Rapid industrialization and urbanization in developing countries has led to an increase in air pollution, along a similar trajectory to that previously experienced by the developed nations. In China, ...particulate pollution is a serious environmental problem that is influencing air quality, regional and global climates, and human health. In response to the extremely severe and persistent haze pollution experienced by about 800 million people during the first quarter of 2013 (refs 4, 5), the Chinese State Council announced its aim to reduce concentrations of PM2.5 (particulate matter with an aerodynamic diameter less than 2.5 micrometres) by up to 25 per cent relative to 2012 levels by 2017 (ref. 6). Such efforts however require elucidation of the factors governing the abundance and composition of PM2.5, which remain poorly constrained in China. Here we combine a comprehensive set of novel and state-of-the-art offline analytical approaches and statistical techniques to investigate the chemical nature and sources of particulate matter at urban locations in Beijing, Shanghai, Guangzhou and Xi'an during January 2013. We find that the severe haze pollution event was driven to a large extent by secondary aerosol formation, which contributed 30-77 per cent and 44-71 per cent (average for all four cities) of PM2.5 and of organic aerosol, respectively. On average, the contribution of secondary organic aerosol (SOA) and secondary inorganic aerosol (SIA) are found to be of similar importance (SOA/SIA ratios range from 0.6 to 1.4). Our results suggest that, in addition to mitigating primary particulate emissions, reducing the emissions of secondary aerosol precursors from, for example, fossil fuel combustion and biomass burning is likely to be important for controlling China's PM2.5 levels and for reducing the environmental, economic and health impacts resulting from particulate pollution.
Delhi, the capital of India, suffers from heavy local emissions as well as regional transport of air pollutants, resulting in severe aerosol loadings. To determine the sources of these pollutants, we ...have quantified the mass concentrations of 26 elements in airborne particles, measured by an online X-ray fluorescence spectrometer with time resolution between 30 min and 1 h. Measurements of PM10 and PM2.5 (particulate matter <10 μm and < 2.5 μm) were conducted during two consecutive winters (2018 and 2019) in Delhi. On average, 26 elements from Al to Pb made up ~25% and ~19% of the total PM10 mass (271 μg m−3 and 300 μg m−3) in 2018 and 2019, respectively. Nine different aerosol sources were identified during both winters using positive matrix factorization (PMF), including dust, non-exhaust, an S-rich factor, two solid fuel combustion (SFC) factors and four industrial/combustion factors related to plume events (Cr-Ni-Mn, Cu-Cd-Pb, Pb-Sn-Se and Cl-Br-Se). All factors were resolved in both size ranges (but varying relative concentrations), comprising the following contributions to the elemental PM10 mass (in % average for 2018 and 2019): Cl-Br-Se (41.5%, 36.9%), dust (27.6%, 28.7%), non-exhaust (16.2%, 13.7%), S-rich (6.9%, 9.2%), SFC1 + SFC2 (4%, 7%), Pb-Sn-Se (2.3%, 1.66%), Cu-Cd-Pb (0.67%, 2.2%) and Cr-Ni-Mn (0.57%, 0.47%). Most of these sources had the highest relative contributions during late night (22:00 local time (LT)) and early morning hours (between 03:00 to 08:00 LT), which is consistent with enhanced emissions into a shallow boundary layer. Modelling of airmass source geography revealed that the Pb-Sn-Se, Cl-Br-Se and SFC2 factors prevailed for northwest winds (Pakistan, Punjab, Haryana and Delhi), while the Cu-Cd-Pb and S-rich factors originated from east (Nepal and Uttar Pradesh) and the Cr-Ni-Mn factor from northeast (Uttar Pradesh). In contrast, SFC1, dust and non-exhaust were not associated with any specific wind direction.
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•Quantified highly time-resolved elements in PM10 and PM2.5 in Delhi during winters•Source apportionment was improved combining receptor model and trajectory analysis.•Major PM10 elemental constituents were Cl, S and crustal elements (Si, Ca, Ti, Fe).•Northwest and east were the most influential source regions for various sources.
A thermal desorption aerosol gas chromatograph coupled to a high resolution – time of flight – aerosol mass spectrometer (TAG-AMS) was connected to an atmospheric chamber for the molecular ...characterization of the evolution of organic aerosol (OA) emitted by woodstove appliances for residential heating. Two log woodstoves (old and modern) and one pellet stove were operated under typical conditions. Emissions were aged during a time equivalent to 5 h of atmospheric aging. The five to seven samples were collected and analyzed with the TAG-AMS during each experiment. We detected and quantified over 70 compounds, including levoglucosan and nitrocatechols. We calculate the emission factor (EF) of these tracers in the primary emissions and highlight the influence of the combustion efficiency on these emissions. Smoldering combustion contributes to a higher EF and a more complex composition. We also demonstrate the effect of atmospheric aging on the chemical fingerprint. The tracers are sorted into three categories according to the evolution of their concentration: primary compounds, non-conventional primary compounds, and secondary compounds. For each, we provide a quantitative overview of their contribution to the OA mass at different times of the photo-oxidative process.
