NMVOC measurements during this study were performed in the winter season from 19 December 2012 until 30 January 2013 at Bode (27.689∘ N, 85.395∘ E, 1345 ma.m.s.l.) in the Bhaktapur district, which is a suburban site located in the westerly outflow of the KMC. The land use in the vicinity of the measurement site consisted of the following cities – KMC (∼ 10 km to the west), Lalitpur Sub-Metropolitan City (∼ 12 km south-west of the site) and Bhaktapur Municipality (∼ 5 km south-east of the site). The site is located in the Madhyapur Thimi Municipality. In addition, the region north of the site had a small forested area (Nilbarahi Jungle, ∼ 0.5 km2 area) and a reserve forest (Gokarna Reserve Forest, ∼ 1.8 km2 area) at approximately 1.5 and 7 km from the measurement site, respectively. Several brick kilns were located in the south-east of the site within a distance of 1 km. Major industries were located mainly in the Kathmandu and Patan cities, whereas the Bhaktapur Industrial Estate was located at around 2 km from the measurement site (in the south-eastern direction). A substantial number of small industries were also located in the south-eastern direction. The Tribhuvan International Airport is located about 4 km to the west of the Bode site. A detailed description of the measurement site and prevalent meteorology is already provided in a paper related to this special issue . A zoomed view of the land use in the vicinity of the measurement site is provided in Fig. .

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 amu. The PTR-TOF-MS instrument works on the basic principle of soft chemical ionization (CI) in which reagent hydronium ions (H3O+) react with analyte NMVOC molecules with a proton affinity (P.A) greater than that of water vapor (165 kcalmol-1) to form protonated molecular ions (with m/z ratio = molecular ion +1), enabling the identification of NMVOCs . As all the relevant analytical details pertaining to the PTR-TOF-MS instrument, ambient air sampling and the quality assurance of the NMVOC dataset have already been provided in detail in , only a brief description of the ambient air sampling and the analytical operating conditions is provided here. Ambient air sampling was performed continuously through a Teflon inlet line protected from floating dust and debris using an in-line Teflon membrane particle filter. The PTR-TOF-MS was operated at a drift tube pressure of 2.2 mbar, a drift tube temperature of 60 ∘C and a drift tube voltage of 600 V, which resulted in an operating E / N ratio of ∼ 135 Td (E = electrical field strength in Vcm-1; N = buffer gas number density in moleculecm-3 and 1 Td = 10-17 Vcm-2). Identification of several previously unmeasured and rarely measured NMVOCs were achieved due to the high mass resolution (m/Δm> 4000) and low detection limit (few tens of parts per trillion) of the instrument. For the quality assurance of the measured NMVOC dataset, the instrument was calibrated twice during the measurement period and regular instrumental background checks were performed using zero air at frequent intervals. A detailed description of the sensitivity characterization of the instrument and the quality assurance of the primary dataset is available in .

During the measurement period, a total of 37 NMVOC signals (m/z) were observed in the PTR-TOF-MS mass spectra that had an average concentration of > 200 ppt. The cutoff of an average concentration of > 200 ppt was employed, keeping in mind the highest instrumental background signals observed during the campaign, so as to have complete confidence that the ion signals were attributable to ambient compounds. For mass identifications at a particular m/z ratio, further quality control was applied. Firstly, only those ion peaks for which there was no contribution from the major shoulder ion peaks within a mass width bin of 0.005 amu were considered for the mass assignments. Next, ion peaks devoid of any variability (that is the time series profile was flat) were not considered for mass assignments at all. Further details, including some known interferences that were identified and taken into account, are available in . Table S1 in the Supplement lists the identified 37 NMVOCs, the corresponding m/z attributions (with references to a few previous works that reported the same compound assignment, wherever applicable) and the elemental molecular formula.

Grab samples from garbage fires (termed garbage burning) were collected near the measurement site (∼ 200 m in the northern direction, upwind of Bode; 27.690∘ N, 85.395∘ E) on 7 December 2014 between 15:00 and 15:03 LT. A brick kiln grab sample was collected on 6 December 2014 from a fixed chimney bull's trench brick kiln (FCBTBK) co-fired using coal, wood dust and sugarcane extracts. Figure S1 in the Supplement shows pictures of the grab sample collection and the instrumental setup for the analysis. All of the air samples were collected in 2 L glass flasks that had been validated for the stability of NMVOCs and were analyzed within 38 h of the collection (on 9 December 2014 between 03:42 and 04:05 LT). The whole air samples were diluted (dilution factor of 9.93) using zero air for the quantification of NMVOCs present in the grab samples using a proton transfer reaction quadrupole mass spectrometer (PTR-QMS) instrument . The average background signals (zero air) were subtracted from each m/z channel and stable data of at least 10 cycles (∼10 min) were considered for the calculation of mixing ratios as per the protocol described by . The zero air background for the m/z reported was 0.04 ± 0.05 ppb, 0.04 ± 0.04 ppb, 0.04 ± 0.06 ppb, 0.07 ± 0.08 ppb, 0.10 ± 0.11 ppb, 0.02 ± 0.06 ppb and 0.02 ± 0.05 ppb for acetonitrile, benzene, toluene, the sum of C8 aromatics, the sum of C9 aromatics, styrene and naphthalene, respectively. The concentration range in the grab samples was 4 ± 0.3 to 323 ± 8 ppb for acetonitrile, 27 ± 4 to 339 ± 19 ppb for benzene, 32 ± 5 to 150 ± 14 ppb for toluene, 40 ± 6 to 113 ± 8 ppb for C8 aromatics, 33 ± 6 to 62 ± 12 ppb for C9 aromatics, 11 ± 1.3 to 95 ± 17 ppb for styrene and 11 ± 1.5 to 64 ± 9 ppb for naphthalene.

