dimensions 30 m × 0.25 mm ID × 0.25 µm (Restek, Bellefonte, PA, USA). The sample was injected in the GC-MS at 260 °C injector temperature in splitless mode. The column oven temperature program was started at 50 °C with 2 min of the isothermal hold, which was further increased to 120 °C (linear elevation at 30 °C min⁻¹) and 300 °C (linear elevation at 6 °C min⁻¹) followed by an isothermal hold of 11 min. The electron impact ionization was used to produce molecular ions at 70 eV with the ion source and interface temperatures of 230 and 270 °C. The molecular ions were scanned for a wide range of *m/z* values from 40 to 650. The target compounds were identified based on retention time and fragmentation pattern from the National Institute of Standards and Technology (NIST) library and standard solutions of analytes. The average recoveries (relative standard deviation, RSD) of the *n*-alkanes (28 compounds) varied from 72 % to 92 % (1 %–12 %), 75 % to 88 % (2 %–7 %) for phthalates (six compounds), 74 % to 92 % (1 %–9 %) for FAMES, 73 % to

93 % (1 %–10 %) for PAHs, and 75 % to 80 % (4 %–6 %) for anhydrosugars (three compounds).

## 2.4 Satellite-based observations

### 2.4.1 Aqua and Terra MODIS data

The aerosol optical depth (AOD) at 550 nm was retrieved daily from MODIS onboard the Aqua satellite in parallel to ground-based aerosol monitoring. The level 2 collection 6 AOD at 10 km resolution was retrieved using MODIS merged DT-DB AOD (AOD_550_-Dark_Deep Bluelive; Levy et al., 2013). The selection of merged DT-DB for retrieving AOD was based on higher retrieval number and accuracy across the IGP (Mhawish et al., 2017). The AOD over the ground station was calculated as the average of 5 × 5 pixels, surrounding the monitoring site. The Ångström exponent (AE, α) was retrieved using MODIS C6 level 2 DB AOD and the relation between AOD and AE was used to measure the aerosol loading and the particle size (M. Kumar et al., 2015; Mhawish et al., 2017). Columnar water vapor content (CWV) was retrieved from Aqua MODIS collection 6 level 2 infrared channel at 1 km spatial resolution. To illustrate the impact of biomass burning, the fire spots were retrieved over the IGP from Aqua and Terra MODIS fire mapper product (collection 6, spatial resolution 1 × 1 km²) provided by the Fire Information for Resource Management System (FIRMS, https://firms.modaps.eosdis.nasa.gov, last access: 2 August 2017). Details about MODIS fire products and their algorithms can be found elsewhere (Justice et al., 2006).
## 2.4.2 Aura OMI and MERRA-2 reanalysis data

OMI onboard the AURA satellite has a typical daily global coverage with 13 × 24 km² spatial resolution at the nadir and measures solar backscatter irradiation in the UV-visible spectrum (264–504 nm; Levelt et al., 2006). Ultraviolet aerosol index (UVAI), tropospheric NO₂, total columnar ozone (TCO), and single-scattering albedo (SSA) were retrieved from Aura OMI available at NASA Goddard Earth Sciences Data and Information Services Centre (GES DISC). OMI UVAI is capable of detecting aerosol absorption from satellite measured radiances without any prior assumption on aerosol composition (Torres et al., 2013). It is a qualitative parameter and is widely used to identify the UV absorbing aerosols (e.g., smoke plumes, soot, and mineral dust; Torres et al., 2013; Mhawish et al., 2018). The UVAI based on the OMI near-UV aerosol retrieval algorithm (OMAERUV) was extracted from the level 2G, version 003 aerosol product containing 1 day's level 2 dataset of original pixels (13 × 24 km²) into 0.25° × 0.25° grids. The NO₂ tropospheric column density was retrieved from the cloud-screened (cloud fraction < 30 %) level 3, version 003, daily 0.25° × 0.25° grid-ded OMNO2d product (Krotkov et al., 2017). To estimate TCO, level 3e data (OMDOAO3) at a spatial resolution of 0.25° × 0.25° were used. SSA at 550 nm was retrieved from the OMI level 2G product (OMAERUV) at 0.25° × 0.25° resolution. The carbon monoxide (CO) surface concentration (in parts per billion by volume) was retrieved from Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) atmospheric reanalysis data available at 0.5° × 0.625° resolution from GES DISC.

## 2.4.3 CALIPSO–CALIOP observations

CALIPSO products were used to examine the vertical distribution of aerosols, altitude of aerosol layers, clouds, aerosol types, and their properties at visible (532 nm) and near-IR wavelengths (1064 nm). The v4.10 CALIOP Level 2 altitude-orbit cross-section profiles retrieved from the CALIPSO sub-setting web application (https://www-calipso.larc.nasa.gov, last access: 9 August 2017) were used. The lidar profiles were processed for images of vertical feature masks, aerosol subtypes, and extinction coefficients (at 532 nm) at 30 m vertical resolution over the selected grid (80–86° N and 22–28° E). Details about data products, calibration, and uncertainty are discussed in Rogers et al. (2011).

## 2.5 Air mass back trajectory

The NOAA HYSPLIT model (Draxler and Rolph, 2003) was used to simulate particle back trajectories in a three-dimensional system. HYSPLIT was run using the Global Data Assimilation System data (GDAS, 0.5° × 0.5°) available from the archive dataset (http://ready.arl.noaa.gov/gdas1.php) to predict 120 h air mass back trajectories (00:00,

06:00, 12:00, and 18:00 IST, Indian standard time) starting from October to December 2016. Trajectories for different aerosol loading periods were then overlaid on the MODIS fire map to study the transboundary movement of emissions from biomass burning. The trajectory analysis was made using the GIS-based software TrajStat (Wang et al., 2009). Concentration-weighted trajectories (CWTs) were also drawn considering columnar aerosol loading to evaluate potential aerosol source fields and mechanisms of aerosol transport. The specificities of the models’ parameters and algorithms are detailed elsewhere (Wang et al., 2009; Kumar et al., 2018).