During winter 2013-2014 aerosol mass spectrometer (AMS) measurements were conducted for the first time with a novel PM2.5 (particulate matter with aerodynamic diameter ≤ 2.5 µm) lens in two major ...cities of China: Xi'an and Beijing. We denote the periods with visibility below 2 km as extreme haze and refer to the rest as reference periods. During the measurements in Xi'an an extreme haze covered the city for about a week and the total non-refractory (NR)-PM2.5 mass fraction reached peak concentrations of over 1000 µg m-3. During the measurements in Beijing two extreme haze events occurred, but the temporal extent and the total concentrations reached during these events were lower than in Xi'an. Average PM2.5 concentrations of 537 ± 146 and 243 ± 47 µg m-3 (including NR species and equivalent black carbon, eBC) were recorded during the extreme haze events in Xi'an and Beijing, respectively. During the reference periods the measured average concentrations were 140 ± 99 µg m-3 in Xi'an and 75 ± 61 µg m-3 in Beijing. The relative composition of the NR-PM2.5 evolved substantially during the extreme haze periods, with increased contributions of the inorganic components (mostly sulfate and nitrate). Our results suggest that the high relative humidity present during the extreme haze events had a strong effect on the increase of sulfate mass (via aqueous phase oxidation of sulfur dioxide). Another relevant characteristic of the extreme haze is the size of the measured particles. During the extreme haze events, the AMS showed much larger particles, with a volume weighted mode at about 800 to 1000 nm, in contrast to about 400 nm during reference periods. These large particle sizes made the use of the PM2.5 inlet crucial, especially during the severe haze events, where 39 ± 5 % of the mass would have been lost in the conventional PM1 (particulate matter with aerodynamic diameter ≤ 1 µm) inlet. A novel positive matrix factorization procedure was developed to apportion the sources of organic aerosols (OA) based on their mass spectra using the multilinear engine (ME-2) controlled via the source finder (SoFi). The procedure allows for an effective exploration of the solution space, a more objective selection of the best solution and an estimation of the rotational uncertainties. Our results clearly show an increase of the oxygenated organic aerosol (OOA) mass during extreme haze events. The contribution of OOA to the total OA increased from the reference to the extreme haze periods from 16.2 ± 1.1 to 31.3 ± 1.5 % in Xi'an and from 15.7 ± 0.7 to 25.0 ± 1.2 % in Beijing. By contrast, during the reference periods the total OA mass was dominated by domestic emissions of primary aerosols from biomass burning in Xi'an (42.2 ± 1.5 % of OA) and coal combustion in Beijing (55.2 ± 1.6 % of OA). These two sources are also mostly responsible for extremely high polycyclic aromatic hydrocarbon (PAH) concentrations measured with the AMS (campaign average of 2.1 ± 2.0-µg-m-3 and frequent peak concentrations above 10 µg m-3). To the best of our knowledge, this is the first data set where the simultaneous extraction of these two primary sources could be achieved in China by conducting on-line AMS measurements at two areas with contrasted emission patterns.
The measurement of elements in PM10 was performed with 1 h time resolution at a rural freeway site during summer 2015 in Switzerland using the Xact1 625 Ambient Metals Monitor. On average the Xact ...elements (without accounting for oxygen and other associated elements) make up about 20 % of the total PM10 mass (14.6 µg m−3). We conducted source apportionment by positive matrix factorisation (PMF) of the
elemental mass measurable by the Xact (i.e. major elements heavier than
Al), defined here as PM10el. Eight different sources were identified in PM10el (elemental PM10) mass driven by the sum of 14 elements (notable elements in brackets): Fireworks-I (K, S, Ba and Cl), Fireworks-II (K), sea salt (Cl), secondary sulfate (S), background dust (Si, Ti), road dust (Ca), non-exhaust traffic-related elements (Fe) and industrial elements (Zn and Pb). The major components were secondary sulfate and non-exhaust traffic-related elements followed by background dust and road dust factors, explaining 21 %, 20 %, 18 % and 16 % of the analysed PM10 elemental mass, respectively, with the factor mass not corrected for oxygen content. Further, there were minor contributions (on the order of a few percent) of sea salt and industrial sources. The regionally influenced secondary sulfate factor showed negligible resuspension, and concentrations were similar throughout the day. The significant loads of the non-exhaust traffic-related and road dust factors with strong diurnal variations highlight the continuing importance of vehicle-related air pollutants at this site. Enhanced control of PMF implemented via the SourceFinder software (SoFi Pro version 6.2, PSI, Switzerland) allowed for a successful apportionment of transient sources such as the two firework factors and sea salt, which remained mixed when analysed by unconstrained PMF.
