A positive matrix
factorization model (US EPA PMF version 5.0) was applied for the source
apportionment of the dataset of 37 non-methane volatile organic compounds
(NMVOCs) measured from
19 December 2012 to 30 January 2013 during the SusKat-ABC international air
pollution measurement campaign using a proton-transfer-reaction
time-of-flight mass spectrometer in the Kathmandu Valley. In all, eight
source categories were identified with the PMF model using the new
constrained model operation mode. Unresolved industrial emissions and traffic
source factors were the major contributors to the total measured NMVOC mass
loading (17.9 and 16.8
Non-methane volatile organic compounds (NMVOCs) are important atmospheric
constituents and are emitted from both natural and anthropogenic sources
Source apportionment of NMVOCs can be achieved by applying source-receptor
models to measured ambient
datasets. Ambient NMVOC mixing ratios depend on the emission profiles of the
sources contributing to the ambient mixture, their relative source strengths,
transport, mixing and removal processes in the atmosphere. Source receptor
models perform statistical analyses on the dataset to identify and quantify
the contribution of different sources to the measured NMVOC concentrations
NMVOC emission inventories are frequently associated with large uncertainties
Kathmandu is considered to be amongst the most polluted cities in Asia
The objective of the current study is to identify and quantify the
contributions of different emission sources to the ambient wintertime NMVOC
concentrations in the Kathmandu Valley using a positive matrix factorization
(US EPA PMF 5.0;
NMVOC measurements during this study were performed in the winter season from
19 December 2012 until 30 January 2013 at Bode (27.689
Location of the measurement site (Bode, orange
circle) along with surrounding cities (Kathmandu, brown circle; Patan,
turquoise circle; Bhaktapur, pink circle), brick kilns (white markers), major
industries (yellow triangles), forested areas (green tree symbols), the airport
(blue marker) and major river paths (sky blue) in the Google Earth image of
the Kathmandu Valley (obtained on 22 May 2015 at 14:55
NMVOC measurements were performed using a high-sensitivity
proton-transfer-reaction time-of-flight mass spectrometer (PTR-TOF-MS model
8000, Ionicon Analytik GmbH, Innsbruck, Austria) over a mass range of
21–210
During the measurement period, a total of 37 NMVOC signals (
Grab samples from garbage fires (termed garbage burning) were collected near
the measurement site (
The US EPA Positive Matrix
Factorization (PMF) receptor model version 5.0
The explained variability is most useful to policy makers. If the observed mass loading of a compound that is known to be harmful to human health is high, the explained variability will indicate which sources are responsible for most of its emissions and what fraction of the total observed mass is contributed by each source. Therefore, this allows the planning of mitigation strategies.
Bootstrap runs were performed to ascertain the magnitude of random errors of
the dataset
For each factor, the factor profile of all bootstrap runs combined is
compared with the profile of the original model output. The model provides
a box and whisker plot for the mass loading (
When all sources are equally strong throughout the entire period, this bootstrap model provides a robust estimate of the total random error. However, if one of the sources is completely absent for a significant fraction of the total hours (like the brick kiln source throughout the first 13 days of the SusKat-ABC campaign), the bootstrap model may substantially overestimate the random error. For such a source, mass loading of all the compounds that contribute strongly to the factor profile of the source will typically be outside the interquartile range. For the same set of compounds, similar behavior could also be seen for the factor profile of several other factors. In such a situation, the error estimate of the bootstrap runs should only be considered as the upper limit of the potential random error.
