Secondary organic aerosol (SOA) is formed from the atmospheric oxidation of gas-phase organic compounds leading to the formation of particle mass. Gasoline- and diesel-powered motor vehicles, both ...on/off-road, are important sources of SOA precursors. They emit complex mixtures of gas-phase organic compounds that vary in volatility and molecular structurefactors that influence their contributions to urban SOA. However, the relative importance of each vehicle type with respect to SOA formation remains unclear due to conflicting evidence from recent laboratory, field, and modeling studies. Both are likely important, with evolving contributions that vary with location and over short time scales. This review summarizes evidence, research needs, and discrepancies between top-down and bottom-up approaches used to estimate SOA from motor vehicles, focusing on inconsistencies between molecular-level understanding and regional observations. The effect of emission controls (e.g., exhaust aftertreatment technologies, fuel formulation) on SOA precursor emissions needs comprehensive evaluation, especially with international perspective given heterogeneity in regulations and technology penetration. Novel studies are needed to identify and quantify “missing” emissions that appear to contribute substantially to SOA production, especially in gasoline vehicles with the most advanced aftertreatment. Initial evidence suggests catalyzed diesel particulate filters greatly reduce emissions of SOA precursors along with primary aerosol.
Particulate matter is a component of ambient air pollution that has been linked to millions of annual premature deaths globally
. Assessments of the chronic and acute effects of particulate matter on ...human health tend to be based on mass concentration, with particle size and composition also thought to play a part
. Oxidative potential has been suggested to be one of the many possible drivers of the acute health effects of particulate matter, but the link remains uncertain
. Studies investigating the particulate-matter components that manifest an oxidative activity have yielded conflicting results
. In consequence, there is still much to be learned about the sources of particulate matter that may control the oxidative potential concentration
. Here we use field observations and air-quality modelling to quantify the major primary and secondary sources of particulate matter and of oxidative potential in Europe. We find that secondary inorganic components, crustal material and secondary biogenic organic aerosols control the mass concentration of particulate matter. By contrast, oxidative potential concentration is associated mostly with anthropogenic sources, in particular with fine-mode secondary organic aerosols largely from residential biomass burning and coarse-mode metals from vehicular non-exhaust emissions. Our results suggest that mitigation strategies aimed at reducing the mass concentrations of particulate matter alone may not reduce the oxidative potential concentration. If the oxidative potential can be linked to major health impacts, it may be more effective to control specific sources of particulate matter rather than overall particulate mass.
Cooking processes produce gaseous and particle emissions that are potentially deleterious to human health. Using a highly controlled experimental setup involving a proton-transfer-reaction ...time-of-flight mass spectrometer (PTR-ToF-MS), we investigate the emission factors and the detailed chemical composition of gas phase emissions from a broad variety of cooking styles and techniques. A total of 95 experiments were conducted to characterize nonmethane organic gas (NMOG) emissions from boiling, charbroiling, shallow frying, and deep frying of various vegetables and meats, as well as emissions from vegetable oils heated to different temperatures. Emissions from boiling vegetables are dominated by methanol. Significant amounts of dimethyl sulfide are emitted from cruciferous vegetables. Emissions from shallow frying, deep frying and charbroiling are dominated by aldehydes of differing relative composition depending on the oil used. We show that the emission factors of some aldehydes are particularly large which may result in considerable negative impacts on human health in indoor environments. The suitability of some of the aldehydes as tracers for the identification of cooking emissions in ambient air is discussed.
