ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-18-11031-2018Revolatilisation of soil-accumulated pollutants triggered by the summer
monsoon in IndiaRevolatilisation of soil-accumulated pollutantsLammelGerhardg.lammel@mpic.dehttps://orcid.org/0000-0003-2313-0628DegrendeleCélineGuntheSachin S.MuQingMuthalaguAkilaAudyOndřejBijuChelackal V.KukučkaPetrMulderMarie D.OctavianiMegaPříbylováPetraShahpouryPouryahttps://orcid.org/0000-0002-2657-3611StemmlerIreneValsanAswathy E.Max Planck Institute for Chemistry, Multiphase Chemistry Department,
Hahn-Meitner-Weg 1, 55128 Mainz, GermanyMasaryk University, Research Centre for Toxic Compounds in the
Environment, Kamenice 5, 62500 Brno, Czech RepublicIndian Institute of Technology Madras, Environmental and Water
Resources Engineering Division, Chennai 600036, IndiaCollege of Engineering Munnar, Department of Civil Engineering, P. B.
No. 45, County Hills, Munnar 685612, IndiaMax Planck Institute for Meteorology, Ocean in the Earth System
Department, Bundesstr. 53, 20146 Hamburg, GermanyGerhard Lammel (g.lammel@mpic.de)7August2018181511031110404January201827April201813July201816July2018This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://acp.copernicus.org/articles/18/11031/2018/acp-18-11031-2018.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/18/11031/2018/acp-18-11031-2018.pdf
Persistent organic pollutants that have accumulated in soils can be
remobilised by volatilisation in response to chemical equilibrium with the
atmosphere. Clean air masses from the Indian Ocean, advected with the onset
of the summer monsoon, are found to reduce concentrations of
hexachlorocyclohexane (HCH), dichlorodiphenyltrichloroethane (DDT) and its
derivatives, endosulfan and polychlorinated biphenyls (PCBs) in air at a
mountain site (all in the range 5–20 pg m-3) by 77 %, 70 %, 82 % and 45 %,
respectively. The analysis of fugacities in soil and air suggest that the
arrival of summer monsoon triggers net volatilisation or enhances ongoing
revolatilisation of the now-banned chemicals HCH and PCBs from
background soils in southern India. The response of the air–soil exchange was
modelled using a regional air pollution model, WRF-Chem PAH/POP. The results
suggest that the air is increasingly polluted during transport by the
south-westerly monsoon winds across the subcontinent. Using a multidecadal
multimedia mass balance model, it is found that air–surface exchange of HCH
and DDT have declined since the ban of these substances from agriculture, but
remobilisation of higher chlorinated PCBs may have reached a historical
high, 40 years after peak emission.
Introduction
Persistent organic pollutants pose a hazard to humans and wildlife as they
may reach harmful concentrations in biota upon accumulation along food
chains. Semi-volatile substances (i.e. vapour pressure at 293 K in the range
10-6–10-2 Pa) diffuse across air–sea and air–land
interfaces in both directions. They tend to net volatilise from land and sea
surfaces on which they had previously been deposited once a level of
contamination in chemical equilibrium with air pollution was reached
(Bidleman, 1999; Cousins et al., 1999; Meijer et al., 2003; Kurt-Karakus et
al., 2006; Wong et al., 2007; Růzičková et al., 2008; Degrendele
et al., 2016). The potential to revolatilise is relevant to assessing risks
from chemicals as it enhances the long-range transport potential and hence
facilitates transport to and accumulation in remote areas, which are
pristine with regard to primary (direct) contamination (Wania and Mackay, 1993; Semeena and Lammel, 2005; Wania and Westgate, 2008; Lammel and
Stemmler, 2012). In the terrestrial environment, soils represent the main
reservoir of the more lipophilic substances (log Koa≳6), while
smaller mass fractions are stored in the atmosphere, vegetation and
freshwater sources as suggested by field studies (Meijer et al., 2003) and modelling
(Wania, 2006; Lammel et al., 2007; Lammel and Stemmler, 2012). Thus,