Organic gases emitted during the flaming phase of residential wood combustion are characterized individually and by functionality using proton transfer reaction time-of-flight mass spectrometry. The ...evolution of the organic gases is monitored during photochemical aging. Primary gaseous emissions are dominated by oxygenated species (e.g., acetic acid, acetaldehyde, phenol and methanol), many of which have deleterious health effects and play an important role in atmospheric processes such as secondary organic aerosol formation and ozone production. Residential wood combustion emissions differ considerably from open biomass burning in both absolute magnitude and relative composition. Ratios of acetonitrile, a potential biomass burning marker, to CO are considerably lower ( ∼ 0.09 pptv ppbv−1) than those observed in air masses influenced by open burning ( ∼ 1–2 pptv ppbv−1), which may make differentiation from background levels difficult, even in regions heavily impacted by residential wood burning. A considerable amount of formic acid forms during aging ( ∼ 200–600 mg kg−1 at an OH exposure of (4.5–5.5) × 107 molec cm−3 h), indicating residential wood combustion can be an important local source for this acid, the quantities of which are currently underestimated in models. Phthalic anhydride, a naphthalene oxidation product, is also formed in considerable quantities with aging ( ∼ 55–75 mg kg−1 at an OH exposure of (4.5–5.5) × 107 molec cm−3 h). Although total NMOG emissions vary by up to a factor of ∼ 9 between burns, SOA formation potential does not scale with total NMOG emissions and is similar in all experiments. This study is the first thorough characterization of both primary and aged organic gases from residential wood combustion and provides a benchmark for comparison of emissions generated under different burn parameters.
Long-term monitoring of organic aerosol is important for epidemiological studies, validation of atmospheric models, and air quality management. In this study, we apply a recently developed ...filter-based offline methodology using an aerosol mass spectrometer (AMS) to investigate the regional and seasonal differences of contributing organic aerosol sources. We present offline AMS measurements for particulate matter smaller than 10 µm at nine stations in central Europe with different exposure characteristics for the entire year of 2013 (819 samples). The focus of this study is a detailed source apportionment analysis (using positive matrix factorization, PMF) including in-depth assessment of the related uncertainties. Primary organic aerosol (POA) is separated in three components: hydrocarbon-like OA related to traffic emissions (HOA), cooking OA (COA), and biomass burning OA (BBOA). We observe enhanced production of secondary organic aerosol (SOA) in summer, following the increase in biogenic emissions with temperature (summer oxygenated OA, SOOA). In addition, a SOA component was extracted that correlated with an anthropogenic secondary inorganic species that is dominant in winter (winter oxygenated OA, WOOA). A factor (sulfur-containing organic, SC-OA) explaining sulfur-containing fragments (CH3SO2+), which has an event-driven temporal behaviour, was also identified. The relative yearly average factor contributions range from 4 to 14 % for HOA, from 3 to 11 % for COA, from 11 to 59 % for BBOA, from 5 to 23 % for SC-OA, from 14 to 27 % for WOOA, and from 15 to 38 % for SOOA. The uncertainty of the relative average factor contribution lies between 2 and 12 % of OA. At the sites north of the alpine crest, the sum of HOA, COA, and BBOA (POA) contributes less to OA (POA / OA = 0.3) than at the southern alpine valley sites (0.6). BBOA is the main contributor to POA with 87 % in alpine valleys and 42 % north of the alpine crest. Furthermore, the influence of primary biological particles (PBOAs), not resolved by PMF, is estimated and could contribute significantly to OA in PM10.