## 2.6 Aerosol optical properties, radiative forcing, and heating rate

Aerosol-induced shortwave (0.2–4.0 µm) direct radiative forcing (ARF) was estimated using the Santa Barbara DISORT Atmospheric Radiative Transfer (SBDART) model (Ricchiazzi et al., 1998). SBDART estimates plane-parallel radiative transfer under clear-sky conditions for both Earth’s top of the atmosphere (TOA) and at the surface (SUF), while atmospheric forcing (ATM) is calculated as the difference between them. The standard atmospheric profile is used together with input variables, e.g., AOD, SSA, CWV, TCO, and an asymmetry parameter (ASP) derived through the OPAC model (Optical Properties of Aerosols and Clouds; Hess et al., 1998). OPAC provides aerosol optical properties over a wide range of wavelengths and delivers necessary input to SBDART. Mean mass concentrations of aerosol water soluble (WSIC) and insoluble (dust and organics) components along with BC mass concentration were converted to particle number densities and introduced to OPAC to derive aerosol optical properties. The AOD and SSA were reconstructed to match modeled and satellite-derived values within ±5 % deviation.

The weekly mean values of AOD, SSA, ASP, CWV, TCO, visibility, and AE were included as inputs to SBDART. SBDART includes multiple scattering in a vertically inhomogeneous, non-isothermal plane-parallel media and is reported to be efficient in resolving the radiative transfer equation (Raju et al., 2016). The ARF was calculated using 10 solar zenith angles (0 to 89, with increments of 10) and was used for conditions like “with aerosols” or “without aerosols”. The surface albedo was decided based on visual observation considering a combination of snow, ocean, sand, and vegetation. Overall uncertainty in the estimated ARF was in the range of 10 %–15 % (Alam et al., 2011). The ATM–ARF was further used to compute atmospheric heating rate ($\partial T/\partial t$, K day⁻¹), using Eq. (1):

$$ \partial T / \partial t = (g / C_p) \cdot (\Delta F / \Delta P), \qquad (1) $$

where $\Delta P$ is the difference in forcing, $\Delta P$ is the pressure difference between the top and bottom boundary layer, $C_p$ is
<figure><img src="image_6.png" /><figcaption>**Figure 2.** Time series of (a) size-segregated particulate mass concentration, (b) particle ratio, and (c) daily means of meteorological variables.</figcaption></figure>

specific heat capacity of air at constant pressure, and *g* is the acceleration due to gravity (Kumar et al., 2017a).

# 3 Results and discussion

## 3.1 General characteristics of aerosols

The weekly variation in particulate concentrations in different size fractions is presented in Fig. 2 with the descriptive statistics included in Supplement Table S1. The total aerosol mass concentration has high intraseasonal variations (median: 370; range: 134–734 µg m⁻³), mainly influenced by coarse-mode particles (PM>2.1) contributing 63 ± 15% of particulate mass. In contrast, the contribution of submicron (PM<1.1: 27 ± 12%) and fine-mode particles (PM1.1–2.1: 10 ± 4%) to total aerosol loading was relatively less (<37%). The average (±1σ) mass concentration of PM2.1 (PM<1.1 + PM1.1–2.1) and total aerosol loading was 162 (±123) and 390 (±199) µg m⁻³, which were approximately 98% (against PM2.5) and 92% higher compared to annual averages observed over the monitoring station (Murari et al., 2017; Prajapati and Tripathi, 2008). To our knowledge, until submission of the paper, there was no published report on submicron particles over the ground station. Time series analysis of size-segregated particulates (Fig. 2) indicates the submicron (PM<1.1) and fine-mode particles (PM1.1–2.1) only had a late rise in mass concentration, while the coarse-mode particulates (PM>2.1) did not show any trend. However, there was a definite increasing pattern in fine-to-coarse particle ratio (PM2.1/PM>2.1; mean: 0.7 ± 0.5; range: 0.2–1.5) due to a continuous increase in the fine mode from mid-November to the end of monitoring. Thus, the contribution of fine-mode particles to total aerosol loading increased from mid-November (>40%) and contributed almost 60% of particulate mass

during the month of December. The submicron particles also indicate a high median concentration (96 µg m⁻³) compared to the fine mode (33 µg m⁻³), and the particle ratio (PM<1.1/PM1.1–2.1) remains > 1 throughout, only to exceed values > 2.5 from November to December. This clearly indicates the dominance of submicron particles within fine-mode fractions, possibly associated with anthropogenic emissions and also influenced by local meteorological conditions, e.g., low temperature (mean ± SD: 20 ± 3 °C), calm wind (mean: 0.6 m s⁻¹), and shallow boundary layer height (mean ± SD: 379 ± 89 m).

## 3.2 Aerosol chemical speciation

### 3.2.1 Water-soluble inorganic species (WSIS)

Temporal variation in WSIS in size-segregated airborne particulates is presented in Fig. 3a. It indicates the major contribution of WSIS to submicron (21%) and fine-particle mass (21%) compared to coarser particles (13%). Secondary inorganic aerosols (SIA = SO₄²⁻ + NO₃⁻ + NH₄⁺) together accounted for 17% of the submicron particle mass, with major contributions from sulfate (9%) and nitrate (6%). Similarly, the case for fine particulates as SIA contributed to almost 17% of aerosol mass with predominant contributions from sulfate (8%) and nitrate (6%) and a relatively small proportion of ammonia (4%). In contrast, the relative contribution of SIA to coarse particulate was lower (7%), also primarily associated with sulfate (5%) and nitrate compounds (2%). This indicates the secondary nature of the origin of fine and submicron particles, which possibly evolve through gas-phase photochemical conversion of SO₂ and NO₂, eventually neutralized by crustal species like carbonate salts (CaCO₃ and MgCO₃) associated with airborne dust. The time series of SIA contribution to particulate mass (Fig. 3a) indi-
<figure><img src="image_7.png" /><figcaption>Figure 3. Variation in (a) ions and (b) trace metals in different aerosol size fractions.</figcaption></figure>

cate a dominance (although in different extent) of secondary aerosols in PM<sub>1.1–2.1</sub> and PM<sub>&ge; 2.1</sub> only during November.