A source apportionment study was performed for particulate matter in the small village of Roveredo, Switzerland, where more than 70% of the households use wood burning for heating purposes. A ...two-lane trans-Alpine highway passes through the village and contributes to the total aerosol burden in the area. The village is located in a steep Alpine valley characterized by strong and persistent temperature inversions during winter, especially from December to February. During two winter and one early spring campaigns, a seven-wavelength aethalometer, high volume (HIVOL) samplers, an Aerodyne quadrupole aerosol mass spectrometer (AMS), an optical particle counter (OPC), and a Sunset Laboratory OCEC analyzer were deployed to study the contribution of wood burning and traffic aerosols to particulate matter. A linear regression model of the carbonaceous particulate mass in the submicrometer size range CM(PM1) as a function of aerosol light absorption properties measured by the aethalometer is introduced to estimate the particulate mass from wood burning and traffic (PMwb, PMtraffic). This model was calibrated with analyses from the 14C method using HIVOL filter measurements. These results indicate that light absorption exponents of 1.1 for traffic and 1.8–1.9 for wood burning calculated from the light absorption at 470 and 950 nanometers should be used to obtain agreement of the two methods regarding the relative wood burning and traffic emission contributions to CM(PM1) and also to black carbon. The resulting PMwb and PMtraffic values explain 86% of the variance of the CM(PM1) and contribute, on average, 88 and 12% to CM(PM1), respectively. The black carbon is estimated to be 51% due to wood burning and 49% due to traffic emissions. The average organic carbon/total carbon (OC/TC) values were estimated to be 0.52 for traffic and 0.88 for wood burning particulate emissions.
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 aerosol (OA) particles are recognized as key factors influencing air
quality and climate change. However, highly time-resolved long-term
characterizations of their composition and sources in ...ambient air are still
very limited due to challenging continuous observations. Here, we present an
analysis of long-term variability of submicron OA using the combination of
an aerosol chemical speciation monitor (ACSM) and a multiwavelength Aethalometer
from November 2011 to March 2018 at a peri-urban background site of the
Paris region (France). Source apportionment of OA was achieved via partially
constrained positive matrix factorization (PMF) using the multilinear engine
(ME-2). Two primary OA (POA) and two oxygenated OA (OOA) factors were
identified and quantified over the entire studied period. POA factors were
designated as hydrocarbon-like OA (HOA) and biomass burning OA (BBOA). The
latter factor presented a significant seasonality with higher concentrations
in winter with significant monthly contributions to OA (18 %–33 %) due to
enhanced residential wood burning emissions. HOA mainly originated from
traffic emissions but was also influenced by biomass burning in cold
periods. OOA factors were distinguished between their less- and
more-oxidized fractions (LO-OOA and MO-OOA, respectively). These factors
presented distinct seasonal patterns, associated with different atmospheric
formation pathways. A pronounced increase in LO-OOA concentrations and
contributions (50 %–66 %) was observed in summer, which may be mainly
explained by secondary OA (SOA) formation processes involving biogenic
gaseous precursors. Conversely, high concentrations and OA contributions
(32 %–62 %) of MO-OOA during winter and spring seasons were partly
associated with anthropogenic emissions and/or long-range transport from
northeastern Europe. The contribution of the different OA factors as a
function of OA mass loading highlighted the dominant roles of POA during
pollution episodes in fall and winter and of SOA for highest springtime and
summertime OA concentrations. Finally, long-term trend analyses indicated a
decreasing feature (of about −175 ng m−3 yr−1) for MO-OOA, very
limited or insignificant decreasing trends for primary anthropogenic
carbonaceous aerosols (BBOA and HOA, along with the fossil-fuel and biomass-burning black carbon components) and no statistically significant trend for
LO-OOA over the 6-year investigated period.