understanding the dynamics of soil contamination and exchange with the
overlying air is important for assessing spatio-temporal scales of the
distribution and impact of local pollution. Air–soil dynamics occurs on
various timescales, from multi-year long-term trends (Lammel and Stemmler,
2012) to seasonal cycling and short-term fluctuations (Bidleman, 1999). One
key region, where persistent organic pollutants have been heavily used, is
southern Asia. In India, high levels of organochlorine pesticides (OCPs) were
found in both abiotic (Ramesh et al., 1989, 1991; Kumari et al., 1996; Rajendran et al., 1999; UNEP, 2002;
Shunthirasingham et al., 2010; Pozo et al.,
2011, Chakraborty and Zhang, 2012; Bajwa et al., 2016) and biotic (Ramesh et
al., 1990, 1992; Senthilkumar et al., 2001; UNEP, 2002) environmental
samples. The country is considered a hotspot for DDT and
hexachlorocyclohexane (HCH) with no evidence of decline (Sharma et al.,
2014). Besides OCPs, polychlorinated biphenyls (PCBs) and
polybrominated diphenylethers (PBDEs) are of relevance in southern Asia, where
they were used as flame retardants. High levels of PBDEs were reported in
India (Zhang et al., 2008) and waste might be a significant ongoing source
of penta- and hexachlorobenzene (PeCB, HCB), PCBs (Senthilkumar et al.,
2001; Wong et al., 2010; Zhang et al., 2011; Sharma et al., 2014) and PBDEs
(Breivik et al., 2012; Sharma et al., 2014). Thus far, studies on the
environmental exposure of the Indian subcontinent have been mostly limited
to urban areas (Chakraborty and Zhang, 2012; Sharma et al., 2014;
Chakraborty et al., 2015), while the continental background was scarcely
addressed. The air–soil dynamics of OCPs or other semi-volatile substances
related to the monsoon have not been studied yet. In India, air pollution levels
are expected to drop with the onset of the summer monsoon. Triggered by the
seasonal shift of the intertropical convergence zone, the large-scale
advection pattern switches from regional (southern Asia and adjacent seas) to
intercontinental (from the Indian Ocean with influence from the relatively
clean Southern Hemisphere (IMD, 2014).
Here we study air and soil pollution in India, first time with focus on the
impact of the summer monsoon on air–surface exchange. The hypothesis is
tested, whether drop of concentrations in air at the onset of the summer
monsoon mobilises pollutants stored in soils. To this end, (1) field
observations in background soils in the Western Ghats, the first highlands
that the south-westerly monsoon winds encounter, were performed before and during
the onset of the monsoon (May–June 2014). These were complemented by (2) regional-scale chemistry-transport modelling of the monsoon onset on the
Indian subcontinent using a 3-D air pollution model coupled to a soil compartment (3-D model WRF-Chem PAH/POP). Finally, (3) the long-term chemodynamics is assessed by
multimedia mass balance modelling, forced by climate and 3-D modelling data (1-D model, series of two boxes).
MethodsSites and sampling
Air samples were collected from 5 May to 10 June 2014, 90 km inland from
the Arabian Sea coast, on a slope oriented south-west in the northern
outskirts of the town Munnar (10.093∘ N,77.068∘ E; Fig. 4) at 1600 m a.s.l., with the mountain ridge's elevation in the area ranging
from 1950 to 2450 m a.s.l. The site is reached freely, i.e. without
topographic obstacles, by air masses that are advected through the sector
180–360∘ N. It is directly adjacent to tea plantations (south to
west) and deciduous forest (north-west to north-east). Additional land cover
includes shrubs (south, east) and, to a lesser extent, agricultural fields
and residential areas (south to south-east). Twenty-four air samples were collected.
The 2014 monsoon season in the area was characterised by scattered rainfall
at the monsoon onset, after which rainfall became persistent from the last
week of June (Valsan et al., 2016).
