ALBERT

Description: Methane emissions from boreal and arctic wetlands, lakes, and rivers are expected to increase in response to warming and associated permafrost thaw. However, the lack of appropriate land cover datasets for scaling field-measured methane emissions to circumpolar scales has contributed to a large uncertainty for our understanding of present-day and future methane emissions. Here we present the Boreal–Arctic Wetland and Lake Dataset (BAWLD), a land cover dataset based on an expert assessment, extrapolated using random forest modelling from available spatial datasets of climate, topography, soils, permafrost conditions, vegetation, wetlands, and surface water extents and dynamics. In BAWLD, we estimate the fractional coverage of five wetland, seven lake, and three river classes within 0.5 × 0.5∘ grid cells that cover the northern boreal and tundra biomes (17 % of the global land surface). Land cover classes were defined using criteria that ensured distinct methane emissions among classes, as indicated by a co-developed comprehensive dataset of methane flux observations. In BAWLD, wetlands occupied 3.2 × 106 km2 (14 % of domain) with a 95 % confidence interval between 2.8 and 3.8 × 106 km2. Bog, fen, and permafrost bog were the most abundant wetland classes, covering ∼ 28 % each of the total wetland area, while the highest-methane-emitting marsh and tundra wetland classes occupied 5 % and 12 %, respectively. Lakes, defined to include all lentic open-water ecosystems regardless of size, covered 1.4 × 106 km2 (6 % of domain). Low-methane-emitting large lakes (〉10 km2) and glacial lakes jointly represented 78 % of the total lake area, while high-emitting peatland and yedoma lakes covered 18 % and 4 %, respectively. Small (〈0.1 km2) glacial, peatland, and yedoma lakes combined covered 17 % of the total lake area but contributed disproportionally to the overall spatial uncertainty in lake area with a 95 % confidence interval between 0.15 and 0.38 × 106 km2. Rivers and streams were estimated to cover 0.12  × 106 km2 (0.5 % of domain), of which 8 % was associated with high-methane-emitting headwaters that drain organic-rich landscapes. Distinct combinations of spatially co-occurring wetland and lake classes were identified across the BAWLD domain, allowing for the mapping of “wetscapes” that have characteristic methane emission magnitudes and sensitivities to climate change at regional scales. With BAWLD, we provide a dataset which avoids double-accounting of wetland, lake, and river extents and which includes confidence intervals for each land cover class. As such, BAWLD will be suitable for many hydrological and biogeochemical modelling and upscaling efforts for the northern boreal and arctic region, in particular those aimed at improving assessments of current and future methane emissions. Data are freely available at https://doi.org/10.18739/A2C824F9X (Olefeldt et al., 2021).

Description: The measurements of aerosol particles with a filter inlet for gases and aerosols (FIGAERO) together with a chemical ionisation mass spectrometer (CIMS) yield the overall chemical composition of the particle phase. In addition, the thermal desorption profiles obtained for each detected ion composition contain information about the volatility of the detected compounds, which is an important property for understanding many physical properties like gas–particle partitioning. We coupled this thermal desorption method with isothermal evaporation prior to the sample collection to investigate the chemical composition changes during isothermal particle evaporation and particulate-water-driven chemical reactions in α-pinene secondary organic aerosol (SOA) of three different oxidative states. The thermal desorption profiles of all detected elemental compositions were then analysed with positive matrix factorisation (PMF) to identify the drivers of the chemical composition changes observed during isothermal evaporation. The keys to this analysis were to use the error matrix as a tool to weight the parts of the data carrying most information (i.e. the peak area of each thermogram) and to run PMF on a combined data set of multiple thermograms from different experiments to enable a direct comparison of the individual factors between separate measurements. The PMF was able to identify instrument background factors and separate them from the part of the data containing particle desorption information. Additionally, PMF allowed us to separate the direct desorption of compounds detected at a specific elemental composition from other signals with the same composition that stem from the thermal decomposition of thermally instable compounds with lower volatility. For each SOA type, 7–9 factors were needed to explain the observed thermogram behaviour. The contribution of the factors depended on the prior isothermal evaporation. Decreased contributions from the factors with the lowest desorption temperatures were observed with increasing isothermal evaporation time. Thus, the factors identified by PMF could be interpreted as volatility classes. The composition changes in the particles due to isothermal evaporation could be attributed to the removal of volatile factors with very little change in the desorption profiles of the individual factors (i.e. in the respective temperatures of peak desorption, Tmax). When aqueous-phase reactions took place, PMF was able to identify a new factor that directly identified the ions affected by the chemical processes. We conducted a PMF analysis of the FIGAERO–CIMS thermal desorption data for the first time using laboratory-generated SOA particles. But this method can be applied to, for example, ambient FIGAERO–CIMS measurements as well. There, the PMF analysis of the thermal desorption data identifies organic aerosol (OA) sources (such as biomass burning or oxidation of different precursors) and types, e.g. hydrocarbon-like (HOA) or oxygenated organic aerosol (OOA). This information could also be obtained with the traditional approach, namely the PMF analysis of the mass spectra data integrated for each thermogram. But only our method can also obtain the volatility information for each OA source and type. Additionally, we can identify the contribution of thermal decomposition to the overall signal.