Among the WSIS, SO<sub>4</sub><sup>2-</sup> was invariably the most abundant species within each particulate size fraction (PM<1.1: 39%, PM<sub>1.1–2.1</sub>: 32%, PM<sub>&ge; 2.1</sub>: 36%), followed by NO<sub>3</sub><sup>-</sup> (PM<1.1: 27%, PM<sub>1.1–2.1</sub>: 29%, PM<sub>&ge; 2.1</sub>: 17%). The NO<sub>3</sub><sup>-</sup>/SO<sub>4</sub><sup>2-</sup> ratio was considered to be an indicator of the mobile and stationary source contribution to nitrogen and sulfur (Tian et al., 2016). An average ratio varying from 0.62 to 1.92 was noted for all size-segregated particulates, testifying dominance of both sources, although on different timescales. In the later phase, the ionic ratio (NO<sub>3</sub><sup>-</sup>/SO<sub>4</sub><sup>2-</sup>) was enhanced (&gt; 1) in submicron and fine-mode particles, identical to the reported haze events over Guangzhou (Tan et al., 2009) and Suzhou, China (Tian et al., 2016). A very high NO<sub>3</sub><sup>-</sup>/SO<sub>4</sub><sup>2-</sup> ratio (3.2 &plusmn; 1.3) was only noted in fine aerosols during October, mainly due to a lower concentration of sulfate. The next two dominant contributors to WSIS were NH<sub>4</sub><sup>+</sup> (PM<1.1: 14%, PM<sub>1.1–2.1</sub>: 19%, PM<sub>&ge; 2.1</sub>: 5%) and K<sup>+</sup> (PM<1.1: 8%, PM<sub>1.1–2.1</sub>: 5%, PM<sub>&ge; 2.1</sub>: 2%), both considered to be molecular tracers for biomass burning emission (Banerjee et al., 2015). They constitute the greater proportion of WSIS in PM<1.1 and PM<sub>1.1–2.1</sub>, especially from the last week of October until the end of November, signifying an elevated contribution of biomass/agro-residue burning emissions to these particle sizes. Further, a strong correlation (R<sup>2</sup> = 0.9) between NH<sub>4</sub><sup>+</sup> and SO<sub>4</sub><sup>2-</sup> and high NH<sub>4</sub><sup>+</sup>/SO<sub>4</sub><sup>2-</sup> equivalent ratio (0.9 &plusmn; 0.2) for submicron particulates indicate the abundance of gaseous NH<sub>3</sub> to neutralize acidic species (SO<sub>4</sub><sup>2-</sup>) by forming (NH<sub>4</sub>)<sub>2</sub>SO<sub>4</sub> and/or NH<sub>4</sub>HSO<sub>4</sub>. The NH<sub>4</sub><sup>+</sup>/SO<sub>4</sub><sup>2-</sup>

equivalent ratio gradually increased from week 5 (mean: 1.2; range: 0.9–1.3), possibly due to abundant emission of NH<sub>4</sub><sup>+</sup> from biomass burning emissions. Unlike submicron particles, the low NH<sub>4</sub><sup>+</sup>/SO<sub>4</sub><sup>2-</sup> equivalent ratios (&lt; 0.7, mean: 0.4) in coarse-mode particles indicate the predominant neutralization by crustal minerals.

Unlike the other WSISs, Na<sup>+</sup> and Ca<sup>2+</sup> were found to contribute the most to PM<sub>&ge; 2.1</sub> (Na<sup>+</sup>: 2%; Ca<sup>2+</sup>: 3%), indicating their crustal origin. The relative abundance of Cl<sup>-</sup> in size-segregated aerosols was roughly equal for each size fraction, contributing almost identically to total WSIS in PM<sub>&lt; 1.1</sub> (6%), PM<sub>1.1–2.1</sub> (5%), and PM<sub>&ge; 2.1</sub> (4%). The possible origin of Cl<sup>-</sup> in PM<sub>&ge; 2.1</sub> could be the aged sea salt, transported from the Bay of Bengal, but its association with PM<sub>&lt; 1.1</sub> was most likely due to biomass burning emissions (Pavuluri et al., 2011; Murari et al., 2015). The temporal variations in WSISs in all particulate size fractions were consistent except for Mg<sup>2+</sup> and PO<sub>4</sub><sup>3-</sup> contributing < 0.2 % of particulate mass and having non-biomass-specific emission sources. A strong correlation between the anion and cation equivalents within all the size groups (0.7–0.9) indicates that most ions were from the filter samples. The total ion equivalent ratio (anions to cation) refers to a cationic imbalance (PM<sub>&lt; 1.1</sub>: 1.2, PM<sub>1.1–2.1</sub>: 0.8 and PM<sub>&ge; 2.1</sub>: 0.6) with excess cations in fine- and coarse-mode particles, possibly due to unmeasured components like carbonates and bicarbonates.
<figure><img src="image_8.png" /><figcaption>**Figure 4.** Variation in BC, Delta-C, and ABL during the entire monitoring period.</figcaption></figure>

### 3.2.2 Trace metals

Total metallic contribution to particulate mass was found at a maximum in PM<sub>1.1–2.1</sub> (24 %), followed by PM<sub>&gt; 2.1</sub> (11 %) and was the least in PM<sub>&lt; 1.1</sub> (7 %, Fig. 3b). The most abundant elements were Na, Ca, K, and Zn for all size fractions, contributing 90%–98% of total identified metals, while the remaining fractions were primarily constituted by Fe (1%–10 %). Within the detectable level of metals, Ca and Na share 88% of metal concentrations in PM<sub>&lt; 1.1</sub> and 7% of submicron particulate mass, without having any specific temporal trend. However, Ca and Na were found to be high in PM<sub>1.1–2.1</sub> (Ca: 10 %; Na: 7 %), indicating their origin from resuspension of crustal materials and road dust.