For air sampling a high volume sampler (Digitel DH-77) equipped with a
quartz-fibre filter and two polyurethane foam (PUF) plugs (Gumotex
Břeclav, density 0.030 g cm-3, 100 mm diameter, total depth 12 cm,
cleaned by extraction in acetone and dichloromethane) was used. Soil samples
were taken from one plot each in the tea plantation, in shrubs and in
forest, at distances within 1 km from each other. The uppermost 5 cm of soil
was collected (using a spade, Edelman auger and sieve). Each soil sample is a
composite (pooled sample), produced from equal amounts of soil collected
from six individual spots at distances of 1 m from each other. Three
replicates of each composite sample were analysed. At all plots the samples
were nitisol (GOI, 1985; FAO, 2014), horizon A, which had a brownish, loose,
single grain structure, with fine roots in the shrubs and forest. Soil
samples were homogenised by sieving and mixing. PUF samples were spiked to
control analyte losses during handling, shipping and storage.
Chemical and data analysis
For organic analysis all samples were extracted with dichloromethane in an
automatic extractor (Büchi B-811). Surrogate extraction standards
(PCB30, PCB185,13C BDEs 28, 47, 99, 100, 153, 154, 183, 209) were
spiked on each sample prior to extraction. The volume was reduced after
extraction under a gentle nitrogen stream at ambient temperature, and
clean-up was achieved on a Florisil column. Samples were analysed using a
GC-MS/MS (gas chromatograph coupled with a tandem mass spectrometer) Agilent
7890 coupled to Agilent 7000B with a SGE HT-8 column (60m×0.25mm×0.25µm) for α-, β-HCH, γ- and δ-HCH
(i.e. four HCH isomers), o, p′- and p, p′-DDE, -DDD and -DDT (six DDX compounds), penta-
and hexachlorobenzene (PeCB, HCB), PCB28, -52, -101, -118, -153, -138 and
-180 (i.e. seven indicator PCBs), aldrin, dieldrin, endrin, α- and
γ-chlordan, α- and β-endosulfan, endosulfan sulfate and mirex. More details are given in the Supplement Sect. S1.1.
The mean of three field blank values was subtracted from the air sample values.
Values below the mean + 3 standard deviations of the field blank values
were considered to be < LOQ. Field blank values of a number of
analytes in air samples were below the instrument limit of quantification (ILOQ),
which corresponded to 0.006–0.012 pg m-3 for PCB/OCPs and
0.50–5.2 pg m-3 for PBDEs (Table S1 in the Supplement). LOQs ranged 0.006–0.06 pg m-3 for PCBs, 0.006–0.12 pg m-3 for OCPs (with few exceptions
higher) and 0.001–0.01 pg m-3 for PBDEs (Supplement Sect. S1.1, Table S1).
Organic and elemental carbon in filter samples, as well as total organic
carbon in soil was determined by a thermal-optical method (Sunset Lab., USA;
EUSAAR protocol).
The pollutant fugacities (Harner et al., 2001) have been derived from
concentrations in soil and air (details in Supplement Sect. S1.2). The
onset of the monsoon on site was dated with high temporal resolution based
on air parcel history (back trajectory analysis, Supplement Sect. S1.3).
Modelling atmospheric transport, chemistry and air–soil exchange
The response of air–soil exchange to the drop in air concentration,
subsequent to the monsoon onset, was studied by the regional-scale
simulation of meteorology and chemistry using the WRF-Chem PAH/POP model.
The WRF-Chem PAH/POP has been recently extended from the regional model
WRF-Chem version 3.6.1 (Grell et al., 2005; Mu et al., 2018), to also
represent the chemistry, in- and below-cloud scavenging, gas-particle
partitioning and surface gas exchange of semi-volatile organics (described in
Supplement Sect. S1.4.1, input data in S1.4.3). The simulation of the
period 1–30 June 2014, with a spatial resolution of 27×27 km2; and a time step of 150 s of the southern Asian domain
(5–32∘ N,69–89∘ E), was driven by NCEP reanalyses
(6 h, 1∘×1∘ resolution). Physical and
chemical spin-up time was 4 days. Primary emissions were considered for DDT
and PCBs (Supplement Sect. S1.4.1), while the secondary emissions were modelled based on
initialising the soils of India uniformly by the observed levels in
background soils (shrub, forest, Sect. 2.1). Non-zero air concentrations,
observed before and during the monsoon at the site (see above), were advected
continuously at all boundaries of the domain (Supplement Sect. S1.4.1). In the model
experiment pre-monsoon levels were continuously replaced by monsoon levels
according to the northward propagation of the monsoon, while in the control
run pre-monsoon levels were kept constant at the boundaries.