The US EPA Positive Matrix Factorization (PMF) receptor model version 5.0 was used for source apportionment of NMVOCs in the Kathmandu Valley. The model is based on the multi-linear engine (ME-2) approach and has been described in detail by . From a data matrix of a number of NMVOCs in a given number of samples, the PMF model helps to determine the total number of possible NMVOC source factors, the chemical fingerprint (source profile) for each factor, the contribution of each factor to each sample, and the residuals of the dataset using the following equation : Xij=∑k=1pgikfkj+eij, where Xij is the NMVOC data matrix with i number of samples and j number of measured NMVOCs, which are resolved by the PMF to provide p number of possible source factors with the source profile f of each source and mass g contributed by each factor to each individual sample, leaving the residuals e for each sample. To obtain the solution of Eq. (1), sum of the squared residuals (e2) and variation in data points (σ2) are inversely weighted in PMF as expressed by the following equation : Q=∑i=1n∑j=1m(eijσij)2=∑i=1n∑j=1m(Xij-∑k=1pgikfkjσij)2, where Q is the object function and a critical parameter for PMF, n is the number of samples, and m is the number of considered species. The original data should always be reproduced by the PMF model within the uncertainty considering the non-negativity constraint for both the predicted source profile and the predicted source contributions. The explained variability (EV) as given below demonstrates the relative contribution of each factor to the individual compound and can be expressed as EVkj=∑i=1n|gikfkj|/σij∑i=1n(∑k=1p|gikfkj|+|eij|)/σij.

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 . Random errors can be caused due to the existence of infinite solutions with different gik, fkj and eij matrices but identical Q=∑i=1n∑j=1m(eij/σij)2. In the bootstrap runs, the time series is partitioned into smaller segments of a user-specified length and the PMF is run on each of these smaller segments for the same number of factors as the original model run. The model output of each bootstrap run is mapped onto the original solution using a cross-correlation matrix of the factor contributions gik of a given bootstrap run with the factor contributions gik of the same time segment of the original solution using a threshold of the Pearson's correlation coefficient (R) >0.6 as suggested by . The bootstrap factor is assigned to the factor with which it is most strongly positively correlated, as long as the value of R is greater than 0.6. If it cannot be attributed to any factor of the original solution it will be termed unmapped. The presence of a high fraction unmapped factor (>20 %) is a clear indication of large random errors (introduced by a few critical observations that drastically impact factor profiles) and should be investigated carefully . In our analysis, no unmapped factors were present.

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 (µgm-3) and percentage of each compound attributed to the factor profile of each of the factors during the bootstrap runs. It also ascertains for each compound whether or not the original solution for that factor falls into the interquartile range of the bootstrap results and provides this information in a table format.

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 . This rotational ambiguity is caused due to the existence of multiple solutions that have a Q similar to the solution produced by the PMF model but different factor profiles and factor contributions. Thus, the model will find different local minima of the residual matrix while determining the factor contribution matrix (gikfkj). The coexistence of different solutions for the factor contribution matrix (gikfkj) with the same sum of the scaled residuals Q=∑i=1n∑j=1m(eij/σij)2 is called the rotational ambiguity of the model. The PMF 5.0 has a new feature called the constrained model operation in which the rotational ambiguity of the model can be constrained using external knowledge of the source composition (fkj) or contribution (gik) matrix. For instance, if a source were inactive for a particular period, then the contribution due to that factor during that time period could be pulled to zero in the model to provide more robust output. Alternatively, the emission ratios obtained from a particular source through samples collected at the source can also be used to constrain the model. Constraining the PMF model using such external knowledge gives rise to a penalty in Q (the object function) and a maximum penalty of 5 % is recommended as a reasonable threshold . A detailed discussion of the use of constraints in a receptor model has been provided in previous studies .

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 (-999). To ensure that differential uncertainties do not drive the object function Q and give undue weighting to calibrated organic ions while constructing source profiles, we followed the procedure used by for source apportionment of NMVOCs in the Houston Ship Channel area, assigning a constant uncertainty of 20 % for all the ions. Due to its erratic time series profile, HCN (m/z = 28.007) was classified as a weak species in the PMF input while all other ions were classified as strong species. For weak species, the stated uncertainty is tripled to reduce their impact on the scaled residual and hence Q. All the input data were converted from mixing ratios of ppb to mass concentrations (µgm-3) using the relevant temperature, pressure and molecular weight and the total measured NMVOC concentration was calculated by adding the mass concentrations of all measured NMVOCs. This conversion allows the calculation of the explained variability for the total VOC mass and comparison of the results with emission inventories. The conversion does not introduce significant additional uncertainty and the variability induced by the temperature (average range observed was 5–20 ∘C) has largely been taken into account by running the model with a 5 % extra modeling uncertainty. The total VOC mass is classified as a weak species in the PMF input . All the measured ions had a signal-to-noise (S / N) ratio greater than 2. Table S2 in the Supplement shows the S / N ratios for all input NMVOC species used in the PMF along with other statistical parameters of the dataset.

