In complex atmospheric emission environments such as urban agglomerates, multiple sources control the ambient chemical composition driving air quality and regional climate. In contrast to pristine sites, where reliance on single or a few chemical tracers is often adequate for resolving pollution plumes and source influences, the comprehensive chemical fingerprinting of sources using non-methane hydrocarbons (NMHCs) and the identification of suitable tracer molecules and emission ratios becomes necessary. Here, we characterise and present chemical fingerprints of some major urban and agricultural emission sources active in South Asia, such as paddy stubble burning, garbage burning, idling vehicular exhaust and evaporative fuel emissions. A total of 121 whole air samples were actively collected from the different emission sources in passivated air sampling steel canisters and then analysed for 49 NMHCs (22 alkanes, 16 aromatics, 10 alkenes and one alkyne) using thermal desorption gas chromatography flame ionisation detection. Several new insights were obtained. Propane was found to be present in paddy stubble fire emissions (8 %), and therefore, for an environment impacted by crop residue fires, the use of propane as a fugitive liquefied petroleum gas (LPG) emission tracer must be done with caution. Propene was found to be
Non-methane hydrocarbons (NMHCs) are an important class of volatile organic
compounds (VOCs) that drive atmospheric chemistry and contribute to the
formation of tropospheric ozone and secondary organic aerosols (SOAs; Poisson et al., 2000; Hallquist et al., 2009; Derwent et al., 2010; Ortega
et al., 2016). Ground level ozone affects ambient air quality, human health
and climate, thus making it a primary target in air quality regulations (EPA,
1990). Furthermore, by reacting with the hydroxyl radical (OH), they can also
affect the oxidative capacity of the atmosphere (Atkinson, 2000). NMHCs have
a wide variety of anthropogenic, pyrogenic and biogenic sources. In urban areas, anthropogenic sources, such as vehicular emissions, industries
and fugitive solvent evaporation, dominate the emissions (Barletta et al.,
2005; Baker et al., 2008; Kansal, 2009; Jaimes-Palomera et al., 2016).
However, in an agrarian and developing economy like India and other parts of
South Asia, other major anthropogenic activities like crop residue burning
and garbage burning have emerged as poorly regulated emission sources. Every
year the northwest Indo–Gangetic Plain (NW-IGP) experiences episodes of
large-scale open burning of paddy stubble in the post-harvest months of
October and November, where
Previous studies have characterised the emissions of selected VOCs, greenhouse gases and primary air pollutants, like benzenoids, carbon monoxide, nitrogen oxides and black carbon (Venkataraman et al., 2006; Sahai et al., 2007), from paddy stubble burning over NW-IGP. However, there is still a considerable deficit in knowledge concerning the speciated non-methane hydrocarbons, which are co-emitted in the smoke (Andreae, 2019; Sinha et al., 2019). The NMHC emissions from different sources, when expressed as emission source profiles (Watson et al., 2001; Hong-li et al., 2017), provide detailed insights for quantitative source apportionment in source receptor models. Moreover, they are helpful for assessing human health risks due to exposure to toxic and hazardous compounds and secondary pollutant formation tendencies and, therefore, assist in prioritisation of pollution control strategies and policies.
The ratio of two NMHCs with different chemical lifetimes can also be used to constrain the photochemical age of air masses and atmospheric transport times (Parrish et al., 1992; McKeen and Liu, 1993). Several source profiles have been compiled for different emission sources in North America (Dallmann et al., 2012; Gentner et al., 2012), Europe (Passant, 2002; Niedojadlo et al., 2007), East Asia (Na et al., 2004; Liu et al., 2008; Zhang et al., 2013; Zheng et al., 2013; Mo et al., 2016; Hong-li et al., 2017) and other areas (Doskey et al., 1999); however, there is still a considerable gap in the data of speciated NMHCs from active emission sources in South Asia. Using the emission source profiles from different regions of the world for modelling and emission inventories can result in large uncertainties as the emissions can change from country to country, depending upon the quality and composition of fuels, combustion practices and vehicular fleet. Therefore, it is essential to have a comprehensive database of regional and local source profiles which can be used to yield more accurate data for the calculation of emissions and source apportionment tools, such as positive matrix factorisation.
In this study, we report the NMHC fingerprinting of paddy stubble burning
emissions, garbage burning emissions, fuel evaporative emissions and idling
exhaust emissions of vehicles powered by liquefied petroleum gas (LPG),
compressed natural gas (CNG), diesel and petrol using 49 speciated NMHCs (22 alkanes, 16 aromatics, 10 alkenes and one alkyne). These compounds were
measured using thermal desorption gas chromatography flame ionisation
detection (TD-GC-FID). Based on the measured source profiles, chemical
tracers were identified to distinguish the varied emission sources and also
for use in positive matrix factorisation (PMF) source apportionment models. Furthermore, we assessed the secondary pollutant formation potential and health risks of the sources in terms of their OH reactivity (s
Number of samples investigated per source for the measurements of non-methane hydrocarbons (NMHCs) source profiles. Note: liquefied petroleum gas – LPG; compressed natural gas – CNG; light-duty vehicle – LDV; heavy-duty vehicle – HDV.