Multi-decadal simulation of pollution of air and soil in India
The air–soil mass exchange flux of the semi-volatile organic compounds
studied were simulated by a non-steady state one-dimensional (series of 7
two-boxes) model of inter-compartmental mass exchange (multimedia mass
balance model (Lammel, 2004; Fig. S1 in the Supplement). The boxes represent 7 zones in the
north–south direction in India, 7.4–33.4∘ N, each 3.75∘
wide. For each box the mass balances for the two compartments planetary,
boundary layer and top soil, were solved. The processes considered in air are
wet and dry (particle) deposition, chemical removal from air by reaction
with the hydroxyl radical, air–surface mass exchange flux (dry gaseous
deposition and volatilisation) and loss by transport to the free
troposphere, while in the soil atmospheric deposition fluxes, air–surface
mass exchange flux and degradation (as first order process) were considered
(Supplement Sect. S1.4.2, input data in Sect. S1.4.3). In addition to a
50-year model run, the sensitivity of soil pollution to a number of input
parameters as well as under a hypothetic no-monsoon scenario was studied.
ResultsField observations
Relatively low pollution levels in soils (0.07–0.11 ng g-1
for Σ4HCH, 0.18–0.43 ng g-1 for Σ6DDX,
0.25–0.28 ng g-1 for Σ7PCB and 8.1–12.7 pg g-1 for
Σ9PBDEs) confirm the classification as a background site
(Table 1). Actually, these HCH and DDX levels are lower than ever reported
from soils in India, which previously ranged 1.6–835 for HCH (excluding hotspots; Sharma et al., 2014), 14–934 ng g-1 for DDX (Sharma et al.,
2014) and 30 (0–149) pg g-1 (rural sites, same congeners; Li et al.,
2016). The soil sample from a tea plantation showed elevated levels of DDT
and its metabolites (27.9 ng g-1Σ6DDX), pointing to
previous application (Table S2).
Observed concentrations in soil, cs (ng g-1; together
with standard deviation based on 3 replicates) of (a) pesticides, (b) PCBs and (c) PBDEs (quantified species only). TOC is total organic carbon content
(% of dry mass).
Observed concentrations in air, ca, of pesticides (Σ4HCH, Σ6DDX, Σ3Endosulfan), Σ7PCB, Σ9PBDE (pg m-3), OC and EC (µg m-3)
before and after the onset of the south-westerly monsoon in Munnar, India,
2014. Error bars reflect standard deviations. All concentration changes are
significant (p < 0.05 level, t test) except for PBDE.
Indeed, measured air concentrations of carbonaceous aerosol and organic
pollutants reach a distinctly lower level during the monsoon, dropping by a
factor of 2–10, except for PBDEs, which apparently increased (Fig. 1, Table S3). These concentration changes, i.e.
77 %, 70 %, 82 % and 45 % for Σ4HCH, Σ6DDX, Σ3Endosulfan
and Σ7PCBs from before to after (Fig. 1) were all significant on the p < 0.05 level,
most on the p < 0.01 level, except for PBDEs,
which were insignificant even on the p < 0.1 level (unpaired Student
t test). Precipitation increased by a factor of ≈2 at the monsoon
onset (from 3.8 to 8.0 mm day-1), associated with convective activity
(Valsan et al., 2016). With 2.3–17.7 pg m-3Σ4HCH
and 0.36–10.4 pg m-3Σ6DDX (Table S3) the measurements at
Munnar range at the lower end of the range reported from rural sites in
India in years after a ban in agriculture (listed in Table S6c). 1.3–8.5 pg m-3 endosulfan (including endosulfan sulfate) measured in Munnar in
2014, shortly after the ban of the pesticide is 3 orders of magnitude below
what was reported from 2006 to 2007 (i.e. 1000–9200 pg m-3 at rural locations
in southern India; Pozo et al., 2011). Similarly, the range of 2.8–70 pg m-3Σ7PCBs measured in 2014 at Munnar lies distinctly below 32–440 pg m-3, reported for the same substances at rural coastal sites in
2006 (Zhang et al., 2008).