There is little evidence of trace metal emissions from burning of biomass. Wang et al. (2015) have concluded that biomass combustion is the most prominent source of Fe concentration in submicron particles. For this analysis, although Fe was measured at a maximum in PM<sub>&gt; 2.1</sub>, the relative increase in Fe concentration in submicron (PM<sub>1.1</sub>: 59 %) and fine aerosols (PM<sub>1.1–2.1</sub>: 415 %) during week 6 to week 9 possibly indicates the added contribution of biomass burning emissions. In addition to Fe, there are also reports of trace metal emissions, particularly K, Cu, S, Zn, and Pb, from burning of rice straw (Ryu et al., 2004); organic bound Fe<sup>2+</sup>, Cu<sup>2+</sup>, Ni<sup>2+</sup>, and Zn<sup>2+</sup> from hardwood burning (Chang-Graham et al., 2011); and Cu, Pb, Ni, and As from the burning of biomass fuel (Zhang et al., 2014). In our case, a massive increase in K (PM<sub>1.1</sub>: 528 %; PM<sub>1.1–2.1</sub>: 119 %) was also noted between weeks 6 and 9. This contrasted with coarse-

particle-bound Fe and K, which are primarily of crustal origin (Banerjee et al., 2015), and we recorded only a 15 % (Fe) and 83 % (K) increase in concentration within weeks 6 to 9. Zinc was found to be considerably high in PM<sub>&gt; 2.1</sub> (3 %) and relatively low in PM<sub>1.1–2.1</sub> (2 %). The major sources of atmospheric Zn are burning of residual oil, refuse, and garbage (Gonzalez et al., 2016), which possibly leads to higher mass fractions in coarser particulates. Here, a relatively high Zn concentration was noted in the later phase of monitoring irrespective of particulate size coinciding with the winter-specific burning of waste refuse (Kumar et al., 2017b). The relative contribution of the rest of the trace metals (e.g., Mn, Pb, Cd, Ni, Cu, Cr, and Co) to particulate mass was insignificant (<0.05 %), without having any specific temporal pattern.

### 3.3 Characteristics of BC mass loading

Daily means of BC concentration and Delta-C (BC<sub>370–BC<sub>880</sub></sub>) are plotted in Fig. 4 with some data gaps. The 24 h average BC concentration varied from 2.0 to 15.4 µg m<sup>-3</sup> with a seasonal mean (±1σ) of 8.3 (±2.9) µg m<sup>-3</sup>. The season-specific BC average was 80 % higher in comparison to the annual mean (4.6 µg m<sup>-3</sup>; Kumar et al., 2017a), while there were also reports of winter-specific very high BC concentration (22 µg m<sup>-3</sup>; Murari et al., 2016) that usually persisted over the region. A distinct diurnal profile with a high BC concentration during nighttime (>9 µg m<sup>-3</sup>; 23:00–07:00 IST) and low daytime concentrations (<6 µg m<sup>-3</sup>, 11:00–17:00 IST) was also noted. A gradual rise in BC mass
<figure><img src="image_9.png" /><figcaption>**Figure 5.** Size-segregated particulate-bound (a) organic aerosols and the difference in the molecular compositions of (b) *n*-alkanes and (c) fatty acids.</figcaption></figure>

loading from 17:00 IST could be mainly attributed to the regional meteorology, which is reported as the most influential factor in regulating BC concentrations compared to anthropogenic sources (R. Kumar et al., 2015; M. Kumar et al., 2017). Boundary layer height and transport are two fundamental processes that influence the diurnal BC variation, mainly by means of regulating horizontal and vertical transport (R. Kumar et al., 2015). Therefore, the diurnal variation in BC concentration was the consequence of change in boundary layer height with occasional inflow of emissions from large-scale burning of agriculture residues–biomass–waste and from vehicular emissions.

The BC time series shows an enhanced BC concentration from the end of October (week 4) until the end of November (week 9). Such an increase in BC concentration was, however, possibly due to increased source strength as there was no significant variation in meteorological variables (like ABL) within this timeframe (Table S1). To understand the variation in BC sources, 24 h average Delta-C concentration (mean ± SD: 2.3 ± 1.0 µg m⁻³) is also included in Fig. 4, which refers to the emission of smoke particles (Wang et al., 2011; Kumar et al., 2016). Except for a few exceptions, high Delta-C (> 2.3 µg m⁻³) was observed particularly in the month of November (80% of days) compared to October (23%) and December (46%), referring to the added contribution of biomass burning emissions.

## 3.4 Composition of organic aerosols

Size-segregated particle-bound OAs were analyzed for 22 *n*-alkanes (C₁₃-C₃₄), three anhydrosugars (levoglucosan, mannosan, and galactosan), four PAHs, and 10 *n*-alkanoic acids (C₁₂-C₂₆) (Fig. 5). Considerable variation in the concentration and size distributions of these OAs was noted. Contributions of OA to size-segregated particulates were rela-

tively low because of partial characterization through GC-MS. Among the identified species, *n*-alkanes were invariably the highest within PM<1.1 (mean ± SD: 484 ± 103 ng m⁻³) compared to fine-mode (267 ± 43 ng m⁻³) and coarse-mode aerosols (308 ± 93 ng m⁻³). The molecular distribution of *n*-alkane homologues in all three size fractions showed a slight dominance of odd-numbered *n*-alkanes. The CPI (Carbon Preference Index) remains close to unity (CPI range: 1.2–2.1; mean ± SD: 1.5 ± 0.5), indicating dominance of anthropogenic emissions like combustion of fossil fuels and biomass burning. The higher-molecular-weight homologue (> C₂₅) concentration was found to be the highest in PM<1.1 with an oscillating pattern, having an odd molecule concentration higher than the adjacent even molecules (Fig. 5b). In contrast the low-molecular-weight homologues (< C₂₅) showed no such specific pattern of odd–even dominance. The sources of higher homologues (C₂₇, C₂₉, and C₃₁) are probably the surface-deposited plant litter for coarse-mode aerosols and biomass burning for fine-mode aerosols, while low-molecular-weight homologues (< C₂₅) primarily originate from fossil fuel combustion (Kang et al., 2016). Saturated fatty acids were found to constitute a larger fraction of solvent-extractable organics within coarse-mode particles (439 ± 38 ng m⁻³) and submicron particles (357 ± 162 ng m⁻³) in comparison to fine-mode particles (171 ± 57 ng m⁻³). For all three size fractions, total low-molecular-weight fatty acid (≤ C₂₀) concentration was found to be higher than the high-molecular-weight fatty acids (≥ C₂₀), implicating anthropogenic emissions like vehicular, residential biomass burning, and energy practices. The presence of high concentrations of C₁₂ and C₁₅ implicates the dominance of cooking oil combustion. The high concentration of C₂₂ further suggests the influence of biomass burning, which potentially emits both high and low fatty acids (Mochida et al., 2007). The fatty acid amide was found in trace amounts and
could possibly be derived from fatty acid and ammonia dur-
ing burning processes. The presence of PAHs was also mea-
sured for size-segregated particulates and was found to be
the highest in submicron particulates (7.0 ng m⁻³) compared
to PM₁.₁–₂.₁ (3.6 ng m⁻³) and PM> 2.₁ (3.1 ng m⁻³). PAHs
are mainly produced due to incomplete combustion of fu-
els like fossil fuels and biomass at relatively high tempera-
tures (Singh et al., 2017b; Chen et al., 2015). There was no
previous report of submicron particulate-bound PAHs during
biomass burning emissions over the IGP to compare, except
for PM₂.₅-bound PAHs in Patiala (27–40 ng m⁻³; Rajput et
al., 2011, 2014), Agra (9 ng m⁻³; Villalobos et al., 2015), and
Kanpur (3 ng m⁻³; Villalobos et al., 2015) and total-aerosol-
bound PAHs in Kathmandu (320 ng m⁻³, Chen et al., 2015).

