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The nature and origin of organic aerosol in the atmosphere remain unclear. The gas–particle partitioning of semi-volatile organic compounds (SVOCs) that constitute primary organic aerosols (POAs) and the multigenerational chemical aging of SVOCs are particularly poorly understood. The volatility basis set (VBS) approach, implemented in air quality models such as WRF-Chem (Weather Research and Forecasting model with Chemistry), can be a useful tool to describe emissions of POA and its chemical evolution. However, the evaluation of model uncertainty and the optimal model parameterization may be expensive to probe using only WRF-Chem simulations. Gaussian process emulators, trained on simulations from relatively few WRF-Chem simulations, are capable of reproducing model results and estimating the sources of model uncertainty within a defined range of model parameters. In this study, a WRF-Chem VBS parameterization is proposed; we then generate a perturbed parameter ensemble of 111 model runs, perturbing 10 parameters of the WRF-Chem model relating to organic aerosol emissions and the VBS oxidation reactions. This allowed us to cover the model's uncertainty space and to compare outputs from each run to aerosol mass spectrometer observations of organic aerosol concentrations and O:C ratios measured in New Delhi, India. The simulations spanned the organic aerosol concentrations measured with the aerosol mass spectrometer (AMS). However, they also highlighted potential structural errors in the model that may be related to unsuitable diurnal cycles in the emissions and/or failure to adequately represent the dynamics of the planetary boundary layer. While the structural errors prevented us from clearly identifying an optimized VBS approach in WRF-Chem, we were able to apply the emulator in the following two periods: the full period (1–29 May) and a subperiod period of 14:00–16:00 h LT (local time) on 1–29 May. The combination of emulator analysis and model evaluation metrics allowed us to identify plausible parameter combinations for the analyzed periods. We demonstrate that the methodology presented in this study can be used to determine the model uncertainty and to identify the appropriate parameter combination for the VBS approach and hence to provide valuable information to improve our understanding of OA production.

Air pollution in urban environments has been shown to have a negative impact on air quality and human health, particularly in megacities. Over recent decades, Delhi, India, has suffered high atmospheric pollution, with significant particulate matter (PM) concentrations as a result of anthropogenic activities. Organic aerosols (OAs) are composed of thousands of different chemical species and are one of the main constituents of submicron particles. However, quantitative knowledge of OA composition, their sources and their processes in urban environments is still limited. This is important particularly in India, as Delhi is a massive, inhomogeneous conurbation, where we would expect the apportionment and concentrations to vary depending on where in Delhi the measurements/source apportionment is performed, indicating the need for multisite measurements. This study presents the first multisite analysis carried out in India over different seasons, with a focus on identifying OA sources. The measurements were taken during 2018 at two sites in Delhi, India. One site was located at the India Meteorological Department, New Delhi (ND). The other site was located at the Indira Gandhi Delhi Technical University for Women, Old Delhi (OD). Non-refractory submicron aerosol (NR-PM1) concentrations (ammonium, nitrate, sulfate, chloride and organic aerosols) of four aerosol mass spectrometers were analysed. Collocated measurements of volatile organic compounds, black carbon, NOx and CO were performed. Positive matrix factorisation (PMF) analysis was performed to separate the organic fraction, identifying a number of conventional factors: hydrocarbon-like OAs (HOAs) related to traffic emissions, biomass burning OAs (BBOAs), cooking OAs (COAs) and secondary OAs (SOAs). A composition-based estimate of PM1 is defined by combining black carbon (BC) and NR-PM1 (C-PM1= BC + NR-PM1). No significant difference was observed in C-PM1 concentrations between sites, OD (142 ± 117 µg m−3) compared to ND (123 ± 71 µg m3), from post-monsoon measurements. A wider variability was observed between seasons, where pre-monsoon and monsoon showed C-PM1 concentrations lower than 60 µg m−3. A seasonal variation in C-PM1 composition was observed; SO42- showed a high contribution over pre-monsoon and monsoon seasons, while NO3- and Cl− had a higher contribution in winter and post-monsoon. The main primary aerosol source was from traffic, which is consistent with the PMF analysis and Aethalometer model analysis. Thus, in order to reduce PM1 concentrations in Delhi through local emission controls, traffic emission control offers the greatest opportunity. PMF–aerosol mass spectrometer (AMS) mass spectra will help to improve future aerosol source apportionment studies. The information generated in this study increases our understanding of PM1 composition and OA sources in Delhi, India. Furthermore, the scientific findings provide significant information to strengthen legislation that aims to improve air quality in India.