Description: This research was part of the Salutary Umeå Study of Aerosols in Biomass Cookstove Emissions (SUSTAINE) laboratory experiment campaign. We studied ice-nucleating abilities of particulate emissions from solid-fuel-burning cookstoves, using a portable ice nuclei counter, Spectrometer Ice Nuclei (SPIN). These emissions were generated from two traditional cookstove types commonly used for household cooking in sub-Saharan Africa and two advanced gasifier stoves under research to promote sustainable development alternatives. The solid fuels studied included biomass from two different African tree species, Swedish softwood and agricultural residue products relevant to the region. Measurements were performed with a modified version of the standard water boiling test on polydisperse samples from flue gas during burning and size-selected accumulation mode soot particles from a 15 m3 aerosol-storage chamber. The studied soot particle sizes in nanometers were 250, 260, 300, 350, 400, 450 and 500. From this chamber, the particles were introduced to water-supersaturated freezing conditions (−32 to −43 ∘C) in the SPIN. Accumulation mode soot particles generally produced an ice-activated fraction of 10−3 in temperatures 1–1.5 ∘C higher than that required for homogeneous freezing at fixed RHw=115 %. In five special experiments, the combustion performance of one cookstove was intentionally modified. Two of these exhibited a significant increase in the ice-nucleating ability of the particles, resulting in a 10−3 ice activation at temperatures up to 5.9 ∘C higher than homogeneous freezing and the observed increased ice-nucleating ability. We investigated six different physico-chemical properties of the emission particles but found no clear correlation between them and increasing ice-nucleating ability. We conclude that the freshly emitted combustion aerosols form ice via immersion and condensation freezing at temperatures only moderately above homogeneous freezing conditions.

Description: Secondary organic aerosol (SOA) is an important constituent of the atmosphere where SOA particles are formed chiefly by the condensation or reactive uptake of oxidation products of volatile organic compounds (VOCs). The mass yield in SOA particle formation, as well as the chemical composition and volatility of the particles, is determined by the identity of the VOC precursor(s) and the oxidation conditions they experience. In this study, we used an oxidation flow reactor to generate biogenic SOA from the oxidation of Scots pine emissions. Mass yields, chemical composition and volatility of the SOA particles were characterized and compared with SOA particles formed from oxidation of α-pinene and from a mixture of acyclic–monocyclic sesquiterpenes (farnesenes and bisabolenes), which are significant components of the Scots pine emissions. SOA mass yields for Scots pine emissions dominated by farnesenes were lower than for α-pinene but higher than for the artificial mixture of farnesenes and bisabolenes. The reduction in the SOA yield in the farnesene- and bisabolene-dominated mixtures is due to exocyclic C=C bond scission in these acyclic–monocyclic sesquiterpenes during ozonolysis leading to smaller and generally more volatile products. SOA particles from the oxidation of Scots pine emissions had similar or lower volatility than SOA particles formed from either a single precursor or a simple mixture of VOCs. Applying physical stress to the Scots pine plants increased their monoterpene, especially monocyclic β-phellandrene, emissions, which further decreased SOA particle volatility and increased SOA mass yield. Our results highlight the need to account for the chemical complexity and structure of real-world biogenic VOC emissions and stress-induced changes to plant emissions when modelling SOA production and properties in the atmosphere. These results emphasize that a simple increase or decrease in relative monoterpene and sesquiterpene emissions should not be used as an indicator of SOA particle volatility.