In addition to the random error, the PMF model also has rotational ambiguity
PMF was applied to the hourly averaged dataset of 37 ions measured using
a PTR-TOF-MS. All relevant analytical details pertaining to the site
description, meteorology, sampling and quality assurance of the NMVOC dataset
have already been described in detail in a paper related to this special
issue
All the available data were used for the PMF analysis and the missing values
were replaced by a missing value indicator (
PMF model runs ranging from 5 to 12 factor numbers were carried out to
ascertain the best solution for this study, consistent with the chemical
environment of the Kathmandu Valley. Based on the
The diagnostics for the eight-factor solution are summarized in
Table
The traffic factor explains more than 60
For styrene the highest correlation is with furan
(
Benzene has a strong source in the form of biomass co-fired brick kilns, which
results in a distinct increase in emission at the time the brick kilns
restart their operations. This source is shared with acetonitrile (
The mixed industrial source explains 66
The mixed daytime factor primarily contains photochemically formed compounds,
most notably isocyanic acid, which shows a strong correlation with its own
precursors formamide (
The solvent evaporation factor is characterized by acetaldehyde and acetic
acid, which have their strongest correlation with each other (
The unresolved industrial emission factor explains a significant fraction of
the 1,3-butadiyne, which shares most of its sources with methanol
(
To identify the uncertainty associated with the PMF solution, bootstrap runs
were performed 100 times taking 96 h as the segment length. This is slightly
shorter than the recommended length based on the equation of
Diagnostic for the results of the positive matrix factorization (PMF) model run.
Figure
The constrained model mode was used to further improve the eight-factor solution.
The constraint mode is a new rotational tool introduced in the 5.0 version of
the EPA PMF as an alternative to the FPeak module. The constraint mode allows
the use of the rotational ambiguity of the model to push the PMF solution
into a physically more realistic space. It uses preexisting knowledge such
as source fingerprints, source emission ratios or activity data. We found
that when the two modules were compared for an equal number of factors the
constraint-mode performance was superior to the FPeak module. The original
model output showed positive correlations between the factor contribution
time series of the biomass co-fired brick kilns and mixed industrial
emissions (
Correlation between estimated and observed NMVOC concentrations.
First, the upper limit for the emission ratio of the individual aromatic
compounds to isoprene as reported by
Inter-NMVOC emission ratios used for biogenic, solvent evaporation and mixed daytime factors to nudge the PMF model and the corresponding emission ratios before and after nudging.
BG is biogenic. SE is solvent evaporation. MD is mixed daytime.
Second, it was assumed that aromatic compounds and acetonitrile are not photochemically produced. Acetic acid is associated with both mixed daytime and solvent evaporation; thus, the ratios of aromatic compounds and acetonitrile to acetic acid were nudged towards 0.0001 for these two factors.
Third, to improve the representation of brick kiln emissions, and the
residential biofuel use and waste disposal in the model, the respective
factors, which were clearly identified in the original model solution, were
nudged using the emission ratios of aromatic compounds to benzene from grab
samples of domestic waste burning (garbage-burning grab sample) and fixed
chimney bull's trench brick kiln emissions (FCBTBK grab sample) collected
directly at the point source. This was required because in the original
model output, the residential biofuel use and waste disposal factor
correlated with the traffic factor (
Nudging was performed by exerting a soft pull, allowing for a maximum
0.2
Implementing the constraints mentioned above significantly improved the representation of biogenic emissions and mixed daytime and solvent evaporation factors. Figure S4 in the Supplement shows a comparison of the box and whisker plots of the biogenic emissions and mixed daytime and solvent evaporation factors before and after nudging and demonstrates the significant improvement after applying constraints.
After nudging, the contribution of the biogenic factor correlated better with
solar radiation (
Comparison of aromatics
It can be seen that most constraints on the aromatic to isoprene ratio could
be executed without exceeding the penalty on
The fact that the constrained run was incapable of removing naphthalene from
the source profiles of the biogenic and the solvent evaporation sources and
the fact that the diel profiles of both these factors show a weak secondary
peak between 17:00 and 22:00
Figure S5a in the Supplement shows the
Table
For the residential biofuel use and waste disposal source, the original model
run already had ERs very similar to the garbage-burning grab
samples of the garbage-burning fire. The constrained run improved the
agreement further for styrene, trimethylbenzenes and naphthalene.