We present the first real-time composition of submicron particulate matter (PM1) in Old Delhi using high-resolution aerosol mass spectrometry (HR-AMS). Old Delhi is one of the most polluted locations in the world, and PM1 concentrations reached ∼ 750 µg m−3 during the most polluted period, the post-monsoon period, where PM1 increased by 188 % over the pre-monsoon period. Sulfate contributes the largest inorganic PM1 mass fraction during the pre-monsoon (24 %) and monsoon (24 %) periods, with nitrate contributing most during the post-monsoon period (8 %). The organics dominate the mass fraction (54 %–68 %) throughout the three periods, and, using positive matrix factorisation (PMF) to perform source apportionment analysis of organic mass, two burning-related factors were found to contribute the most (35 %) to the post-monsoon increase. The first PMF factor, semi-volatility biomass burning organic aerosol (SVBBOA), shows a high correlation with Earth observation fire counts in surrounding states, which links its origin to crop residue burning. The second is a solid fuel OA (SFOA) factor with links to local open burning due to its high composition of polyaromatic hydrocarbons (PAHs) and novel AMS-measured marker species for polychlorinated dibenzodioxins (PCDDs) and polychlorinated dibenzofurans (PCDFs). Two traffic factors were resolved: one hydrocarbon-like OA (HOA) factor and another nitrogen-rich HOA (NHOA) factor. The N compounds within NHOA were mainly nitrile species which have not previously been identified within AMS measurements. Their PAH composition suggests that NHOA is linked to diesel and HOA to compressed natural gas and petrol. These factors combined make the largest relative contribution to primary PM1 mass during the pre-monsoon and monsoon periods while contributing the second highest in the post-monsoon period. A cooking OA (COA) factor shows strong links to the secondary factor, semi-volatility oxygenated OA (SVOOA). Correlations with co-located volatile organic compound (VOC) measurements and AMS-measured organic nitrogen oxides (OrgNO) suggest SVOOA is formed from aged COA. It is also found that a significant increase in chloride concentrations (522 %) from pre-monsoon to post-monsoon correlates well with SVBBOA and SFOA, suggesting that crop residue burning and open waste burning are responsible. A reduction in traffic emissions would effectively reduce concentrations across most of the year. In order to reduce the post-monsoon peak, sources such as funeral pyres, solid waste burning and crop residue burning should be considered when developing new air quality policy.

Atmospheric aerosol particles are known to have detrimental effects on human health and climate. Black carbon is an important constituent of atmospheric aerosol particulate matter (PM), emitted from incomplete combustion. Source apportionment of BC is very important, to evaluate the influence of different sources. The high-resolution soot particle aerosol mass spectrometer (HR-SP-AMS) instrument uses a laser vaporiser, which allows the real-time detection and characterisation of refractory black carbon (rBC) and its internally mixed particles such as metals, coating species, and rBC subcomponents in the form of HOA + fullerene. In this case study, the soot data were collected by using HR-SP-AMS during Guy Fawkes Night on 5 November 2014. Positive matrix factorisation was applied to positively discriminate between different wood-burning and bonfire sources for the first time, which no existing black carbon source apportionment technique is currently able to do. Along with this, the use of the fullerene signals in differentiating between soot sources and the use of metals as a tracer for fireworks has also been investigated, which did not significantly contribute to the rBC concentrations. The addition of fullerene signals and successful positive matrix factorisation (PMF) application to HR-SP-AMS data apportioned rBC into more than two sources. These bonfire sources are HOA + fullerene, biomass burning organic aerosol, more oxidised oxygenated organic aerosol (MO-OOA), and non-bonfire sources such as hydrocarbon-like OA and domestic burning. The result of correlation analysis between HR-SP-AMS data and previously published Aethalometer, MAAP, and CIMS data provides an effective way of gaining insights into the relationships between the variables and provide a quantitative estimate of the source contributions to the BC budget during this period. This research study is an important demonstration of using HR-SP-AMS for the purpose of BC source apportionment.