Description: Online analysis with mass spectrometers produces complex data sets, consisting of mass spectra with a large number of chemical compounds (ions). Statistical dimension reduction techniques (SDRTs) are able to condense complex data sets into a more compact form while preserving the information included in the original observations. The general principle of these techniques is to investigate the underlying dependencies of the measured variables by combining variables with similar characteristics into distinct groups, called factors or components. Currently, positive matrix factorization (PMF) is the most commonly exploited SDRT across a range of atmospheric studies, in particular for source apportionment. In this study, we used five different SDRTs in analysing mass spectral data from complex gas- and particle-phase measurements during a laboratory experiment investigating the interactions of gasoline car exhaust and α-pinene. Specifically, we used four factor analysis techniques, namely principal component analysis (PCA), PMF, exploratory factor analysis (EFA) and non-negative matrix factorization (NMF), as well as one clustering technique, partitioning around medoids (PAM). All SDRTs were able to resolve four to five factors from the gas-phase measurements, including an α-pinene precursor factor, two to three oxidation product factors, and a background or car exhaust precursor factor. NMF and PMF provided an additional oxidation product factor, which was not found by other SDRTs. The results from EFA and PCA were similar after applying oblique rotations. For the particle-phase measurements, four factors were discovered with NMF: one primary factor, a mixed-LVOOA factor and two α-pinene secondary-organic-aerosol-derived (SOA-derived) factors. PMF was able to separate two factors: semi-volatile oxygenated organic aerosol (SVOOA) and low-volatility oxygenated organic aerosol (LVOOA). PAM was not able to resolve interpretable clusters due to general limitations of clustering methods, as the high degree of fragmentation taking place in the aerosol mass spectrometer (AMS) causes different compounds formed at different stages in the experiment to be detected at the same variable. However, when preliminary analysis is needed, or isomers and mixed sources are not expected, cluster analysis may be a useful tool, as the results are simpler and thus easier to interpret. In the factor analysis techniques, any single ion generally contributes to multiple factors, although EFA and PCA try to minimize this spread. Our analysis shows that different SDRTs put emphasis on different parts of the data, and with only one technique, some interesting data properties may still stay undiscovered. Thus, validation of the acquired results, either by comparing between different SDRTs or applying one technique multiple times (e.g. by resampling the data or giving different starting values for iterative algorithms), is important, as it may protect the user from dismissing unexpected results as “unphysical”.

Description: The volatility distribution of the organic compounds present in secondary organic aerosol (SOA) at different conditions is a key quantity that has to be captured in order to describe SOA dynamics accurately. The development of the Filter Inlet for Gases and AEROsols (FIGAERO) and its coupling to a chemical ionization mass spectrometer (CIMS; collectively FIGAERO–CIMS) has enabled near-simultaneous sampling of the gas and particle phases of SOA through thermal desorption of the particles. The thermal desorption data have been recently shown to be interpretable as a volatility distribution with the use of the positive matrix factorization (PMF) method. Similarly, volatility distributions can be inferred from isothermal particle evaporation experiments when the particle size change measurements are analyzed with process-modeling techniques. In this study, we compare the volatility distributions that are retrieved from FIGAERO–CIMS and particle size change measurements during isothermal particle evaporation with process-modeling techniques. We compare the volatility distributions at two different relative humidities (RHs) and two oxidation conditions. In high-RH conditions, where particles are in a liquid state, we show that the volatility distributions derived via the two ways are similar within a reasonable assumption of uncertainty in the effective saturation mass concentrations that are derived from FIGAERO–CIMS data. In dry conditions, we demonstrate that the volatility distributions are comparable in one oxidation condition, and in the other oxidation condition, the volatility distribution derived from the PMF analysis shows considerably more high-volatility matter than the volatility distribution inferred from particle size change measurements. We also show that the Vogel–Tammann–Fulcher equation together with a recent glass transition temperature parametrization for organic compounds and PMF-derived volatility distribution estimates are consistent with the observed isothermal evaporation under dry conditions within the reported uncertainties. We conclude that the FIGAERO–CIMS measurements analyzed with the PMF method are a promising method for inferring the volatility distribution of organic compounds, but care has to be taken when the PMF factors are analyzed. Future process-modeling studies about SOA dynamics and properties could benefit from simultaneous FIGAERO–CIMS measurements.