Table 1 summarises the details of the whole air sample collection
experiments for emissions from paddy stubble burning, garbage burning, busy
traffic junctions, idling vehicular exhaust emissions and fuel evaporation.
The paddy stubble burning samples (three flaming and three smouldering) were
collected at an agricultural field in Kurari, Mohali (30.605
The whole air was actively sampled in commercially available 6 L passivated
SilcoCan air sampling steel canisters (Restek) and then analysed, using
a thermal desorption gas chromatograph equipped with a flame ionisation
detector (TD-GC-FID), within 1 d of the sample collection as per the collection procedure described in previous works (Chandra et al., 2017; Vettikkat et al., 2020). Stability tests of the compounds in the canisters were also conducted, which showed that all the measured compounds reported in this work, including alkenes and alkyne, remained stable for up to 3 d. The air was actively sampled into the canisters using a Teflon VOC pump (model N86 KT.45.18; KNF) operating at a flow rate of
NMHCs in the sample air were measured using a gas chromatograph equipped
with two flame ionisation detectors (GC-FID 7890B; Agilent Technologies). Sampling and pre-concentration was performed using a thermal desorption (CIA Advantage-HL and Unity 2; Markes International) unit coupled to the GC-FID system. Helium (99.999 % pure; Sigma Gases and Services) was used as the carrier gas. Hydrogen (99.9995 %; Precision Hydrogen 100 H
Figure S2 shows the schematic representation of the TD-GC-FID instrument
during a typical sample injection and chromatographic run. In the first
stage, sample air was passed through a Nafion dryer (integrated into the CIA
Advantage) to remove water (Badol et al., 2004; Gros et al., 2011). It was
then preconcentrated at
Prior to the sampling, the instrument was calibrated by dynamic dilution
with zero air at different mixing ratios (in the range of 2–200 parts per billion – ppb) using a standard gas calibration unit (GCU-s, v2.1; Ionimed, Ionicon Analytik GmbH). A NIST calibrated flow meter (Bios DryCal Definer 220) was used to measure the flows of both the standard gas and zero air mass flow controllers before and after the calibration experiments. Figure S3 shows the sensitivity and linearity of NMHCs obtained from the calibration
experiments performed over a dynamic range of 2–200 ppb over two sets of
calibrations, namely regular calibration of 2–20 ppb and a high mixing ratio calibration of 10–200 ppb. This covers a range of 2 orders of magnitude over which the instrument exhibited an excellent linearity
(
The Supplement 3 (in Excel file format) provides details of the measured mixing ratios for each individual sample measured by the TD-GC-FID system after dilution, mixing ratios of the compound in the actual sample after correcting for dilution along with uncertainty and the values in the corresponding background samples. For the major compounds determining the normalised source profiles (presented and discussed in Sect. 3.1), the sample values were significantly higher than the background values (even by an order of magnitude or more for smoke and vehicular exhaust source categories). Therefore, while the background values were used to calculate excess concentrations, they hardly played any role in the determination of the emission profiles. The peaks in the chromatograms of the emission sources were also well resolved and separated and were identified using the calibration gas standards. In case a shoulder peak was present, the parent peak was separately integrated, i.e., any interference from a shoulder peak was subtracted from the parent signal. In the calibration gas standard, some additional compounds were also present, namely 2,2,4-trimethylpentane, 2,3,4-trimethylpentane and methylcyclohexane, each of which had a well-resolved and separate peak during the calibration experiments. However, during the analysis of emission source samples, these compounds exhibited poor peak features like peak shape, several shoulder peaks, etc. Therefore, to remain consistent across all samples, these compounds were excluded from the analysis, and only those compounds that were well resolved were included. Table S3 lists the details of two VOC gas standards, namely the (1) gas standard (Chemtron Science Laboratories Private Limited) containing VOCs at a mixing ratio of circa 1 parts per million by volume (ppmv; stated accuracy of
Here,
Normalised source profiles of
Figure 1a–d show the normalised emission profiles of the whole air samples collected from paddy stubble and garbage fires under flaming and smouldering conditions. The mixing ratios were corrected for ambient background levels using samples collected just before the fires, normalised to the NMHC, with the maximum mass concentration in the respective source sample and averaged for the different fires.
The largest contributors to the mass concentrations in paddy fires under flaming conditions were ethene (16 %), benzene (16 %), propene (13 %), acetylene (13 %) and ethane (12 %), while in smouldering conditions ethane (21 %), isoprene (13 %), propene (13 %), propane (8 %) and ethene (6 %) were the highest ranked contributors.