Levoglucosan was found to be the most abundant anhy-
drosugar in submicron particles with an average (±1σ) of
649 (±177) ng m⁻³. In contrast, concentration in fine (229 ±
87 ng m⁻³) and coarse particles (162 ± 68 ng m⁻³) was rela-
tively low, indicating the influence of biomass burning emis-
sions for submicron particles. Levoglucosan concentration
measured in this study is comparable to other reported ob-
servations, especially with the cases that have accounted for
the influence of biomass burning emissions, e.g., New Delhi
(1978 ng m⁻³, Li et al., 2014), Mt. Tai, China (391 ng m⁻³,
Fu et al., 2008), Gent, Belgium (477 ng m⁻³, Zdrahal et al.,
2002), Lumbini, Nepal (734 ng m⁻³, Wan et al., 2017), and
Beijing, China (590 ng m⁻³, Cheng et al., 2013). In addition
to levoglucosan, the relative concentration of other anhy-
drosugars (mannosan and galactosan) in all size-segregated
aerosols was negligible (< 70 ng m⁻³, not shown).

3.5 Signature of biomass burning emissions

Biomass primarily consists of different biopolymers (e.g.,
cellulose, hemicellulose, lignin, suberin, sporopollenin, and
chitin) with a small proportion of lipids and terpenoids. Dur-
ing thermal combustion, such biomass emits different types
of organic molecules, some of which have the potential to
be considered a signature molecule based on their long resi-
dence time and chemical stability (Banerjee et al., 2015). The
major combustion product of cellulose and hemicellulose in-
cludes anhydrosugars like levoglucosan (1,6-anhydro-β-D-
glucopyranose, C₆H₁₀O₅) and its two isomers (mannosan
and galactosan). Among these, levoglucosan is a robust and
widely used tracer for biomass burning emissions, both glob-
ally (Simoneit et al., 1999; Schkolnik et al., 2005; Cheng et
al., 2013) and over the IGP (Li et al., 2014; Banerjee et al.,
2015; Wan et al., 2017). In our case, levoglucosan was abun-
dant in submicron particles with a peak during November
(weeks 6 to 9; Fig. 6). The rise in concentration was universal
in each particulate size fraction, but typically in submicron
(837 ± 83 ng m⁻³) and fine particulates (311 ± 47 ng m⁻³),
having a 54%-70% rise compared to the rest of the mon-
itoring period. This could correspond to a short-term varia-
tion in emissions source strength, which possibly influenced

the aerosol properties. A ratio between levoglucosan and the
rest of the anhydrosugars (mannosan and galactosan) was
also considered to indicate the dominating type of biomass
burning, with a ratio < 10 specific for softwood combustion
and > 10 for burning of hardwood and crop residue (Cheng
et al., 2013). Even a ratio > 40 was reported from physical
experiments using rice straw, wheat straw, and maize stalks
(Engling et al., 2009). Although the presence of mannosan
and galactosan was not frequent in our case, an overall ratio
> 50 indicates the exclusive dominance of agriculture residue
burning across the IGP.

The possibility of considering K⁺ and NH₄⁺ as biomass
burning tracers was investigated in terms of their associa-
tion with levoglucosan for submicron and fine particulates.
In general, the temporal trend in levoglucosan coincided
well with both K⁺ and NH₄⁺, and all these tracers regis-
tered a gradual rise in concentration during November. A
highly significant correlation (R²) between levoglucosan and
K⁺ (PM₁.₁: 0.80, PM₁.₁–₂.₁: 0.76; *p* < 0.01), and levogl-
ucosan and NH₄⁺ (PM₁.₁: 0.95, PM₁.₁–₂.₁: 0.60; *p* < 0.01), was
noted at the 99 % confidence interval. That definitely indi-
cates that levoglucosan, K⁺, and NH₄⁺ have similar biogenic
sources over the IGP that predominately contribute to the
aerosol loading, especially in PM₁.₁ and PM₁.₁–₂.₁. The re-
lation between levoglucosan and K⁺ and NH₄⁺ further ap-
peared to be nonlinear, with an exponential fit for submi-
cron (R²: 0.84, 0.94) and for fine particulates (R²: 0.83,
0.65). Nonlinear correlations between levoglucosan and K⁺
are also reported in the Amazon (Schkolnik et al., 2005) and
in Beijing (Cheng et al., 2013) during extreme biomass burn-
ing emissions. There was also evidence that NH₄⁺ was bet-
ter associated with levoglucosan compared to K⁺, indicat-
ing the presence of additional K⁺ sources across the region
(like fireworks; Kumar et al., 2016). However, in the absence
of aerosol organic carbon content, contribution of biomass
burning to aerosol mass was not computed.