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 Q/Qtheoretical ratio, the physical plausibility of the factors and constraints imposed by the rotational ambiguity of the solution, an eight-factor solution was deemed to be the best for this dataset. For the data presented in this study, the Q/Qtheoretical ratio is <1 even for a three-factor solution with no physical plausibility, and hence the absolute number does not help to decide the optimum number of factors. Supplement Fig. S2 shows clearly that the number of factors has almost no impact on how well the total mass is reproduced by the model, but the last distinct drop in the Q/Qtheoretical ratio is seen when the number of factors is increased to eight. When fewer than seven factors were employed, several source profiles appeared to be mixed (Fig. S3a, b), indicating inadequate resolution of sources. The solution incorporating seven factors was considered inappropriate, as the daytime biogenic emissions and photochemical sources could not be separated from the nighttime combustion source of isoprene in the seven-factor solution. Even when the model was nudged towards separating the biogenic emissions and the anthropogenic combustion sources of isoprene using the constraint mode, this separation could only be accomplished with a large penalty on Q in the seven-factor solution. The nine-factor solution had too much rotational ambiguity and assigned brick kiln emissions to two largely co-linear factors, both of which had an incomplete source profile with respect to aromatic compounds and were essentially created to better account for minor variations in the emission ratios associated with brick kiln emissions during the firing up period and the continuous operation later in the campaign (Fig. S3c).

The diagnostics for the eight-factor solution are summarized in Table . The eight factors were (1) traffic, (2) residential biofuel use and waste disposal, (3) mixed industrial emissions, (4) biomass co-fired brick kilns, (5) unresolved industrial emissions, (6) solvent evaporation, (7) mixed daytime source, and (8) biogenic emissions. A detailed description for the identification and the attribution of the eight-factor solutions is provided later in Sect. . The primary data strongly support an eight-factor solution. The top two to three compounds explained by each of the eight factors have a much higher R when their input time series is correlated compared to the R obtained when their time series is correlated with the time series of any other compound (Supplement Table S5).

The traffic factor explains more than 60 % of the variability in toluene and C8 and C9 aromatics. The time series of toluene and C8 and C9 aromatics correlate with R>0.96 for all possible pairs when the original time series of these compounds are correlated with each other. The R of the time series of these same compounds with the time series of styrene is lower (0.81–0.85) while a correlation of their time series with all other compounds yields R<0.78. This indicates toluene and the sum of C8 and C9 aromatics share a major common source with each other that is not shared by other compounds, namely the traffic source. Hence, a PMF solution with less than six factors, which is incapable of capturing the traffic source, is not a better representation of the reality.

For styrene the highest correlation is with furan (R=0.87), indicating that the two compounds have a significant source in common, which styrene also shares with higher aromatics and propyne (R=0.86), but the lower R of styrene with the aromatic compounds indicates that styrene has at least two dominant sources with distinct emission ratios. These sources are the traffic source (explaining roughly 40 % of the styrene) and the residential burning source, which explains 30 % of the styrene and furan variability. These two sources are separated only with a six-factor solution.

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 (R=0.89), nitromethane (R=0.82) and naphthalene (R=0.81) but all of these compounds also have other sources that are either not shared with benzene or have different emission ratios. This source appears in the three-factor solution but its source profile is contaminated with mixed industrial emission. The closure period of brick kilns is only fully captured and restricted to the brick kiln factor after the number of factors is increased to seven.

The mixed industrial source explains 66 % of the ethanol variability, but this compound has a relatively low R with all other compounds (0.73 with propene and 0.7 with nitromethane and acetonitrile < 0.66 with the rest) indicating that there must be at least two distinct ethanol sources with different source fingerprints. A second distinct ethanol source in the form of solvent evaporation, however, separates from the mixed daytime factor only in the seven-factor solution.

The mixed daytime factor primarily contains photochemically formed compounds, most notably isocyanic acid, which shows a strong correlation with its own precursors formamide (R=0.85) and acetamide (R=0.82). Figure S8 presents a reaction schematic for the formation of formamide and isocyanic acid. This compound has a much weaker correlation with other compounds, which have other sources in addition to the photochemical source (R=0.5 to 0.58 for formaldehyde, acetaldehyde, the nitronium ion, formic acid and acetic acid). This factor should ideally be restricted to photochemically formed secondary compounds; however, it remains heavily contaminated with nighttime primary emissions during the second half of the campaign until the number of factors is increased to eight (Fig. S3c). Even the eight- and nine-factor solutions still contain some minor contamination from primary emissions. Hence, the name of the source is retained as mixed daytime source.