Delhi is one of the most polluted cities worldwide and a comprehensive understanding and deeper insight into the air pollution and its sources is of high importance. We report 5 months of highly ...time-resolved measurements of non-refractory PM2.5 and black carbon (BC). Additionally, source apportionment based on positive matrix factorization (PMF) of the organic aerosol (OA) fraction is presented. The highest pollution levels are observed during winter in December/January. During that time, also uniquely high chloride concentrations are measured, which are sometimes even the most dominant NR-species in the morning hours. With increasing temperature, the total PM2.5 concentration decreases steadily, whereas the chloride concentrations decrease sharply. The concentrations measured in May are roughly 6 times lower than in December/January. PMF analysis resolves two primary factors, namely hydrocarbon-like (traffic-related) OA (HOA) and solid fuel combustion OA (SFC-OA), and one or two secondary factors depending on the season. The uncertainties of the PMF analysis are assessed by combining the random a-value approach and the bootstrap resampling technique of the PMF input. The uncertainties for the resolved factors range from ±18% to ±19% for HOA, ±7% to ±19% for SFC-OA and ±6 % to ±11% for the OOAs. The average correlation of HOA with equivalent black carbon from traffic (eBCtr) is R2 = 0.40, while SFC-OA has a correlation of R2 = 0.78 with equivalent black carbon from solid fuel combustion (eBCsf). Anthracene (m/z 178) and pyrene (m/z 202) (PAHs) are mostly explained by SFC-OA and follow its diurnal trend (R2 = 0.98 and R2 = 0.97). The secondary oxygenated aerosols are dominant during daytime. The average contribution during the afternoon hours (1 pm–5 pm) is 59% to the total OA mass, with contributions up to 96% in May. In contrast, the primary sources are more important during nighttime: the mean nightly contribution (22 pm–3 am) to the total OA mass is 48%, with contributions up to 88% during some episodes in April.
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•Over 65% of the daily PM2.5 concentrations exceed the national air quality standard.•Exceptionally high chloride contribution during early mornings in the cold period•Significant solid fuel combustion contribution throughout the full campaign•PMF uncertainty assessment
Residential wood combustion remains one of the most important sources of
primary organic aerosols (POA) and secondary organic aerosol (SOA)
precursors during winter. The overwhelming majority of ...these precursors have not been traditionally considered in regional models, and only recently were lignin pyrolysis products and polycyclic aromatics identified as the
principal SOA precursors from flaming wood combustion. The SOA yields of
these components in the complex matrix of biomass smoke remain unknown and
may not be inferred from smog chamber data based on single-compound systems.
Here, we studied the ageing of emissions from flaming and
smoldering-dominated wood fires in three different residential stoves,
across a wide range of ageing temperatures (−10, 2
and 15 ∘C) and emission loads. Organic gases (OGs) acting as SOA
precursors were monitored by a proton-transfer-reaction time-of-flight mass
spectrometer (PTR-ToF-MS), while the evolution of the aerosol properties
during ageing in the smog chamber was monitored by a high-resolution
time-of-flight aerosol mass spectrometer (HR-ToF-AMS). We developed a novel
box model based on the volatility basis set (VBS) to determine the
volatility distributions of the oxidation products from different precursor
classes found in the emissions, grouped according to their emission pathways
and SOA production rates. We show for the first time that SOA yields in
complex emissions are consistent with those reported in literature from
single-compound systems. We identify the main SOA precursors in both flaming
and smoldering wood combustion emissions at different temperatures. While
single-ring and polycyclic aromatics are significant precursors in flaming
emissions, furans generated from cellulose pyrolysis appear to be important
for SOA production in the case of smoldering fires. This is especially the
case at high loads and low temperatures, given the higher volatility of
furan oxidation products predicted by the model. We show that the oxidation
products of oxygenated aromatics from lignin pyrolysis are expected to
dominate SOA formation, independent of the combustion or ageing conditions,
and therefore can be used as promising markers to trace ageing of biomass
smoke in the field. The model framework developed herein may be
generalizable for other complex emission sources, allowing determination of
the contributions of different precursor classes to SOA, at a level of
complexity suitable for implementation in regional air quality models.
Current mass spectrometry techniques for the online measurement of organic aerosol (OA) composition are subjected to either thermal/ionization-induced artifacts or limited mass resolving power, ...hindering accurate molecular characterization. Here, we combined the soft ionization capability of extractive electrospray ionization (EESI) and the ultrahigh mass resolution of Orbitrap for real-time, near-molecular characterization of OAs. Detection limits as low as tens of ng m–3 with linearity up to hundreds of μg m–3 at 0.2 Hz time resolution were observed for single- and mixed-component calibrations. The performance of the EESI-Orbitrap system was further evaluated with laboratory-generated secondary OAs (SOAs) and filter extracts of ambient particulate matter. The high mass accuracy and resolution (140 000 at m/z 200) of the EESI-Orbitrap system enable unambiguous identification of the aerosol components’ molecular composition and allow a clear separation between adjacent peaks, which would be significantly overlapping if a medium-resolution (20 000) mass analyzer was used. Furthermore, the tandem mass spectrometry (MS2) capability provides valuable insights into the compound structure. For instance, the MS2 analysis of ambient OA samples and lab-generated biogenic SOAs points to specific SOA precursors in ambient air among a range of possible isomers based on fingerprint fragment ions. Overall, this newly developed and characterized EESI-Orbitrap system will advance our understanding of the formation and evolution of atmospheric aerosols.
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