Constraining this factor with the ERs of three-stone firewood stoves from
For brick kilns, the ERs of the constrained model output runs diverged from the ERs of the FCBTBK grab samples. However, the temporal profile of the activity, especially the closure of the brick kilns during the first part of the campaign is better captured by the constrained run and the correlation with mixed industrial emission sources reduced significantly. The FCBTBK grab samples were collected on 6 December 2014, 2 years after the SusKat study. Thus, differences from the emission profiles observed during the SusKat-ABC campaign are a possibility. Alternatively, the differences could also stem from the inherently variable nature of this source. In particular, naphthalene and benzene were higher in the source profiles of the SusKat-ABC campaign compared to their relative abundances in the FCBTBK grab samples. At the time the FCBTBK grab samples were collected (on 6 December 2014), brick kilns were co-fired using coal, wood dust and sugarcane extracts. It is possible that in January, during peak winter season, a different type of biomass, one associated with higher benzene and naphthalene emissions (e.g., wood) was used in these biomass co-fired brick kilns, resulting in the slight disagreement between the PMF source profile and FCBTBK grab sample signature for this factor. Table S3 in the Supplement shows the percentage contribution of PMF-derived factors obtained from constrained runs with five, six, seven, eight and nine factors.
For identifying the physical locations associated with different local
sources, CPF analyses were performed. CPF
is a well-established method for identifying source locations of local sources
based on the measured wind
The ozone formation potential of individual NMVOCs was calculated as
described by the following equation
SOA yield of a particular NMVOC depends on the NO
Figure
Identification and attribution of these factors is discussed in detail in the following sections.
Factor profiles of the eight sources obtained by PMF analysis.
More than 60
Time series and diel box and whisker plot for Factor 1 (traffic).
Table
Emission ratios of NMVOCs
Time series and diel box and whisker plot for Factor 2 (residential biofuel use and waste disposal).
Factor 2 also showed regular evening hour peaks and a bimodal profile
(Fig.
Most of the measured NMVOC mass in this factor was contributed by acetic
acid, propyne, methanol, benzene, propene and acetone
Emission ratios of NMVOCs
There is some agreement with the ERs reported in previous studies, though all of these previous studies found higher ERs for styrene. This could indicate that the composition of household waste in the Kathmandu Valley is different (less polystyrene, plastic and more biomass) or that the source profile is mixed with that of a second source, with similar spatial and temporal characteristics. Residential biofuel use is expected to have a similar temporal profile and did not appear as a separate factor in the PMF solution. Therefore, Factor 2 was attributed to residential biofuel use and waste disposal sources collectively.
Time series and diel box and whisker plot for Factor 3 (mixed industrial emissions).
This factor explained 66
Time series and diel box and whisker plot for Factor 4 (biomass co-fired brick kilns).
The diel box and whisker plot of Factor 4 (Fig.
Benzene (
It is reported that brick kilns in the Kathmandu Valley burn large quantities
of biomass, wood and crop residue along with coal
Factor 5 explained
Time series and diel box and whisker plot for Factor 5 (unresolved industrial emissions).
Time series and diel box and whisker plot for Factor 6 (solvent evaporation).
Figure
Most of the total measured NMVOC mass of Factor 5 was due to oxygenated
NMVOCs like methanol (
Factor 6 explains approximately 25–40
Formic acid and acetic acid contributed most to the total measured NMVOC mass
of Factor 7 (
Approximately 41
Time series and diel box and whisker plot for Factor 7 (mixed daytime).
Time series and diel box and whisker plot for Factor 8 (biogenic emissions).
Factor 8 explains more of the total isoprene mass than any of the other
factors (
Most of the total measured NMVOC mass in this factor was associated with
oxygenated NMVOCs, namely acetaldehyde, acetic acid, acetone and formic acid,
which contributed
To summarize, based on the characteristics observed in the factor profiles, factor time series and diel plots, Factor 1 was attributed to traffic, Factor 2 was attributed to residential biofuel use and waste disposal, Factor 3 was attributed to mixed industrial emissions (MI), Factor 4 was attributed to biomass co-fired brick kilns, Factor 5 was attributed to unresolved industrial emissions, Factor 6 was attributed to solvent evaporation, Factor 7 was attributed to mixed daytime source and Factor 8 was attributed to biogenic NMVOC emissions. Table S4 in the Supplement shows the calculated correlation coefficients between the PMF-resolved source factors and the independent meteorological parameters.