The fugacity ratio fs/fa is used to characterise air–soil exchange
(Supplement Sect. S1.2). Calculations indicate both downward (PCB180, DDT
and metabolites over forest and shrub soils, BDE99) and upward (PCB101,
PeCB, DDT and metabolites over tea garden soils, BDE28, BDE47; Fig. 2)
diffusive air–soil exchange fluxes prior to the monsoon. With the monsoon
onset fs/fa generally increases (except for PBDEs, for which
concentrations in monsoon air were somewhat elevated compared to pre-monsoon
air; Table S3). This can trigger a change in flux direction for the tri- to
hexachlorinated PCBs (i.e. all targeted except PCB180) and α- and
β-HCH (Fig. 2). For example, α- and γ-HCH were close to
phase equilibrium before onset, but net volatilisation occurred during the
monsoon, while β-HCH changed from net-depositional to near phase
equilibrium.
Change in air–soil chemical equilibrium with the monsoon onset.
Arrows denote direction and amount of change in the fugacity ratio,
fs/fa, over various soils from prior to posterior onset.
fs/fa < 1 denotes downward (net deposition)
while fs/fa > 1 denotes upward (net volatilisation) flux. The shaded
zone (0.33 < fs/fa < 3) indicates
insignificance of deviation from 1 due to input data uncertainties.
Response of air–soil gas exchange of pollutants to monsoon onset
Findings from the field campaign were used to constrain the regional
WRF-Chem model simulations for the Indian subcontinent. In the model
experiment the pre-contaminated soil (as observed at the background site,
mean of soil samples) is exposed to a drop of atmospheric concentrations,
forced from the domain boundaries along with the monsoon onset and its
northward propagation. In a control experiment pre-monsoon air
concentrations are prescribed at the boundaries throughout the simulation
(detailed in Methods and Supplement Sect. S1.4.1).
Air pollutant distributions. α-HCH, γ-HCH, PCB28 and
PCB153 (pg m-3) predicted concentrations in near-ground air prior to
monsoon onset (1–3 June, a) and difference distributions due to
monsoon advection (experiment – control; b: 8–10 June, c: 28–30 June 2014). The difference is significant (p < 0.05,
t test) south of the dotted line.
Within a few days after the monsoon onset in southern India, the advection of
air from the Indian Ocean reduced atmospheric levels of HCH and PCB over
southern India and the Bay of Bengal, and to a lesser extent over central
India (Fig. 3, centre panels). Three weeks after onset in southern India,
the northern monsoon boundary has passed over India except the north-western
states of Gujarat and Rajasthan (i.e. north of ≈22∘ N and
west of ≈77∘ E; Valsan et al., 2016), but the
distributions of HCH and PCB in air maintain significant gradients with
high, i.e. only moderately reduced (by < 1 pg m-3) levels in
the north and east, and low levels after a decline of > 3 pg m-3 of HCH isomers and > 5 pg m-3 of PCB28,
respectively, in the south and south-west. The response of the air–soil
system to the monsoon leads to a spatially inhomogeneous
distribution of pollutants across India. It is dominated by clean air
advection in the south and south-west but only moderately decreased air
pollution in northern and eastern parts of the subcontinent, as the air
receives secondary emissions from the soils. The secondary emissions increase with
distance from the coasts after the monsoon onset. The differences in
concentrations before and during the monsoon are significant (p < 0.05,
t test) in southern and central India and parts of the north (Fig. 3). The model
results show that HCH isomers and PCB28 concentrations drop by ≈80 %, ≈20 % and ≈4 % at 9, 22 and 29∘ N,
respectively, PCB153 by ≈40 % and ≈10 % at
9 and 22∘ N, respectively, while they increase by
≈1 % at 29∘ N (Table S5a). The model realistically
reproduces the decline in atmospheric concentration at the field site
(southern India, 9∘ N; Table S6a, b). In the model, the HCH and
PCB volatilisation fluxes are enhanced in the south (by 0.02–0.78 pg m-2 h-1 i.e. 3–11 %; Table S5b) by the drop in air pollution,
to a lesser degree in central India (0.002–0.19 pg m-2 h-1),
and by an even smaller or negligible amount at a site in northern India (< 0.0001–0.007 pg m-2 h-1).