Polycyclic aromatic hydrocarbons (PAHs) are ubiquitous pollutants in air, soil, and water and are known to have harmful effects on human health and the environment. The diurnal and nocturnal variations of 17 PAHs in ambient particle-bound PAHs were measured in urban Beijing (China) and Delhi (India) during the summer season using gas-chromatography–quadrupole time-of-flight mass spectrometry (GC-Q-TOF-MS). The mean concentration of particles less than 2.5 µm (PM2.5) observed in Delhi was 3.6 times higher than in Beijing during the measurement period in both the daytime and night-time. In Beijing, the mean concentration of the sum of the 17 PAHs (∑17 PAHs) was 8.2 ± 5.1 ng m−3 in daytime, with the highest contribution from indeno[1,2,3-cd]pyrene (12 %), while at night-time the total PAHs was 7.2 ± 2.0 ng m−3, with the largest contribution from benzo[b]fluoranthene (14 %). In Delhi, the mean ∑17 PAHs was 13.6 ± 5.9 ng m−3 in daytime and 22.7 ± 9.4 ng m−3 at night-time, with the largest contribution from indeno[1,2,3-cd]pyrene in both the day (17 %) and night (20 %). Elevated mean concentrations of total PAHs in Delhi observed at night were attributed to emissions from vehicles and biomass burning and to meteorological conditions leading to their accumulation from a stable and low atmospheric boundary layer. Local emission sources were typically identified as the major contributors to total measured PAHs in both cities. Major emission sources were characterized based on the contribution from each class of PAHs, with the four-, five- and six-ring PAHs accounting ∼ 95 % of the total PM2.5-bound PAHs mass in both locations. The high contribution of five-ring PAHs to total PAH concentration in summer Beijing and Delhi suggests a high contribution from petroleum combustion. In Delhi, a high contribution from six-ring PAHs was observed at night, suggesting a potential emission source from the combustion of fuel and oil in power generators, widely used in Delhi. The lifetime excess lung cancer risk (LECR) was calculated for Beijing and Delhi, with the highest estimated risk attributed to Delhi (LECR = 155 per million people), which is 2.2 times higher than the Beijing risk assessment value (LECR = 70 per million people). Finally, we have assessed the emission control policies in each city and identified those major sectors that could be subject to mitigation measures.

A number of inorganic (nitryl chloride, ClNO2; chlorine, Cl2; and hypochlorous acid, HOCl) and chlorinated, oxygenated volatile organic compounds (ClOVOCs) have been measured in Manchester, UK during October and November 2014 using time-of-flight chemical ionisation mass spectrometry (ToF-CIMS) with the I− reagent ion. ClOVOCs appear to be mostly photochemical in origin, although direct emission from vehicles is also suggested. Peak concentrations of ClNO2, Cl2 and HOCl reach 506, 16 and 9 ppt respectively. The concentrations of ClNO2 are comparable to measurements made in London, but measurements of ClOVOCs, Cl2 and HOCl by this method are the first reported in the UK. Maximum HOCl and Cl2 concentrations are found during the day and ClNO2 concentrations remain elevated into the afternoon if photolysis rates are low. Cl2 exhibits a strong dependency on shortwave radiation, further adding to the growing body of evidence that it is a product of secondary chemistry. However, night-time emission is also observed. The contribution of ClNO2, Cl2 and ClOVOCs to the chlorine radical budget suggests that Cl2 can be a greater source of Cl than ClNO2, contributing 74 % of the Cl radicals produced on a high radiant-flux day. In contrast, on a low radiant-flux day, this drops to 14 %, as both Cl2 production and loss pathways are inhibited by reduced photolysis rates. This results in ClNO2 making up the dominant fraction (83 %) on low radiant-flux days, as its concentrations are still high. As most ClOVOCs appear to be formed photochemically, they exhibit a similar dependence on photolysis, contributing 3 % of the Cl radical budget observed here.