Acetylene was found to be negligible (
Figure 1e–h show the normalised source profiles of the whole air samples collected from the headspace of liquefied petroleum gas (LPG), petrol and diesel.
Propane,
Normalised source profiles of
Figure 2 shows the normalised source profiles of the whole air samples collected from the exhaust tailpipe of idling vehicles with different fuel types and from busy traffic junctions. Among the NMHCs, compressed natural gas (CNG) vehicular emissions (Fig. 2a) had 70 % ethane by mass concentration, which is not surprising considering that it is mostly composed of methane and ethane (Goyal and Sidhartha, 2003). The other major NMHC emissions from CNG exhaust were propane (11 %) and ethene (10 %). Overall, alkanes (87 %) and alkenes (12 %) accounted for almost all NMHC emissions from the CNG vehicles.
Figure 2b shows that LPG vehicular emissions were mainly comprised of low
molecular weight alkanes, i.e, C
Figure 2c–d show the averaged vehicular emissions for two wheelers and four
wheelers fuelled by petrol. Aromatics (44 %) and alkanes (42 %) were the major constituents of emissions from petrol vehicles, with toluene (15 %),
Figure 2e–g show the tailpipe emissions from light-duty three wheelers,
light-duty four wheelers and heavy-duty vehicles fuelled by diesel. The
diesel exhaust emission profiles were much simpler than the petrol exhaust
emissions. Alkenes and acetylene were the major constituents of the diesel
vehicular exhaust, contributing 58 % to the total NMHC emissions.
Furthermore, the BTEX (16 %) and C
Compound-specific precision errors (%), limit of detection (LOD; in parts per trillion – ppt) and total measurement uncertainties (%). Note: ppb – parts per billion.
In comparison to petrol, the diesel exhaust had lower fraction of heavier
C
Based on the emission characteristics discussed earlier for each fuel type,
petrol vehicles and LPG vehicles were identified as being the most likely sources of
In the past three decades, India has undergone rapid economic and industrial
growth, which in turn has resulted in increased consumption of diesel to
sustain the increased freight transport across the country (Nielsen, 2013). As discussed previously, the diesel vehicular exhaust and evaporative emissions were dominated by heavier C
It was estimated that in 2009 the transport sector contributed 694 Gg of particulate matter (PM) emissions in India,
Comparison of the contribution of chemical compositions in groups
(aromatics, alkene and alkyne and alkanes) to
Figure 3a–d show the comparison of the percentage contribution of different
chemical classes of NMHCs to the total mass concentrations, OH reactivity
(s
The ozone formation potential (OFP) is used as a metric to measure the
contribution of NMHCs to the total O
In order to ascertain any statistical difference between the average OFPs of
the emission sources, we carried out Tukey's pairwise honestly significant
difference test (which accounts for sample size), and the summary of the test
results is provided in Table S5. Based on the statistical test, it could be
concluded with more than 95 % confidence that CNG vehicular emissions and
the fuel evaporative emissions had different OFPs compared to other emission
sources. The averaged OFP for the emission sources was diesel vehicle
exhaust (6.5
In paddy stubble fires under flaming conditions, propene (33 %), and under smouldering conditions, isoprene (46 %), were the largest contributors to the total OH reactivity (details in Figure S5 and S6). These two NMHCs were also the largest contributors (
LPG evaporative and vehicular exhaust emissions comprised of 68 %–81 % and 56 % alkanes, respectively; however,
In diesel evaporative emissions there was approximately equal contribution
to the total OH reactivity from alkanes (36 %) and aromatics (44 %).
This is because of the presence of larger fractions of heavier C
The total OH reactivity from traffic emissions was dominated by alkenes
(48 %) and aromatics (35 %). The NMHCs contributing the largest
fractions to the total OH reactivity were styrene (9 %)
The mixing ratios of styrene and 1-hexene measured in our traffic samples
were higher than the Fu Gui Mountain tunnel (styrene – 0.08
In order to assess the health risks associated with these sources, we
compared the fraction of BTEX compounds in each of the emission sources.