In addition to using conventional biomass burning tracers, we also evaluated the association of submicron and fine-particulate-bound levoglucosan with weekly averages of Delta-C and UVAI (Fig. 6). Both Delta-C and UVAI are the measures of identifying the relative dominance of absorbing aerosols. In all scenarios, a significant correlation (R²) was noted between levoglucosan with Delta-C (0.65, *p* < 0.01) and UVAI (0.66, *p* < 0.01). In addition to the ground-based aerosol measurement, dynamic profiles of trace gas concentration, especially for trace gases that behave as aerosol precursors, are assessed from the Real-time Air Quality Data inventory of the Central Pollution Control Board (https://app.cpcbccr.com/ccr, last access: 12 March 2018). The hourly average concentrations of individual trace gases were initially checked for data quality and outliers and further averaged to 24 h. No such universal trend in concentration of all the trace gases was evident, except an overall increasing trend for NO, NO₂, NOₓ, and CO, while SO₂ remained stable and there was a negative trend for O₃. The most striking feature
<figure><img src="image_10.png" /><figcaption>**Figure 6.** Temporal variation in trace gases and biomass burning signature molecules ($NH_4^+$, $K^+$, levoglucosan) and their associations within different aerosol size fractions.</figcaption></figure>

was an increase in concentration particularly during November, although of a different magnitude. This was also evident in the variation in particulate-bound biomass tracers, which inspired us to consider two different aerosol loading scenarios, namely scenario 1 for the biomass-burning-dominated period (weeks 6 to 9, BDP) and scenario 2 for the less dominant biomass burning period (weeks 1–5 and weeks 10–11, BLDP). Such classification was intended to recognize if there was any variation in aerosol source fields over the IGP and in aerosol-induced radiative forcing.

## 3.6 Spatiotemporal nature of aerosol columnar properties

Spatiotemporal variations in aerosol columnar properties and trace gases are plotted in Fig. 7a, including the daily variations at the ground station (Fig. 7b). Instead of considering the columnar properties for the entire season, spatial plots are generated for two different scenarios like BDP and BLDP.

The spatial pattern in aerosol columnar properties was typical, having a very high aerosol loading exclusively over the IGP (area-weighted AOD mean ± SD: 0.55 ± 0.21) in comparison to the rest of South Asia (0.31 ± 0.21). However, there was no such temporal variation particularly over the IGP as both $BDP_{AOD}$ (0.56 ± 0.23) and $BLDP_{AOD}$ (0.53 ± 0.23) were similar. The $BDP_{AOD}$ was slightly higher (12%) than that of the reported decadal average (0.50 ± 0.25; Kumar et al., 2018) and was comparable to the season-specific average over the IGP (0.55 ± 0.20; Kumar et al., 2018). It should be noted that the area-weighted AOD average includes all the pixels retrieved across the region, some of which may not represent the biomass burning emissions. This leads us to further re-

trieve and compare AOD particularly over the ground station. In this case, the mean AOD was significantly high during the post-monsoon season (0.81 ± 0.39), 44% higher for $BDP_{AOD}$ (0.98 ± 0.42) with respect to $BLDP_{AOD}$ (0.68 ± 0.32). The $BDP_{AOD}$ was 46% higher compared to the decadal average for the station (0.67 ± 0.28; Kumar et al., 2018). Figure 7a also includes a comparison of the relative dominance of aerosol types in terms of AE, and in both conditions fine particles (AE; BDP: 1.5, BLDP: 1.7) were found to dominate with a season-specific mean (±1σ) of 1.6 (±0.2).

Following the evidence of persisting high AOD and high AE indicating dominance of fine particulates of anthropogenic origin, the nature of aerosols in terms of absorbing and/or scattering was distinguished using OMI UVAI. UVAI has been widely used to detect dust (Badarinath et al., 2010), biomass burning aerosols (Torres et al., 2013; Kaskaoutis et al., 2014), and soot particles (Kumar et al., 2016), and has also been used in combination with CALIPSO to detect the height of the aerosol layer (Guan et al., 2010). In our experiment, the daily UVAI varied from (–) 0.34 to (+) 2.24 with a seasonal mean (±1σ) of 0.99 (±0.49) over the IGP, which is considerably higher than the seasonal mean for all of South Asia (0.47 ± 0.46). Interestingly, negative UVAI was only evident during early October (week 1), signifying the presence of non-absorbing aerosols (like sulfate), while UV absorbing aerosols such as smoke and/or mineral dust was mainly evident during the rest of the season. During BDP, the high UVAI values (> 1.5) were mainly found to concentrate over the upper to middle IGP with 72% of observations >1.0. This clearly indicates the larger abundance of fresh UV-absorbing particles and is similar to the reported UVAI
<figure><img src="image_11.png" /><figcaption>**Figure 8.** Aerosol vertical profiles from selected CALIPSO overpasses across ground station (a) aerosol subtypes, (b) extinction coefficients of each aerosol type, and (c) time series of the extinction profile for total and smoke aerosols.</figcaption></figure>

vertical profile, however, was modified from the end of October due to biomass burning emissions, with dominance of smoke particles, mainly persisting at low altitude (< 1.5 km). The height of smoke layer was consistent with that of OMI UVAI projected aerosol height. Smoke particles were found to associate with polluted dust, clean continental, and polluted continental aerosols, with overlapping profiles. Overall, smoke was the most frequent aerosol type with a high aerosol extinction coefficient (1–2.5 km⁻¹ at 532 nm), and the altitude of the largest occurrence frequency of smoke remained below ~ 1.5 km. The low injection height of smoke plumes from biomass burning may serve as a key input for aerosol transport modeling over the IGP as it critically regulates the distance and direction of the particle dispersion (Guan et al., 2010; Banerjee et al., 2011).