It can be seen from Table S4 in the Supplement that during daytime, the
solvent evaporation factor correlated best with the rate of change in
solar radiation and the rate of change in ambient temperature (
The mixed daytime factor correlated with solar radiation, ambient
temperature and wind speed (
Figure
Conditional probability function (CPF) plots for all source factors resolved by PMF showing wind directional dependency of different source categories.
The CPF plot for the traffic factor showed maximum conditional probability (0.4–0.7) from the W-NW direction where the Kathmandu city center and the busiest traffic intersections were located. The conditional probability for the SW and NE wind directions ranged from 0.2 to 0.4. Two cities, namely Lalitpur (Patan) and Bhaktapur are located upwind of the site in these directions. The lowest conditional probability was observed for the SE wind direction.
The residential biofuel use and waste disposal factor showed a high
conditional probability of emissions exceeding the mean for air masses
reaching the site from most wind directions (0.5–0.7 for N-NW,
The mixed industrial emissions factor showed the highest conditional
probability of air masses, with above-average mass loadings reaching the
receptor site from the NE to SE wind sectors (
For brick kilns the highest conditional probability was observed for air
masses reaching the receptor site from the NE to SE (
It is interesting to note that the unresolved industrial emissions factor
shows a clear directional dependence (
The solvent evaporation factor also shows high conditional probabilities for the SE-SW wind direction (Patan Industrial Estate) and low conditional probabilities for the NW-NE wind direction. The conditional probability function shows significant overlap with that of the unresolved industrial emissions factor. It therefore highlights the plausibility that solvent and/or chemical evaporation or emissions from industrial units are the primary sources for this factor, although the temperature changes after sunrise drive partitioning into the gas phase.
Within the bin of calm wind speeds (
The mixed daytime factor shows no obvious directional dependence for the
conditional probability of recording values above the average at the receptor
site (
The biogenic factor showed high conditional probabilities for air masses
reaching the receptor site from the SW to N direction (
The CPF analysis of the PMF model output clearly indicates that spatially fixed sources are responsible for a significant fraction of the overall measured NMVOC mass loadings and opens up the possibility of identifying and mitigating emissions or at least the build-up of pollutants in a shallow inversion.
Figure
Contributions of various sources to the total NMVOC mass loading observed at Bode, a semi-urban site in the Kathmandu Valley.
According to two widely used emission inventories, namely REAS v2.1 (Regional
Emission inventory in ASia) and EDGAR v4.2 (Emissions Database for Global
Atmospheric Research)
The EDGAR v4.2 inventory (for the full year 2008) attributes only
10.6
The REAS v2.1 inventory (for the year 2008) estimates that 67.2
The EDGAR inventory considers solvent use (66
The annual Nepalese inventory (for the year 2000) considers solvent and paint
use to be the second largest contributor to the anthropogenic NMVOC emissions
in Nepal, while industries are considered to make an insignificant overall
contribution (0.7
Traffic was considered to contribute only between
Comparison of the PMF-derived contribution of anthropogenic sources with NMVOC source contribution according to the existing Nepalese, REAS and EDGAR emission inventories.
Inefficient biomass co-fired brick kilns are a unique industrial source in
the Kathmandu Valley and contributed significantly (
Only two sources, domestic fuel usage (on account of the changed heating
demand) and agricultural waste burning are expected to have significant
seasonality. Jointly, they account for less than 10
The REAS v2.1 emission inventory for the Kathmandu Valley, however,
seems to require large corrections. While our analysis of the REAS inventory
was restricted to December and January, annual averages of individual sources
differ by less than
Contribution of PMF-derived source factors to acetonitrile and aromatic NMVOCs. Source names are abbreviated as follows: MD is mixed daytime, MI is mixed industrial, UI is unresolved industrial, BK is brick kiln, TR is traffic, RB is residential burning and waste disposal, SE is solvent evaporation, and BG is biogenic.