The solvent evaporation factor is characterized by acetaldehyde and acetic acid, which have their strongest correlation with each other (R=0.82). Apart from this, the defining compound, acetaldehyde, shows moderate correlation with formaldehyde (R=0.72) and acetone (R=0.68) but only the former correlates with acetic acid (R=0.85) as it shares both the solvent evaporation source and the photooxidation source with acetaldehyde. Conversely, acetone correlates much more strongly with methyl ethyl ketone (R=0.95), methyl vinyl ketone (R=0.86), and isoprene (R=0.79) and hence shares the biogenic emission source in addition to the solvent evaporation factor. While these three daytime sources are resolved in the seven-factor solution, their source profiles continue to be contaminated with primary emissions. While the same can be pushed around from the biogenic factor into the mixed daytime factor using rotational tools, they cannot be sufficiently removed from both until an eighth factor is allowed.

The unresolved industrial emission factor explains a significant fraction of the 1,3-butadiyne, which shares most of its sources with methanol (R=0.9). The source profile also captures several other compounds with a lower correlation with 1,3-butadiyne, including propanenitrile (R=0.86), acrolein + methylketene (R=0.82) and propene (R=0.8). The R obtained while cross correlating the time series of 1,3-butadiyne with that of ethanol, the defining compound of the mixed industrial source profile, is only 0.73 and ethanol correlates only weakly with acrolein + methylketene (R=0.59), indicating that these mixed industrial emissions and unresolved industrial emissions represent distinct sources, which can only be resolved in a eight-factor solution.

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 of 108 h but represents a multiple of 24 h and hence ensures that each bootstrap run contains 4 full days' worth of data. There were no unmapped factors in the bootstrap runs.

Figure shows the correlation between the estimated total measured NMVOC concentrations calculated using the contributions from all factors (vertical axis) with measured total measured NMVOC concentrations (horizontal axis). An excellent correlation (r2=0.99) indicates that the PMF model can explain almost all variance in the total measured NMVOC concentrations.

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 (r2=0.27) factors as well as the residential biofuel use and waste disposal factor with traffic factor (r2=0.42). Since this is a new feature and has only recently been used by for ambient air data, a detailed description of the implementation procedure and an analysis of how the constraints affected the model output are provided here. Several constraints were used to obtain a more robust PMF solution.

First, the upper limit for the emission ratio of the individual aromatic compounds to isoprene as reported by was used to constrain the factor profile of primary biogenic emissions. As a small fraction of the biogenic isoprene gets attributed to other daytime factors (mixed daytime) by the PMF model, the same constraints were used on the mixed daytime factor and the solvent evaporation factor as well.

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 (r2=0.42), while the brick kiln emission factor correlated with the mixed industrial emissions factor (r2=0.27). This indicates that there was substantial rotational ambiguity for these two factor pairs.

Nudging was performed by exerting a soft pull, allowing for a maximum 0.2 % change in Q for each constraint. A soft pull allows the change in the Q value up to a certain limit by pulling the values to a target value for an expression of elements (the emission ratio). If no minima for which the change in Q=∑i=1n∑j=1m(eij/σij)2 is less than 0.2 % in the gikfkj matrix after fkj has been constrained could be found, no change was made and the original solution was retained. If the condition can be met without changing Q by more than the threshold, the revised factor profiles will be used as the base upon which the next constraint in the list of constraints will be executed.

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 (r2=0.48), while the mixed daytime factor correlated better with ambient temperature (r2=0.42). The factor profile of the solvent evaporation correlates better with the rise in solar radiation and temperature after sunrise (07:00–09:00 LT; r2=0.53). Table  represents the emission ratios used to nudge the biogenic, mixed daytime and solvent evaporation factors and provides the corresponding ERs before and after nudging.

It can be seen that most constraints on the aromatic to isoprene ratio could be executed without exceeding the penalty on Q. In the biogenic factor, only the naphthalene / isoprene ratio could not be constrained. The solvent evaporation and mixed daytime factors contain only a small fraction of the total daytime isoprene (8 and 7 %, respectively). Given the very small overall isoprene mass in these two factor profiles, a few additional ratios did not meet the constraining criteria in these factor profiles (namely the acetonitrile / isoprene and trimethylbenzenes / isoprene ratios in the mixed daytime factor and the xylenes / isoprene and naphthalene / isoprene ratios in the solvent evaporation factor). Some of these compounds (such as naphthalene) could not be constrained in the same factors while constraining the ERs with respect to acetic acid.

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 LT seem to indicate that an additional weak combustion source with a high naphthalene emission ratio is possibly poorly represented by the current eight-factor solution. Cooking on three-stone fires is known to emit large amounts of benzene and naphthalene and the temporal profile of such a cooking source could overlap with that of the garbage fires. It can be noted that three-stone fires are still a common way to cook for construction workers and brick kiln workers staying in temporary camps in the Kathmandu Valley. This would make it challenging for the model to separate these two sources. We will henceforth refer to the garbage-burning factor as the residential biofuel use and waste disposal factor.

Figure S5a in the Supplement shows the G-space plots for two factors, namely biomass co-fired brick kilns and mixed industrial emissions. A stronger correlation (r2=0.42), which reduced to r2=0.18, existed in the original solution prior to nudging with ERs of FCBTBK grab samples. Similarly, after nudging with ERs of the garbage-burning grab sample the correlations between residential biofuel use and waste disposal were reduced from 0.27 to 0.18, as shown in Fig. S5b. Thus, the new solution fills the solution space better.