The daily variation in total aerosol extinction and aerosol extinction only by smoke particles were also included in Fig. 8c. Total aerosol extinction indicates a corresponding increase during biomass burning, which peaks particularly in November, with low smoke injection height. Clear evidence of a gradual increase in smoke particle aerosol extinction was also noted. A single piece of evidence of high smoke extinction (> 1 km⁻¹) at a greater height (~ 3.4 km) was noted on 11 November, which may be associated with particle long-range transport and/or by the influence of the ABL. Overall, the CALIOP aerosol profiles were in accordance with the ground observations and OMI UVAI, indicating exclusive dominance of high-UV-absorbing aerosols across the plain during intense biomass burning.
<figure><img src="image_12.png" /><figcaption>**Figure 9.** Episode-specific MODIS fire count, fire radiative power (FRP, MW), brightness temperature (B. temp., K), and 5-day air mass back trajectory along with CWT.</figcaption></figure>

<figure><img src="image_13.png" /><figcaption>**Figure 10.** Episode-specific aerosol shortwave radiative forcing and atmospheric heating.</figcaption></figure>

## 3.8 Potential aerosol sources and transport

Active fire counts from the Terra and Aqua MODIS fire and thermal anomalies (with ≥ 70% confidence) clearly indicate that fire spots were predominately over the upper IGP, mainly concentrated over the Indian states of Punjab, Haryana, and western Uttar Pradesh, and in the Punjab state of Pakistan (Fig. 9). However, there was a temporal shift in the total number of fire counts (Fig. 9, within the marked region) from the biomass-burning-dominated period (BDP: 5272) to a less dominating period (BLDP: 4466). The fire radiative power (FRP), i.e., the rate of energy released in unit time, indicates a relative change in amount and strength of biomass burning emissions, mainly during BDP (138 366 MW) in com-

parison to BLDP (112 168 MW). The total FRP was higher during BDP mainly due to a higher number of fire counts and fire strength, as the rate of release of thermal radiation is related to the amount of biomass burnt and smoke being released (Schroeder et al., 2010). The MODIS fire spots (with brightness temperature), specially subset over the IGP, were plotted against 5-day air mass back trajectories and simulated and integrated at three vertical heights (100, 300, and 500 m) over the ground station. Vertical heights were selected based on the average planetary boundary layer height (402±81 m) for the monitoring period. The air mass back trajectories indicate the upper IGP as the sole source of aerosols during BDP, which was otherwise influenced by both continental and marine air masses during the non-dominating period. The air mass back trajectories during BDP overlap precisely with the fire spots that correspond to higher brightness temperature, indicating greater relevance to FRP. The air masses for individual episodes were further subject to cluster and CWT analysis considering columnar aerosol load, and the result was consistent with our prior observations. High CWT (>0.8) during BDP was clearly attributed to the regional pollution, mainly originating from the upper IGP. In contrast, relatively low CWT was noted during BLDP, originating from the upper IGP (CWT < 0.8), western dry region (CWT < 0.6), and an oceanic environment (CWT < 0.4). This leads us to conclude with confidence that there was a strong temporal gradient in post-monsoon-specific biomass burning emissions over the upper IGP, which greatly influences the regional aerosol climatology and thereby influences aerosol-induced health effects and regional climate.
## 3.9 Aerosol radiative forcing and atmospheric heating

Daily satellite-retrieved AOD, TCO, CWV, SSA, ground-based BC mass concentration, and aerosol water-soluble and water-insoluble fractions were used as an input to the OPAC model to simulate ARF (at 0.2–4.0 µm). Within the period, TCO varied between 237 and 277 DU without any difference between BDP (257 ± 10 DU) and BLDP (256 ± 12 DU). The SSA (at 550 nm), designating the fraction of scattered light over total light extinction, was lower during BDP (0.86 ± 0.05) compared to BLDP (0.98 ± 0.04), suggesting an abundance of strong absorbing aerosols especially during BDP. The CWV also fluctuates considerably (range: 0.28–3.92 cm), with an overall season-specific mean (±σ) of 2.0 (±0.7) cm.

The direct ARF and heating rate were estimated under clear-sky conditions with the SBDART model using OPAC output. The composite ARF was calculated for individual episodes at surface (SRF), top of the atmosphere (TOA), and atmosphere (ATM) (Fig. 10). Overall, the ARF at TOA and SRF were negative, indicating the aerosol cooling effect at the surface and top of the atmosphere. There was a slight temporal change in TOA radiative forcing (BDP: −28; BLDP: −23 W m⁻²) compared to the considerable intraseasonal variation in SRF forcing (BDP: −163; BLDP: −79 W m⁻²). The variation in SRF forcing was mainly induced by the surface BC (mean; BDP, BLDP: 9, 7 µg m⁻³), aerosol mass concentration (501, 327 µg m⁻³), and WSIC fractions, particularly in SO₄²⁻ (38, 15 µg m⁻³), NO₃⁻ (19, 12 µg m⁻³), and NH₄⁺ (11, 4 µg m⁻³). Since the ATM forcing is the balance of attenuation of radiation at TOA and SRF, the resultant atmospheric forcing was found to be very high, especially during the biomass-burning-dominated period (BDP: 135 W m⁻²), compared to the non-dominating one (BLDP: 56 W m⁻²). Overall, there was a clear indication of intraseasonal variation in ARF, which needs to be considered in parametrization of aerosol schemes for regional climate models. Similarly, the corresponding heat rate was substantially high during BDP (4.3 K day⁻¹), possibly influenced by more absorbing aerosols, compared to BLDP (1.8 K day⁻¹). The computed ARF during the post-monsoon season was comparable to other urban sites in the IGP that are reported to be influenced by biomass burning, e.g., Delhi (44–131 W m⁻², Bisht et al., 2015), Patiala (57–63 W m⁻², Sharma et al. 2017), Kanpur (30–43 W m⁻², Kaskaoutis et al., 2013), and over Karachi (35–84 W m⁻², Alam et al., 2011). However, none of the earlier reports noted the intraseasonality in ARF by means of change in driving factors, which appeared to be significant and necessitate proper address in regional model simulation. Intraseasonality in ARF was reported earlier over Varanasi during winter (ARF: 31–47 W m⁻², Kumar et al., 2017b), while the change in forcing was not as drastic as evident during the post-monsoon season. Therefore, it is extremely likely that intraseasonality in aerosol properties significantly influences the aerosol-

climate-health interactions over the IGP and therefore must need to be taken into account for uncertainty analysis in the regional aerosol–climate model.