Emission ratios of NMVOCs
Figure
Residential biofuel use and waste disposal contributed
Figure
Contribution of PMF-derived sources to formamide,
acetamide, isocyanic acid and formic acid. Source names are abbreviated as
follows: MD is mixed daytime, MI is mixed industrial, UI is unresolved
industrial, BK is brick kiln, TR is traffic, RB
Figure
Contribution of PMF-derived sources to
1,3-butadiyne and oxygenated NMVOCs
such as methanol, acetone, acetaldehyde, ethanol and acetic acid. Source
names are abbreviated as follows: MD is mixed daytime, MI is mixed
industrial, UI is unresolved industrial, BK is brick kiln, TR is traffic,
RB
Figure
During the first period, the contribution of traffic was found to be higher
(
Daily mean relative contribution of the eight PMF-derived sources during the SusKat-ABC campaign
Figure
The consequence of including only a subset of NMVOCs is an underestimation of
the OH reactivity and hence ozone production potential, which scales directly
with the OH reactivity. For the city of Lahore,
Daytime
Contribution of the eight PMF-derived sources to SOA formation in the Kathmandu Valley.
SOA production was calculated using the concentrations and the known SOA
yields for benzene, toluene, styrene, xylene, trimethylbenzenes, naphthalene
and isoprene
The PMF model results reveal several new results
regarding the source apportionment of NMVOCs in the Kathmandu Valley.
Speciation of NMVOCs in the emission inventory for Nepal only includes
compound classes (e.g., alkanes and alkenes) and not specific compounds.
This imposes certain limitations while comparing emission inventories with
the compounds measured in our study. However, the existing emission
inventories (e.g., REAS v2.1, EDGAR v4.2;
This study has provided quantitative information regarding the contributions of the major NMVOC sources in the Kathmandu Valley. This will enable focused mitigation efforts by policy makers and practitioners to improve the air quality of the Kathmandu Valley by reducing emissions of both toxic NMVOCs and formation of secondary pollutants. The results will also enable significant improvements in existing NMVOC emission inventories so that chemical transport models can be parameterized more accurately over the South Asian region and the air quality–climate predictions by models can become more reliable.
All the data reported in this article can be obtained from the corresponding author by sending an email to vsinha@iisermohali.ac.in. The primary data have been submitted to the SUSKAT data repository, which will be made publicly accessible in due course.
Sections of this study were submitted in part for the fulfilment of the PhD work of CS carried out under the supervision of VS at IISER Mohali. The VOC dataset QA–QC and analyses were performed by CS and VS, whereas BS designed and set up the PMF model and ensured QA–QC of PMF output, which was performed by CS. AP helped with the interpretation of PMF results and suggested grab sampling experiments at an early stage. CS, VS and BS wrote the paper and all co-authors discussed the results and commented on the paper.
The authors declare that they have no conflict of interest.
Chinmoy Sarkar and Vinayak Sinha acknowledge the support extended by the founding director of IISER Mohali, Narayanasami Sathyamurthy to enable participation of the IISER Mohali team in the SusKat-ABC campaign. Chinmoy Sarkar acknowledges the Ministry of Human Resources and Development (MHRD), India, and IASS Potsdam, Germany, for funding with a service contract. IASS Potsdam funded the deployment of the PTR-TOF-MS by the IISER Mohali team in Kathmandu and local logistical support was provided by Khadak S. Mahata, Dipesh Rupakheti and Bhogendra Kathayat at the Bode site.
This study was partially supported by core funds of ICIMOD contributed by the governments of Afghanistan, Australia, Austria, Bangladesh, Bhutan, China, India, Myanmar, Nepal, Norway, Pakistan, Switzerland and the United Kingdom.Edited by: Elizabeth Stone Reviewed by: two anonymous referees