The south-westerly summer monsoon is associated with strong convection that
effectively lifts air pollution to high altitudes in the troposphere. The
monsoon outflow from India is predominantly directed towards western Asia,
Africa and the Mediterranean, while a smaller fraction is transported
towards eastern Asia (Lawrence and Lelieveld, 2010).
Multidecadal air–surface cycling of POPs and historic trends
To put the above described seasonal feature into historical context, with
long-term trends of air–soil contamination, a multimedia mass balance box
model was developed and applied to several measured contaminants.
As a result of historical applications in agriculture and industry, POPs
have been accumulating in soils in India over decades (Fig. S3), partly
continuing beyond peak emission. The atmospheric concentrations of PCBs have
decreased since ≈1974 and those of α-HCH and DDT since ≈1989, but soil concentrations only decreased for p, p′-DDT and
levelled off for α-HCH (Fig. S3a, b), or are even still on the rise
(PCB153; Fig. S3d). Apart from changes over time, in general related to
substance usage, the spatial variation in the concentrations of pollutants in
mostly agricultural soil in India (Ramesh et al., 1991; Kumari et al., 1996;
Sharma et al., 2014) is very large, i.e. ≥2 orders of magnitude. No
data from background sites are available (Table S6c). The simulated
pesticide values, 0.5–20 ng g-1α-HCH and 50–5000 and 1–200 ng g-1 DDT in the 1990s and 2000s, respectively (Fig. S2), fall into the
ranges spanned by the observations (Table S6c). For PCBs, no soil data were
reported (UNEP, 2002).
A north–south gradient is predicted for the pollutants (Fig. S3), which is
certainly influenced by the emission distribution (maximum in northern India,
in the Indo-Gangetic Plain) as well as the direction of advection in air
(prevailing westerly, with northerly component). For α-HCH, this
gradient was also reflected in soil distributions in India, which were based
on a gridded mass balance model (Xu et al., 2013). While PCB28
had net volatilisation after a few years upon release into the environment,
this was much later for the highly lipophilic PCB153, with ≈1 decade in
southern India, ≈2 decades in central and even longer in northern
India (Fig. 4d). Nowadays, the diffusive air–surface exchange flux of the
pesticides α-HCH and DDT is expected in the 0.1–1 fg m-2 h-1 range, several orders of magnitude lower than before or shortly
after the ban (Fig. 4a, b). In contrast and related to ongoing emissions
from old industrial facilities, the strong decrease in PCB usage did not
strongly impact air–surface cycling. The magnitude of fluxes remained within
the same order of magnitude, 0.1–1 fg m-2 h-1, being even on the
rise in the case of PCB153 (Fig. 4c, d). The air–ground fluctuations are
expectedly mediated by the storage of part of the pollutant burden in
vegetation (not resolved in the model).
Predicted multidecadal diffusive air–surface exchange fluxes. 1-D
model. Fc (positive is upward, negative is downward; lower) of
(a)α-HCH, (b)p, p′-DDT, (c) PCB28, (d) PCB153 in the northern
(29.7–33.4∘ N, blue), central (18.5–22.3∘ N, red) and
southern (7.4–11.2∘ N, green) zones of India during 1965–2014.
Predicted concentrations in air and soil are shown in Fig. S3 in the Supplement.
Illustration of temporal (a) and spatial (b) variation
in semi-volatile and persistent substance advection over southern, central and
northern India in response to the monsoon onset and its northward
propagation. Field site is Munnar.
The results of a simulation of a fictive no-monsoon scenario suggest that the
effects of the monsoon have been limiting pollution of soils by HCH and PCB28
somewhat (< 20 % in 2014), while they have been contributing to
DDT and PCB153 in soils by ≈50 % and ≈10 %,
respectively (Supplement Sect. S2.2.3, Table S8). This suggests that the monsoon's effect on
revolatilisation of soil burdens in response to drop in air concentrations
at its onset is a secondary effect for DDT and PCB153, while
the monsoon's enhancement of air-to-soil transfer by wet deposition is the
primary effect. This trend could be explained by the higher significance of
wet deposition for DDT and PCB153, which partition the
particulate phase more than HCH and PCB28, whereas the efficiency of gas
scavenging is generally low for POPs (Atlas and Giam, 1988; Bidleman, 1988;
Shahpoury et al., 2015).