Table summarizes the aromatics / benzene ERs derived from the PMF (before and after nudging) and its comparison with the ERs obtained from grab samples for biomass co-fired brick kilns and residential biofuel use and waste disposal sources. These ERs are also compared with the ERs reported for three-stone firewood stoves in and the mixed-garbage burning and open-cooking-fire sources reported for Nepal in .

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 instead of our garbage-burning grab samples resulted in a larger penalty on Q and did not improve the representation of the biogenic, mixed daytime and solvent evaporation factors.

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 . In CPF, the probability of a particular source contribution from a specific wind direction bin exceeding a certain threshold is employed and is calculated as follows: CPF=mΔθnΔθ, where mΔθ represents the number of data points in the wind direction bin Δθ that exceeded the threshold criterion and nΔθ represents the total number of data points from the same wind direction bin. For this study, Δθ was chosen as 30∘ and data for wind speed >0.5 m-1 were used.

The ozone formation potential of individual NMVOCs was calculated as described by the following equation : Ozone  production  potential=(∑k(VOCi+OH)[VOC]i)×OH×n. For the ozone production potential calculation, the average hydroxyl radical concentration was assumed to be [OH] =1×106 moleculescm-3 with n=2 and only data pertaining to the mid-daytime period were considered (11:00–14:00 LT).

SOA yield of a particular NMVOC depends on the NOx conditions and previously reported NOx-rich conditions in the Kathmandu Valley. Therefore, SOA production was calculated by using reported SOA yield at high-NOx conditions according to the following equation: SOA  production=[VOC]i×SOA  yield  of  VOCi.

Figure represents the factor profiles of all eight factors resolved by the PMF model. Grey bars (left axis) indicate the mass concentrations and red lines with markers (right axis) show the percentage of a species in the respective factor.

Identification and attribution of these factors is discussed in detail in the following sections.

Figure shows the CPF plots that were used to examine the spatial profile of the eight different PMF source factors. For the CPF plots, only data with wind speed >0.5 ms-1 were considered. Six factors, namely traffic, residential biofuel use and waste disposal, mixed industrial emissions, unresolved industrial emissions, solvent evaporation, and biomass co-fired brick kilns, could be clearly associated with anthropogenic activities and are therefore likely to be impacted by spatially fixed sources, while one factor (mixed daytime) was related to photochemistry. One factor, biogenic emissions, is natural but can also be attributed to spatially fixed sources such as forests.

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, ∼0.4 for N-NE and S-SW, and 0.2 for SE), indicating that this source is spatially distributed throughout the Kathmandu Valley. Only for the wind sector from SW to NW is the conditional probability of this source low. The reason for this low conditional probability is that every day in the afternoon winds from the western mountain passes reach the receptor site. The same wind direction is extremely rare after sunset and during the early morning hours, when residential biofuel use and waste disposal mostly occur. Consequently, the conditional probability plot shows low conditional probabilities for this wind sector.

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 (p=0.4–0.6), where Bhaktapur Industrial Estate is located within a distance of 3–4 km upwind of the receptor site. Conditional probabilities of 0.2–0.4 were observed for the NW wind direction where several industries are located.

For brick kilns the highest conditional probability was observed for air masses reaching the receptor site from the NE to SE (p∼0.4), which had several active brick kilns near the Bhaktapur Industrial Estate, which was ∼4 km upwind of the receptor site.

It is interesting to note that the unresolved industrial emissions factor shows a clear directional dependence (p=0.5–0.7 for the NE–SW wind sector), indicating that this factor, too, can be attributed to spatially fixed sources in the Bhaktapur Industrial Estate and Patan Industrial Estate. Polymer production and manufacturing industries for adhesives, paints and/or pharmaceuticals upwind of the site likely contributed towards the measured NMVOC mass of the unresolved industrial factor.

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 (<0.5 ms-1) the maximum conditional probabilities were observed for mixed industrial emissions, unresolved industrial emissions and brick kilns (0.25, 0.18 and 0.18, respectively), which indicates that emissions from these sources tended to accumulate in a shallow boundary layer during stagnant conditions in the Kathmandu Valley. Therefore, using taller chimney stacks, at least for combustion sources, to prevent accumulation of emissions in a shallow boundary layer could potentially improve the air quality of the valley during foggy nights.

The mixed daytime factor shows no obvious directional dependence for the conditional probability of recording values above the average at the receptor site (p>0.3 for all directions). Slightly higher conditional probabilities (p∼0.6) are recorded for air masses reaching the receptor site from the N-NE and S-SW wind directions.

The biogenic factor showed high conditional probabilities for air masses reaching the receptor site from the SW to N direction (p=0.5 to 1) where a few forested areas such as Nilbarahi Jungle and Gokarna Reserve Forest were located. Also, forested areas on mountain slopes in the SW and NW directions and the midday fetch region coming frequently from this sector explain the directional dependency of the biogenic factor.

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  shows a pie chart summarizing contributions of individual sources to the total measured NMVOC mass loading. Total measured NMVOC mass loading was calculated by summing up the concentrations of individual measured NMVOCs (in µgm-3). The distribution shows that biogenic sources and the mixed daytime factor contributed only 10 and 9.2 %, respectively, to the total measured NMVOC mass loading, while all the anthropogenic sources collectively contributed ∼80 % to the total measured NMVOC mass loading.