Over the past decade, there has been an increasing interest in short-term events that negatively affect air quality such as bonfires and fireworks. High aerosol and gas concentrations generated from public bonfires or fireworks were measured in order to understand the night-time chemical processes and their atmospheric implications. Nitrogen chemistry was observed during Bonfire Night with nitrogen containing compounds in both gas and aerosol phases and further N2O5 and ClNO2 concentrations, which depleted early next morning due to photolysis of NO3 radicals and ceasing production. Particulate organic oxides of nitrogen (PONs) concentrations of 2.8 µg m−3 were estimated using the m ∕ z 46 : 30 ratios from aerosol mass spectrometer (AMS) measurements, according to previously published methods. Multilinear engine 2 (ME-2) source apportionment was performed to determine organic aerosol (OA) concentrations from different sources after modifying the fragmentation table and it was possible to identify two PON factors representing primary (pPON_ME2) and secondary (sPON_ME2) contributions. A slight improvement in the agreement between the source apportionment of the AMS and a collocated AE-31 Aethalometer was observed after modifying the prescribed fragmentation in the AMS organic spectrum (the fragmentation table) to determine PON sources, which resulted in an r2 = 0.894 between biomass burning organic aerosol (BBOA) and babs_470wb compared to an r2 = 0.861 obtained without the modification. Correlations between OA sources and measurements made using time-of-flight chemical ionisation mass spectrometry with an iodide adduct ion were performed in order to determine possible gas tracers to be used in future ME-2 analyses to constrain solutions. During Bonfire Night, strong correlations (r2) were observed between BBOA and methacrylic acid (0.92), acrylic acid (0.90), nitrous acid (0.86), propionic acid, (0.85) and hydrogen cyanide (0.76). A series of oxygenated species and chlorine compounds showed good correlations with sPON_ME2 and the low volatility oxygenated organic aerosol (LVOOA) factor during Bonfire Night and an event with low pollutant concentrations. Further analysis of pPON_ME2 and sPON_ME2 was performed in order to determine whether these PON sources absorb light near the UV region using an Aethalometer. This hypothesis was tested by doing multilinear regressions between babs_470wb and BBOA, sPON_ME2 and pPON_ME2. Our results suggest that sPON_ME2 does not absorb light at 470 nm, while pPON_ME2 and LVOOA do absorb light at 470 nm. This may inform black carbon (BC) source apportionment studies from Aethalometer measurements, through investigation of the brown carbon contribution to babs_470wb.

The multilinear engine (ME-2) factorization tool is being widely used following the recent development of the Source Finder (SoFi) interface at the Paul Scherrer Institute. However, the success of this tool, when using the a value approach, largely depends on the inputs (i.e. target profiles) applied as well as the experience of the user. A strategy to explore the solution space is proposed, in which the solution that best describes the organic aerosol (OA) sources is determined according to the systematic application of predefined statistical tests. This includes trilinear regression, which proves to be a useful tool for comparing different ME-2 solutions. Aerosol Chemical Speciation Monitor (ACSM) measurements were carried out at the urban background site of North Kensington, London from March to December 2013, where for the first time the behaviour of OA sources and their possible environmental implications were studied using an ACSM. Five OA sources were identified: biomass burning OA (BBOA), hydrocarbon-like OA (HOA), cooking OA (COA), semivolatile oxygenated OA (SVOOA) and low-volatility oxygenated OA (LVOOA). ME-2 analysis of the seasonal data sets (spring, summer and autumn) showed a higher variability in the OA sources that was not detected in the combined March–December data set; this variability was explored with the triangle plots f44 : f43 f44 : f60, in which a high variation of SVOOA relative to LVOOA was observed in the f44 : f43 analysis. Hence, it was possible to conclude that, when performing source apportionment to long-term measurements, important information may be lost and this analysis should be done to short periods of time, such as seasonally. Further analysis on the atmospheric implications of these OA sources was carried out, identifying evidence of the possible contribution of heavy-duty diesel vehicles to air pollution during weekdays compared to those fuelled by petrol.