## 4 Conclusions

The influence of biomass burning emissions on aerosol properties, transport, and radiative forcing was evaluated over the Indo-Gangetic Plain, South Asia. Very high concentrations of total and fine-mode aerosol (PM₂.₁) were observed during the post-monsoon season, with a significant increase in fine-to-coarse particle ratio (> 1) particularly from November. Submicron particles dominate the aerosol fine mode, with the PM₁.₁-to-PM₁.₁–₂.₁ ratio frequently exceeding 2.5. The WSIS was found to constitute a greater proportion of submicron and fine-particle mass compared to the coarser particles. The WSIS was mainly of secondary nature, with major contributions from sulfate and nitrate ions. A strong correlation between NH₄⁺ and SO₄²⁻ and a high NH₄⁺/SO₄²⁻ equivalent ratio in submicron particulates indicate the abundance of gaseous NH₃ to neutralize acidic species (SO₄²⁻). This contrasted with coarse-mode particles for which a low NH₄⁺/SO₄²⁻ equivalent ratio indicates the predominant neutralization by crustal minerals. The NO₃⁻-to-SO₄²⁻ ratio for submicron and fine-mode particles also increased (> 1) during extreme biomass burning emissions, as expected considering other reported observations of haze events over Asia. A rise in black carbon with a corresponding increase in Delta-C refer to the added contribution of biomass burning emissions. The influence of emissions was further quantified using specific organic (levoglucosan), inorganic (K⁺ and NH₄⁺), and satellite (UVAI) tracers. Levoglucosan was the most abundant species in submicron particles, with a very high ratio (> 50) with other anhydrosugars denoting exclusive emissions from burning of agriculture residue. The temporal variation in levoglucosan was consistent with inorganic tracers (K⁺ and NH₄⁺), with a sharp rise during November, and a strong correlation among these three indicates their biogenic sources. The association between levoglucosan and K⁺ or NH₄⁺ was nonlinear, with an exponential fit for submicron and fine particulates. The spatiotemporal distribution of aerosols was evaluated in terms of area-weighted mean over both the IGP and over the selected transect across the ground station. During the biomass-burning-dominated period, a considerable increase in columnar aerosol loading was highlighted (AOD: 0.98), consisting of absorbing aerosols (UVAI > 1.5) with a corresponding low plume height (~ 1.5 km). Moreover, the variation in a few trace gases associated with biomass burning emissions (CO and NO₂) was consistent with AOD, allowing a definite spatial signature of emission sources and transport across the IGP. The CALIPSO–CALIOP cross-sectional altitudinal profiles clearly illustrate the intraseasonality in aerosol types that were dominated by smoke and polluted continental
aerosols during biomass burning emissions, which are other-
wise associated with clean continental, polluted dust, and
dust aerosols. The possible pathway for regional transport of
aerosols from the upper IGP to the ground station was noted
using cluster analysis and concentration-weighted air mass
back trajectories. Finally, aerosol optical and microphysi-
cal properties were used in combination to simulate direct
aerosol radiative forcing (ARF) and atmospheric heating.
There was evidence of strong intraseasonality in ARF with
a very high atmospheric forcing (135 W m⁻²) and heating
rate (4.3 K day⁻¹) during the biomass-burning-dominated
period compared to the non-dominating one (56 W m⁻²,
1.8 K day⁻¹).

Considering that the duration of these biomass burning
emissions represents several weeks per year, their impact on
ARF and by consequence on the regional climate is not neg-
ligible. We therefore conclude with a reasonable level of con-
fidence that intraseasonality in aerosol properties must be se-
riously considered in the regional aerosol–climate model for
improving assessment and forecasting of aerosol–climate–
health interactions across the IGP.

*Data availability.* MODIS data are available from the Level 1 and Atmosphere Archive & Distribution System (LAADS) at https://ladsweb.nascom.nasa.gov (last access: 12 March 2018). Aura OMI and MERRA-2 reanalysis data are available from the Mirador NASA Goddard Earth Sciences Data and Information Center (GES DISC) (https://mirador.gsfc.nasa.gov, last access: 12 March 2018). CALIPSO data are available from the NASA Atmospheric Science Data Center (https://eosweb.larc.nasa.gov, last access: 12 March 2018). Planetary boundary layer height and air mass back trajectories are retrieved from Global Data Assimilation System (GDAS) archives hosted at the NOAA Air Resource Laboratory (https://ready.arl.noaa.gov, last access: 12 March 2018). MODIS fire products are obtained from the Fire Information for Resource Management System (FIRMS) (https://firms.modaps.eosdis.nasa.gov). Trace gas data at the ground station are available at the Real time Air Quality Data inventory of the Central Pollution Control Board (https://app.cpcbccr.com/ccr).

*Supplement.* The supplement related to this article is available
online at: https://doi.org/10.5194/acp-18-14197-2018-supplement.

***Author contributions.*** NS and TB designed the experiment while NS, MPR, and TB carried out the experiment and analyzed the data. NS, MPR, KD, TB, RSS, RKM, and MSH interpreted the observations and NS, TB, and KD drafted the paper.

***Competing interests.*** The authors declare that they have no conflict of interest.

*Acknowledgements.* This research is supported by the Science and Engineering Research Board (SERB), Department of Science and Technology (DST), New Delhi (SR/FTP/ES-52/2014). Tirthankar Banerjee acknowledges the financial support from the University Grants Commission (UGC) under the UGC-ISF bilateral project (6-11/2018 IC), Ram S. Singh acknowledges the Indian Space Research Organization under ARFI (code P-32-13), and Rajesh K. Mall acknowledges financial support from the Climate Change Programme of DST (DST/CCP/CoE/80/2017-G). Nandita Singh acknowledges financial support under the DST Women Scientist scheme (SR/WOS-A/EA-1012/2015) and Meytar Sorek-Hamer acknowledges the NASA postdoctoral fellowship, administered by USRA. The authors duly acknowledge the guidance and cooperation provided by the dean and director of IESD-BHU.

Edited by: Rolf Müller

Reviewed by: two anonymous referees

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