Organic aerosol (OA) represents a large fraction of submicron aerosols in the
megacity of Beijing, yet long-term characterization of its sources and
variations is very limited. Here we present an ...analysis of in situ
measurements of OA in submicrometer particles with an aerosol chemical
speciation monitor (ACSM) for 2 years from July 2011 to May 2013. The sources
of OA are analyzed with a multilinear engine (ME-2) by constraining three
primary OA factors including fossil-fuel-related OA (FFOA), cooking OA (COA),
and biomass burning OA (BBOA). Two secondary OAs (SOA), representing a less
oxidized oxygenated OA (LO-OOA) and a more oxidized (MO-OOA), are identified
during all seasons. The monthly average concentration OA varied from 13.6 to
46.7 µg m−3 with a strong seasonal pattern that is usually
highest in winter and lowest in summer. FFOA and BBOA show similarly
pronounced seasonal variations with much higher concentrations and
contributions in winter due to enhanced coal combustion and biomass burning
emissions. The contribution of COA to OA, however, is relatively stable
(10–15 %) across different seasons, yet presents significantly higher
values at low relative humidity levels (RH < 30 %),
highlighting the important role of COA during clean periods. The two SOA
factors present very different seasonal variations. The pronounced
enhancement of LO-OOA concentrations in winter indicates that emissions from
combustion-related primary emissions could be a considerable source of SOA
under low-temperature (T) conditions. Comparatively, MO-OOA shows high
concentrations consistently at high RH levels across different T levels,
and the contribution of MO-OOA to OA is different seasonally with lower
values occurring more in winter (30–34 %) than other seasons
(47–64 %). Overall, SOA (= LO-OOA + MO-OOA) dominates OA
composition during all seasons by contributing 52–64 % of the total OA
mass in the heating season and 65–75 % in non-heating seasons. The
variations in OA composition as a function of OA mass loading further
illustrate the dominant role of SOA in OA across different mass loading
scenarios during all seasons. However, we also observed a large increase in
FFOA associated with a corresponding decrease in MO-OOA during periods with
high OA mass loadings in the heating season, illustrating an enhanced role of
coal combustion emissions during highly polluted episodes. Potential source
contribution function analysis further shows that the transport from the
regions located to the south and southwest of Beijing within ∼ 250 km
can contribute substantially to high FFOA and BBOA concentrations in the
heating season.
Equivalent black carbon (EBC) measured by a multi-wavelength Aethalometer can be apportioned to traffic and wood burning. The method is based on the differences in the dependence of aerosol ...absorption on the wavelength of light used to investigate the sample, parameterized by the source-specific absorption Ångström exponent (α). While the spectral dependence (defined as α values) of the traffic-related EBC light absorption is low, wood smoke particles feature enhanced light absorption in the blue and near ultraviolet. Source apportionment results using this methodology are hence strongly dependent on the α values assumed for both types of emissions: traffic αTR, and wood burning αWB. Most studies use a single αTR and αWB pair in the Aethalometer model, derived from previous work. However, an accurate determination of the source specific α values is currently lacking and in some recent publications the applicability of the Aethalometer model was questioned.Here we present an indirect methodology for the determination of αWB and αTR by comparing the source apportionment of EBC using the Aethalometer model with 14C measurements of the EC fraction on 16 to 40 h filter samples from several locations and campaigns across Switzerland during 2005–2012, mainly in winter. The data obtained at eight stations with different source characteristics also enabled the evaluation of the performance and the uncertainties of the Aethalometer model in different environments. The best combination of αTR and αWB (0.9 and 1.68, respectively) was obtained by fitting the Aethalometer model outputs (calculated with the absorption coefficients at 470 and 950 nm) against the fossil fraction of EC (ECF ∕ EC) derived from 14C measurements. Aethalometer and 14C source apportionment results are well correlated (r = 0.81) and the fitting residuals exhibit only a minor positive bias of 1.6 % and an average precision of 9.3 %. This indicates that the Aethalometer model reproduces reasonably well the 14C results for all stations investigated in this study using our best estimate of a single αWB and αTR pair. Combining the EC, 14C, and Aethalometer measurements further allowed assessing the dependence of the mass absorption cross section (MAC) of EBC on its source. Results indicate no significant difference in MAC at 880 nm between EBC originating from traffic or wood-burning emissions. Using ECF ∕ EC as reference and constant a priori selected αTR values, αWB was also calculated for each individual data point. No clear station-to-station or season-to-season differences in αWB were observed, but αTR and αWB values are interdependent. For example, an increase in αTR by 0.1 results in a decrease in αWB by 0.1. The fitting residuals of different αTR and αWB combinations depend on ECF ∕ EC such that a good agreement cannot be obtained over the entire ECF ∕ EC range using other α pairs. Additional combinations of αTR = 0.8, and 1.0 and αWB = 1.8 and 1.6, respectively, are possible but only for ECF ∕ EC between ∼ 40 and 85 %. Applying α values previously used in the literature such as αWB of ∼ 2 or any αWB in combination with αTR = 1.1 to our data set results in large residuals. Therefore we recommend to use the best α combination as obtained here (αTR = 0.9 and αWB = 1.68) in future studies when no or only limited additional information like 14C measurements are available. However, these results were obtained for locations impacted by black carbon (BC) mainly from traffic consisting of a modern car fleet and residential wood combustion with well-constrained combustion efficiencies. For regions of the world with different combustion conditions, additional BC sources, or fuels used, further investigations are needed.