Discussion
Both the field measurements and modelling results of this
study indicate a so-far overlooked mechanism of pollutant cycling over the
Indian subcontinent, i.e. monsoon-driven mobilisation of POPs from
previously contaminated soils. The decline of POP levels in the
south-westerly flow at the monsoon onset is partly related to the advection of
clean air from the Indian Ocean (seasonal shift of the ITCZ), and partly by
the washout of particulate pollutants (Fig. 5), as well as deepening of the
planetary boundary layer. In contrast, washout of gaseous organic pollutants
is very limited, because of low water solubility (Atlas and Giam, 1988; He
and Balasubramanian, 2010; Shahpoury et al., 2015). Because of the
convective vertical transport during the monsoon, pollutants can be released
at the cloud top and subsequently undergo long-range transport in the upper
troposphere over and beyond southern Asia. During transport over the Indian
subcontinent near the surface, air masses collect pollution emitted from
primary and secondary sources at the ground in urban and rural areas. We
have shown here that secondary sources are partly triggered by the low
concentrations in relatively pristine air, which are most pronounced in areas that
receive marine background air, i.e. in south-western India. This corresponds
to a seasonal decrease in the soil burden by a few percent relative to the
annual mean. This secondary source (revolatilisation) weakens as a function
of distance from the coast, as the monsoon advection propagates across the
subcontinent (Table S4b, Fig. 5). The 2014 south-westerly monsoon was
relatively weak (South Asian summer monsoon index; Li and Zeng, 2002)
compared to the long-term mean. For strong monsoon events, more efficient
air-to-soil transfer of pollutants by wet deposition could result. The
simulation under a no-monsoon scenario suggests that the latter process
dominates for the least water soluble and least volatile (high partitioning
to the particulate phase) pollutants. Scavenging and air-to-soil transfer of
POPs under monsoon rain had hardly been studied in the field and should be
addressed.
Secondary emissions, originating from past deposition to soils, also
contribute to the long-range transport of atmospheric POPs to remote areas
in central Asia (Sheng et al., 2013; Gong et al., 2015). A similar trend of
pollutant release from soils can be expected for other semi-volatile organic
substances such as polycyclic aromatic hydrocarbons (actually indicated by
observations on site, not reported here) and brominated chemicals.
All data needed to evaluate the conclusions in the paper
are present in the paper and/or the Supplement. Additional data related to
this paper may be requested from the authors.
The supplement related to this article is available online at: https://doi.org/10.5194/acp-18-11031-2018-supplement.
GL conceived the study. SSG,
GL, AM, PS, and AEV conducted the air and soil sampling and field
measurements. OA, PK and PP did the chemical analysis of samples. GL
and SSG did the field data analysis. CVB, and CD and MDM provided
on-site support and meteorological analyses, respectively. CD, QM, MO
and IS prepared model input. CD and GL designed the 1-D model. CD and
QM incorporated model parameterizations, and performed and analysed the 1-D
and 3-D model runs, respectively. CD, SSG, GL, and IS discussed the
results. GL wrote the manuscript with input from all
co-authors.
The authors declare that they have no conflict of
interest.
Acknowledgements
We thank Tabish Umar Ansari (IIT) and Roman Prokeš (MU)
for on-site and logistic support, Milán Sáňka (MU) for
assistance with soil sampling and data, and Jos Lelieveld (MPIC), Andreas
Fink (Karlsruhe Institute of Technology) and Fei Ge (MPIM) for discussion.
The MU RECETOX Research Infrastructure was supported by the Czech Ministry of Education, Youth and Sports
(LM2015051 and CZ.02.1.01/0.0/0.0/16_013/0001761).
Sachin S. Gunthe acknowledges
the financial support from DST-Max Planck Partner Group on Bioaerosol
Research at IIT.
The article processing charges for this open-access publication were covered by the Max Planck
Society.
Edited by: Paul O. Wennberg
Reviewed by: two anonymous referees
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