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) , and the existing Nepalese inventory obtained from the International Centre for Integrated Mountain Development's (ICIMOD) database, residential biofuel use is considered to be the predominant source of anthropogenic NMVOC emissions in Nepal. When the analysis is spatially restricted to the Kathmandu Valley for those inventories that provide gridded emissions (as shown in Fig. ), differences between EDGAR v4.2 and REAS v2.1 appear.

The EDGAR v4.2 inventory (for the full year 2008) attributes only 10.6 % of the total anthropogenic NMVOC emissions in the Kathmandu Valley (85.2–85.5 longitude and 27.6–27.8 latitude) to be due to residential biofuel use and an additional 8.9 % to solid waste disposal. These numbers are in reasonable agreement with our PMF output, which attributes 13.5% instead of 19.5 % of the total measured NMVOC mass to these two sources combined. The EDGAR v4.2 inventory provides only spatially resolved data, not seasonally resolved data.

The REAS v2.1 inventory (for the year 2008) estimates that 67.2 % of the total wintertime (December and January) anthropogenic NMVOC emissions in the Kathmandu Valley (85.25–85.5 longitude and 27.5–27.75 latitude) originates from residential and commercial biofuel use – a significant overestimation when the numbers are compared to our PMF output and the EDGAR v4.1 inventory. The national Nepalese emission inventory also apportions a large share of the total national annual NMVOCs emissions to residential and commercial biofuel use (83.1 %). It therefore appears that while apportioning the emissions spatially, the REAS v2.1 emission inventory does not fully account for the socioeconomic differences between rural and urban areas. The EDGAR v4.2 emission inventory, however, seems to apportion most of the national consumption of liquefied petroleum gas (LPG) for cooking to the highly urbanized Kathmandu Valley and correspondingly scales down the emission from biofuel use within the Kathmandu Valley. In absolute terms the annual NMVOC emissions attributed to domestic fuel usage within the Kathmandu Valley by EDGAR v4.2 are a factor of 3.6 lower compared to the annual NMVOC emissions attributed to this sector by REAS v2.1.

The EDGAR inventory considers solvent use (66 %) and mixed industrial emissions to represent the second most important source of NMVOCs. Solvent use and other industrial emissions (8.5 %) combined account for 74.5 %. Collectively they are considered to contribute ∼ 10 % to the total anthropogenic NMVOC mass in the EDGAR v4.2 inventory, while our PMF results attribute 52.8 % of the measured NMVOCs to solvent use and industrial emissions combined. It should be noted that solvent use and other factors related to industrial emissions (mixed industrial and unresolved industrial) must be combined while comparing our PMF output with emission inventories. Both the mixed industrial emission factor and the unresolved industrial emission factor contain a significant NMVOC mass fraction from industrial solvent use, but they also contain combustion-related emissions from industrial units. Unfortunately, industrial solvent use and industrial combustion emissions from co-located units cannot be cleanly segregated using the PMF model, which relies on spatiotemporal patterns while building factor profiles. Overall, our PMF output agrees with the EDGAR v4.2 inventory, which shows that industries are the dominant source of NMVOCs in the Kathmandu Valley. According to the REAS v2.1 inventory, solvent use is considered to be the second most dominant contributor (29.8 %) to wintertime NMVOC emissions in the Kathmandu Valley. Solvents and other industrial emissions (0.9 %) combined account for 30.7 % of the total wintertime NMVOC emissions in the REAS v2.1 emission inventory. Since most of the national consumption of solvents and a significant share of Nepal's industrial production is concentrated in the Kathmandu Valley, the discrepancies between the REAS v2.1 emission inventory and our results indicate that the REAS v2.1 emission inventory does not sufficiently account for the special status of the Kathmandu Valley while spatially apportioning emissions. The emissions that EDGAR v4.2 attributed to solid waste disposal, industries, the transport sector, and solvent use within the Kathmandu Valley are a factor of 17.4, 14.0, 7.4 and 3.3 times higher, respectively, compared to what the REAS v2.1 inventory attributes to the same sectors for the same geographical area.

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 %). These numbers cannot be compared to our results in a meaningful manner, as the national emissions in particular for sectors such as domestic fuel usage and agricultural waste burning may be dominated by the rural hinterland, while our PMF results apply to the largest urban agglomeration in Nepal.