The North China Plain (NCP) frequently experiences heavy haze pollution, particularly during wintertime. In winter 2015–2016, the NCP region suffered several extremely severe haze episodes with air ...pollution red alerts issued in many cities. We have investigated the sources and aerosol evolution processes of the severe pollution episodes in Handan, a typical industrialized city in the NCP region, using real-time measurements from an intensive field campaign during the winter of 2015–2016. The average (±1σ) concentration of submicron aerosol (PM1) during 3 December 2015–5 February 2016 was 187.6 (±137.5) µg m−3, with the hourly maximum reaching 700.8 µg m−3. Organic was the most abundant component, on average accounting for 45 % of total PM1 mass, followed by sulfate (15 %), nitrate (14 %), ammonium (12 %), chloride (9 %) and black carbon (BC, 5 %). Positive matrix factorization (PMF) with the multilinear engine (ME-2) algorithm identified four major organic aerosol (OA) sources, including traffic emissions represented by a hydrocarbon-like OA (HOA, 7 % of total OA), industrial and residential burning of coal represented by a coal combustion OA (CCOA, 29 % of total OA), open and domestic combustion of wood and crop residuals represented by a biomass burning OA (BBOA, 25 % of total OA), and formation of secondary OA (SOA) in the atmosphere represented by an oxygenated OA (OOA, 39 % of total OA). Emissions of primary OA (POA), which together accounted for 61 % of total OA and 27 % of PM1, are a major cause of air pollution during the winter. Our analysis further uncovered that primary emissions from coal combustion and biomass burning together with secondary formation of sulfate (mainly from SO2 emitted by coal combustion) are important driving factors for haze evolution. However, the bulk composition of PM1 showed comparatively small variations between less polluted periods (daily PM2. 5 ≤ 75 µg m−3) and severely polluted periods (daily PM2. 5 > 75 µg m−3), indicating relatively synchronous increases of all aerosol species during haze formation. The case study of a severe haze episode, which lasted 8 days starting with a steady buildup of aerosol pollution followed by a persistently high level of PM1 (326.7–700.8 µg m−3), revealed the significant influence of stagnant meteorological conditions which acerbate air pollution in the Handan region. The haze episode ended with a shift of wind which brought in cleaner air masses from the northwest of Handan and gradually reduced PM1 concentration to < 50 µg m−3 after 12 h. Aqueous-phase reactions under higher relative humidity (RH) were found to significantly promote the production of secondary inorganic species (especially sulfate) but showed little influence on SOA.
We show for the first time quantitative online measurements of five nitrated phenol (NP) compounds in ambient air (nitrophenol C6H5NO3, methylnitrophenol C7H7NO3, nitrocatechol C6H5NO4, ...methylnitrocatechol C7H7NO4, and dinitrophenol C6H4N2O5) measured with a micro-orifice volatilization impactor (MOVI) high-resolution chemical ionization mass spectrometer in Detling, United Kingdom during January–February, 2012. NPs absorb radiation in the near-ultraviolet (UV) range of the electromagnetic spectrum and thus are potential components of poorly characterized light-absorbing organic matter (“brown carbon”) which can affect the climate and air quality. Total NP concentrations varied between less than 1 and 98 ng m–3, with a mean value of 20 ng m–3. We conclude that NPs measured in Detling have a significant contribution from biomass burning with an estimated emission factor of 0.2 ng (ppb CO)−1. Particle light absorption measurements by a seven-wavelength aethalometer in the near-UV (370 nm) and literature values of molecular absorption cross sections are used to estimate the contribution of NP to wood burning brown carbon UV light absorption. We show that these five NPs are potentially important contributors to absorption at 370 nm measured by an aethalometer and account for 4 ± 2% of UV light absorption by brown carbon. They can thus affect atmospheric radiative transfer and photochemistry and with that climate and air quality.
PDF