Benzene is classified as a human carcinogen (IARC, 2012), the potential
health risk assessments of which have already been elucidated in
NW-IGP during the periods influenced by intense paddy stubble fires (Chandra
and Sinha, 2016). Other benzenoids like toluene and xylenes have also been
associated with adverse effects on human health (ATSDR, 2000, 2007)
and are classified as group “D” carcinogens by the US Environmental Protection Agency (EPA). Using the BTEX
fraction, which is a well-known metric (Słomińska et al., 2014), is
useful for comparing the mass fractional BTEX content of the emission
sources. The statistical differences in the average BTEX fraction between
the different emission sources were ascertained by Tukey's pairwise honestly
significant difference test, and the summary for this information is provided in Table S6. Based on the statistical test, it could be concluded with more than 95 % confidence that diesel and petrol evaporative emissions, diesel
vehicles and smouldering paddy fires had different average BTEX fractions compared to other emission sources. Out of 28 possible pairwise comparisons,
14 pairs show statistically significant differences with
Inter-NMHC molar ratios (ppb/ppb) are very useful tools that can not only be used to distinguish between different emission sources but also constrain the identity of the sources affecting ambient mixing ratios in a complex environment (Barletta et al., 2005, 2017). This is because, for the NMHC species with similar chemical lifetimes, the molar ratios remain preserved during chemical oxidation and ambient dilution (Parrish et al., 1998; Jobson et al., 1999). Furthermore, NMHC molar ratios that remain similar across sources can also be employed to assess the photochemical age of air masses.
Characteristic inter-NMHC molar ratios (ppb/ppb) for the whole air samples collected from paddy stubble fires, garbage fires, evaporative fuel emissions (petrol, diesel and LPG), and traffic and vehicular exhaust from different fuel types (petrol, diesel, LPG and CNG).
Note: TMB – trimethylbenzene; F – flaming; S – smouldering.
Table 3 lists the commonly used inter-NMHC molar emission ratios for the
emission sources studied in this work. The toluene
The
Comprehensive chemical speciation source profiles of 49 NMHCs (22 alkanes,
16 aromatics, 10 alkenes and one alkyne) were obtained for several major
emission sources, namely paddy stubble burning, garbage burning, idling
vehicular exhaust and evaporative fuel emissions. Many of these compounds,
like the higher C Propane was found to be one of the abundant NMHC compounds in paddy stubble fire emissions. This is in contrast to the existing literature which considers it as a tracer for fugitive LPG emissions. In a complex emission environment influenced by several sources like paddy fires, the use of propane as an LPG tracer therefore calls for caution. Propene emissions in smouldering fires were found to be more than ethene by Isoprene was identified as a reliable tracer for distinguishing between the paddy fires and garbage fires at night. Compositional differences in the evaporative emissions from the two types of LPG (commercial and domestic) used widely in South Asia were also identified. While propane was the most dominant NMHC in the domestic LPG vapours, the commercial LPG vapours were dominated by butanes. Toluene The
These source profiles can be used for accurate and reliable emission
calculations, source apportionment studies and to assess the choice of fuels
from the point of view of air quality impacts, both as primary emission sources and also their potential to form secondary air pollutants like ozone and particulate matter. Ambient traffic emissions were found to be dominated by the petrol exhaust emissions due to the typically higher fraction of petrol-fuelled vehicles among the on-road intracity vehicular fleet in India. The potential toxicity and health impacts of the emission sources were
assessed by using the BTEX fraction as a metric, and petrol exhaust, paddy
stubble fires and garbage fires were ranked higher in toxicity than other
emissions, based on this metric. Based on our limited measurements of ambient
benzene in the traffic thoroughfares, the mass concentration was 6.1
The results and insights obtained from this study will aid in the identification of factor profiles in source apportionment models, such as positive matrix factorisation, yielding more accurate quantitative data for the mitigation of ambient air pollution.
Data are available from the corresponding author upon request.
The supplement related to this article is available online at:
VS and AK conceived and designed the study. AK carried out the sample collection, field work and performed TD-GC-FID measurements with the help of MS and HH and the advice of BB concerning the analytical system. AK carried out the preliminary analysis and wrote the first draft. VS revised the paper and carried out the advanced analyses and interpretation of the data and supervised all experimental aspects of the work. VG participated in the discussion of the analytical system and commented on the paper.
The authors declare that they have no conflict of interest.
We acknowledge the IISER Mohali Atmospheric Chemistry Facility for the data and the Ministry of Human Resource Development (MHRD), India, for funding the facility. Ashish Kumar, Haseeb Hakkim and Muhammed Shabin acknowledge MHRD and IISER Mohali for the doctoral (SRF and JRF) fellowships. We acknowledge EGU for the waiver of the APC through the EGU 2019 OSPP award to Ashish Kumar. We also thank Baerbel Sinha (Department of Earth and Environmental Sciences, Indian Institute of Science Education and Research, Mohali) and the two anonymous reviewers for their helpful suggestions and insightful comments which helped to improve the paper. We also acknowledge the help and support of the members of IISER Mohali Atmospheric Chemistry facility, namely Harshita Pawar, Pallavi, Abhishek Mishra, Abhishek Verma, Bharti Sohpaul and Tess George for their technical assistance during field sampling.
This research has been supported by the National Mission on Strategic knowledge for Climate Change (NMSKCC) MRDP Program of the Department of Science and Technology, India vide grant (SPLICE; grant no: DST/CCP/MRDP/100/2017(G)).
This paper was edited by Eliza Harris and reviewed by two anonymous referees.