Traffic was considered to contribute only between ∼ 1.3 % (in the REAS v2.1 inventory) and a maximum of ∼ 2.6 % (in EDGAR v4.2 inventory) of the total anthropogenic NMVOC emissions in the Kathmandu Valley. This stands in stark contrast to the results of our PMF analyses, which indicate that traffic contributes ca. 20 %, solvent evaporation and industrial solvent and/or chemical usage accounts for ca. 36 % (unresolved industrial emissions + solvent evaporation) and other industrial emissions (mixed industrial emissions + brick kilns) account for ca. 30 % of the total measured anthropogenic NMVOC mass loading in the Kathmandu Valley. According to the recent study of the vehicle fleet in the Kathmandu Valley , transport sector NMVOC emissions in the Kathmandu Valley for the year 2010 amounted to 7654 tyr-1, a number that is 10 times higher than the number currently in the EDGAR v4.2 inventory and 72 times higher than the number currently in the REAS v2.1 inventory. If the emission estimate of were incorporated into the EDGAR v4.2 inventory without any further changes, the percentage share of transport sector emissions to the total NMVOC emissions would increase to 38.7 %, while the contribution of domestic fuel usage and waste disposal would drop to 12.7 % (PMF 13.5 %) and the contribution of industrial emissions and solvent use would drop to 48.6 % (PMF 52.8 %). Our PMF results, however, seem to suggest that 2012 transport sector emissions decreased by ∼ 50 % compared to the 2010 emissions presented in , possibly due to a reduction in the number of older vehicles in the fleet.

Inefficient biomass co-fired brick kilns are a unique industrial source in the Kathmandu Valley and contributed significantly (∼15 %) to the total measured anthropogenic NMVOC mass loading. The existing Nepalese inventory considers contributions of brick kilns only to the emission of particulate matter (PM10 and PM2.5), while the two other emission inventories do not include emissions from brick kilns in the Kathmandu Valley at all. If transport sector NMVOC emissions of ∼ 3800 tyr-1 and an additional ∼ 2400 tyr-1 NMVOC emissions from brick kilns were included in the EDGAR v4.2 emission inventory, the EDGAR emission inventory and our PMF output would agree perfectly (within ±0.2 %) on the relative contribution of all sources, without changing the contribution from any of the other sources.

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 % of the total NMVOC emissions. Since cooking needs persist throughout the year and the decrease in agricultural waste burning outside harvest season may be partially offset by leaf-litter burning (a source currently not in the model), it is likely that the failure to account for seasonal effects imparts an uncertainty of less than 1 % on the overall result of our analysis.

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 ±10 % from the winter values. Therefore, the difference in the time window selected for the analysis cannot explain the observed discrepancies to the EDGAR emission inventory.

Figure represents the pie charts showing contribution of the eight source factors to individual NMVOCs such as acetonitrile, benzene, styrene, toluene, the sum of C8 aromatics (xylenes and ethylbenzene) and the sum of C9 aromatics (trimethylbenzenes and propylbenzene). Maximum contribution to the acetonitrile mass concentration was observed from the unresolved industrial emission sources (∼30 %) followed by the biomass co-fired brick kiln emission (∼24 %) and mixed industrial emission (∼ 20 %) factors. Residential biofuel use and waste disposal features only fourth (∼18 %). The same sources also contribute most to benzene emissions, indicating that fuel usage, rather than its application as a solvent and/or chemical reagent in industrial processes, is responsible for most of the industrial acetonitrile emissions. It also indicates that industrial rather than residential biofuel usage contributes more towards outdoor NMVOC air pollution. Most of the benzene (which is a human carcinogen) can be attributed to biomass co-fired brick kilns (∼37 %) and mixed industrial (∼17 %) and unresolved industrial (∼18 %) sources. Residential biofuel use again featured only fourth as far as the contribution towards mixing ratios of this compound in the outdoor environment is concerned. Table shows a comparison of NMVOCs / benzene ERs for four PMF-derived sources (residential biofuel use and waste disposal, biomass co-fired brick kilns and mixed industrial and unresolved industrial sources) to the ERs obtained from the grab samples collected for garbage burning in the Kathmandu Valley and the previously reported ERs for waste burning, wood burning and charcoal burning sources.

Residential biofuel use and waste disposal contributed ∼28 % of the total styrene that was emitted significantly from waste burning. However, traffic was found to be equally important as a styrene source (∼37 %) in the Kathmandu Valley. Recently, styrene has been detected from traffic and was found to have high ERs with respect to benzene after the cold startup of engines and in liquefied petroleum gas fuel . Biomass co-fired brick kilns and mixed industrial emissions also contribute significantly (∼21 and ∼14 %, respectively) towards styrene mass loadings. Traffic was found to be the most important source of higher aromatics, including toluene, C8 aromatics, and C9 aromatics (>60 %). Biomass co-fired brick kilns were the second largest contributors towards their mass loadings, while residential biofuel usage and waste disposal ranked third.

Figure  shows the pie charts summarizing contributions of PMF-derived sources to two newly quantified compounds in the Kathmandu Valley, namely formamide and acetamide, along with isocyanic acid and formic acid. All these compounds showed maximum contribution from the mixed daytime factor (∼34 to ∼41 %) due to the photooxidation source. As discussed previously in and in Sect. , both formamide and acetamide are formed primarily as a result of photooxidation of amine compounds and N-containing compounds. These can be emitted from the various inefficient combustion processes in the Kathmandu Valley. Photooxidation of these amides further forms isocyanic acid (reaction schematic is shown in Fig. S8 in the Supplement). Apart from the mixed daytime source, unresolved industrial emissions factors also contributed significantly to all these compounds (∼22 to ∼23 %) as they are used as reactants (e.g., formic acid is used as reactant to produce formamide in industries) or are produced during different industrial processes (for example, formamide is produced in pharmaceutical and plastic industries). The solvent evaporation factor contributed ∼19 % to formamide while the biogenic factor contributed ∼14 % to formic acid. Contributions from all the other sources to these NMVOCs amounted to <10 %.