The climate effects of atmospheric aerosol particles serving as cloud condensation nuclei (CCN) depend on chemical composition and hygroscopicity, which are highly variable on spatial and temporal scales. Here we present global CCN measurements, covering diverse environments from pristine to highly polluted conditions. We show that the effective aerosol hygroscopicity, κ, can be derived accurately from the fine aerosol mass fractions of organic particulate matter (ϵ org ) and inorganic ions (ϵ inorg ) through a linear combination, κ = ϵ org  ⋅ κ org  + ϵ inorg  ⋅ κ inorg . In spite of the chemical complexity of organic matter, its hygroscopicity is well captured and represented by a global average value of κ org  = 0.12 ± 0.02 with κ inorg  = 0.63 ± 0.01 as the corresponding value for inorganic ions. By showing that the sensitivity of global climate forcing to changes in κ org and κ inorg is small, we constrain a critically important aspect of global climate modelling., (© 2023. Springer Nature Limited.)

Rapid economic growth and development have exacerbated air quality problems across India, driven by many poorly understood pollution sources and understanding their relative importance remains critical to characterising the key drivers of air pollution. A comprehensive suite of measurements of 90 non-methane hydrocarbons (NMHCs) (C 2 -C 14 ), including 12 speciated monoterpenes and higher molecular weight monoaromatics, were made at an urban site in Old Delhi during the pre-monsoon (28-May to 05-Jun 2018) and post-monsoon (11 to 27-Oct 2018) seasons using dual-channel gas chromatography (DC-GC-FID) and two-dimensional gas chromatography (GC×GC-FID). Significantly higher mixing ratios of NMHCs were measured during the post-monsoon campaign, with a mean night-time enhancement of around 6. Like with NO x and CO, strong diurnal profiles were observed for all NMHCs, except isoprene, with very high NMHC mixing ratios between 35-1485 ppbv. The sum of mixing ratios of benzene, toluene, ethylbenzene and xylenes (BTEX) routinely exceeded 100 ppbv at night during the post-monsoon period, with a maximum measured mixing ratio of monoaromatic species of 370 ppbv. The mixing ratio of highly reactive monoterpenes peaked at around 6 ppbv in the post-monsoon campaign and correlated strongly with anthropogenic NMHCs, suggesting a strong non-biogenic source in Delhi. A detailed source apportionment study was conducted which included regression analysis to CO, acetylene and other NMHCs, hierarchical cluster analysis, EPA UNMIX 6.0, principal component analysis/absolute principal component scores (PCA/APCS) and comparison with NMHC ratios (benzene/toluene and i-/n-pentane) in ambient samples to liquid and solid fuels. These analyses suggested the primary source of anthropogenic NMHCs in Delhi was from traffic emissions (petrol and diesel), with average mixing ratio contributions from Unmix and PCA/APCS models of 38% from petrol, 14% from diesel and 32% from liquified petroleum gas (LPG) with a smaller contribution (16%) from solid fuel combustion. Detailed consideration of the underlying meteorology during the campaigns showed that the extreme night-time mixing ratios of NMHCs during the post-monsoon campaign were the result of emissions into a very shallow and stagnant boundary layer. The results of this study suggest that despite widespread open burning in India, traffic-related petrol and diesel emissions remain the key drivers of gas-phase urban air pollution in Delhi.

Food-cooking organic aerosols (COA) are one of the primary sources of submicron particulate matter in urban environments. However, there are still many questions surrounding source apportionment related to instrumentation as well as semivolatile partitioning because COA evolve rapidly in the ambient air, making source apportionment more complex. Online measurements of emissions from cooking different types of food were performed in a laboratory to characterize particles and gases. Aerosol mass spectrometer (AMS) measurements showed that the relative ionization efficiency for OA was higher (1.56-3.06) relative to a typical value of 1.4, concluding that AMS is over-estimating COA and suggesting that previous studies likely over-estimated COA concentrations. Food-cooking mass spectra were generated using AMS, and gas and particle food markers were identified with filter inlets for gases and aerosols-chemical ionization mass spectrometer (CIMS) measurements to be used in future food cooking-source apportionment studies. However, there is a considerable variability in both gas and particle markers, and dilution plays an important role in the particle mass budget, showing the importance of using these markers with caution during receptor modeling. These findings can be used to better understand the chemical composition of COA, and they provides useful information to be used in future source-apportionment studies.