Figure  shows the pie charts with the contributions of the eight sources derived from PMF to 1,3-butadiyne and oxygenated compounds, namely methanol, acetone, acetaldehyde, ethanol and acetic acid. It can be seen from Fig.  that emissions of all these compounds in the Kathmandu Valley were dominated by different industrial activities. The total unresolved industrial emissions factor dominated the contribution to 1,3-butadiyne (∼48 %), methanol (∼35 %) and acetone (∼22 %). Residential biofuel use and waste disposal also contributed significantly to 1,3-butadiyne (∼21 %) and methanol (∼16 %). Traffic was found to have a significant contribution to acetone (∼21 %). It is known that acetaldehyde, ethanol and acetic acid are used as solvents in different industries and it was found that industrial sources obtained from PMF (mixed industrial + unresolved industrial + solvent evaporation) together contributed ∼72 % of the total acetaldehyde, 100 % of the total ethanol and ∼47 % of the total acetic acid. Biogenic sources also had a significant contribution to acetaldehyde and acetic acid (∼17 and ∼14 %, respectively), whereas residential biofuel use and waste disposal contributed ∼15 % of the total acetic acid.

Figure  shows a time series of the daily mean relative contribution of the PMF-derived sources during the SusKat-ABC campaign. As discussed in , the whole campaign can be divided into three different periods: the measurements until the first period (from the start of the campaign until 3 January 2013), which was associated with high daytime isoprene emissions due to strong biogenic emissions; the second period (4–18 January 2013), which was marked by enhancements in acetonitrile and benzene concentrations due to the start of the biomass co-fired brick kilns in the Kathmandu Valley and the third period (19 January until the end of the campaign), in which more oxygenated NMVOCs were observed, which was believed to be due to the stable operation of the brick kilns and more contribution from the industrial sources. PMF-derived results also support these observations, as can be seen in Fig. . It can be seen that from the start of the campaign until 3 January 2013, the contribution of PMF-derived biogenic sources was >20 % for most of the time, while contribution from the brick kilns emission factor was negligible (≤5 %). From 4 until 18 January 2013, the contribution of brick kilns increased significantly (∼20 % to ∼40 %) as almost all brick kilns in the Kathmandu Valley became operational. After 18 January until the end of the campaign, the contribution of brick kilns became lower due to their stable operation.

During the first period, the contribution of traffic was found to be higher (∼20 to ∼30 %) compared to the rest of the campaign. The higher contribution of the mixed daytime source during the second and third parts of the campaign was due to the early morning and daytime photooxidation of the precursor compounds that were emitted as a result of biomass co-fired brick kilns and other biomass burning emissions during these periods. The mixed industrial emissions factor contributed almost equally throughout the campaign (contributing ∼10 to ∼15 %) but the solvent evaporation and the unresolved industrial emissions factor contributed more during the second and third parts of the campaign (increase of ∼10 %).

Figure a shows the source contribution to daytime O3 production potential while Fig. b shows the contribution of different classes of compounds measured in the Kathmandu Valley to the daytime O3 production potential as discussed in . The daytime O3 production potential for individual sources was calculated by summing up the O3 production potential for the individual compounds, which was calculated according to the method described by . The distribution of the daytime O3 production potential obtained from the measurements (Fig. b) shows that ∼70 % of the total daytime O3 production potential was due to the contribution from isoprene and oxygenated NMVOCs, which could presumably indicate dominance of biogenic emissions and photochemistry in the Kathmandu Valley even in the winter. However, the distribution of different sources obtained from PMF to daytime O3 production potential shows that the biogenic factor together with the photochemistry factor (mixed daytime) contributed only ∼30 % of the total O3 production potential. The remaining ∼70 % was contributed by anthropogenic sources. While solvent evaporation contributed the most (∼20 %) to the total daytime O3 production potential, traffic and unresolved industrial emission stood second and third, respectively, in terms of anthropogenic ozone precursor emissions. Residential biofuel use and waste disposal and biomass co-fired brick kilns, while potentially important from a human health perspective, contributed only a minor fraction of the total anthropogenically emitted ozone precursors.

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, reported the maximum contribution of methane and 63 non-methane hydrocarbon to the measured OH reactivity as 14 %. Lahore is a much larger, and by all indications, more polluted city than Kathmandu. Despite high concentration abundances in urban atmospheric environments, the rate constants of these species are typically 100 times lower than compounds like isoprene, and hence their contribution to the total OH reactivity is much lower. For example, even 3 ppm methane (observed only in plumes) would contribute only ∼ 0.5 s-1 to the total OH reactivity and hence make an insignificant contribution to the ozone production potential. Hence, our analyses of the ozone production potential may underestimate the total ozone production potential by 15–25%, if we can extrapolate the observations from another South Asian city like Lahore.

SOA production was calculated using the concentrations and the known SOA yields for benzene, toluene, styrene, xylene, trimethylbenzenes, naphthalene and isoprene . As the biomass co-fired brick kilns and the traffic factors contain most of the reactive aromatic compounds, they appeared to be the dominant contributors to SOA production (as shown in Fig. ) in the Kathmandu Valley.