Description: Source apportionment of organic aerosols (OAs) is of great importance to better understand the health impact and climate effects of particulate matter air pollution. Air quality models are used as potential tools to identify OA components and sources at high spatial and temporal resolution; however, they generally underestimate OA concentrations, and comparisons of their outputs with an extended set of measurements are still rare due to the lack of long-term experimental data. In this study, we addressed such challenges at the European level. Using the regional Comprehensive Air Quality Model with Extensions (CAMx) and a volatility basis set (VBS) scheme which was optimized based on recent chamber experiments with wood burning and diesel vehicle emissions, and which contains more source-specific sets compared to previous studies, we calculated the contribution of OA components and defined their sources over a whole-year period (2011). We modeled separately the primary and secondary OA contributions from old and new diesel and gasoline vehicles, biomass burning (mostly residential wood burning and agricultural waste burning excluding wildfires), other anthropogenic sources (mainly shipping, industry and energy production) and biogenic sources. An important feature of this study is that we evaluated the model results with measurements over a longer period than in previous studies, which strengthens our confidence in our modeled source apportionment results. Comparison against positive matrix factorization (PMF) analyses of aerosol mass spectrometric measurements at nine European sites suggested that the modified VBS scheme improved the model performance for total OA as well as the OA components, including hydrocarbon-like (HOA), biomass burning (BBOA) and oxygenated components (OOA). By using the modified VBS scheme, the mean bias of OOA was reduced from −1.3 to −0.4 µg m−3 corresponding to a reduction of mean fractional bias from −45 % to −20 %. The winter OOA simulation, which was largely underestimated in previous studies, was improved by 29 % to 42 % among the evaluated sites compared to the default parameterization. Wood burning was the dominant OA source in winter (61 %), while biogenic emissions contributed ∼ 55 % to OA during summer in Europe on average. In both seasons, other anthropogenic sources comprised the second largest component (9 % in winter and 19 % in summer as domain average), while the average contributions of diesel and gasoline vehicles were rather small (∼ 5 %) except for the metropolitan areas where the highest contribution reached 31 %. The results indicate the need to improve the emission inventory to include currently missing and highly uncertain local emissions, as well as further improvement of VBS parameterization for winter biomass burning. Although this study focused on Europe, it can be applied in any other part of the globe. This study highlights the ability of long-term measurements and source apportionment modeling to validate and improve emission inventories, and identify sources not yet properly included in existing inventories.

Description: The fraction of gasoline direct-injection (GDI) vehicles comprising the total vehicle pool is projected to increase in the future. However, thorough knowledge about the influence of GDI engines on important atmospheric chemistry processes is missing – namely, their contribution to secondary organic aerosol (SOA) precursor emissions, contribution to SOA formation, and potential role in biogenic–anthropogenic interactions. The objectives of this study were to (1) characterize emissions from modern GDI vehicles and investigate their role in SOA formation chemistry and (2) investigate biogenic–anthropogenic interactions related to SOA formation from a mixture of GDI-vehicle emissions and a model biogenic compound, α-pinene. Specifically, we studied SOA formation from modern GDI-vehicle emissions during the constant-load driving. In this study we show that SOA formation from GDI-vehicle emissions was observed in each experiment. Volatile organic compounds (VOCs) measured with the proton-transfer-reaction time-of-flight mass spectrometer (PTR-ToF-MS) could account for 19 %–42 % of total SOA mass generated in each experiment. This suggests that there were lower-volatility intermediate VOCs (IVOCs) and semi-volatile organic compounds (SVOCs) in the GDI-vehicle exhaust that likely contributed to SOA production but were not detected with the instrumentation used in this study. This study also demonstrates that two distinct mechanisms caused by anthropogenic emissions suppress α-pinene SOA mass yield. The first suppressing effect was the presence of NOx. This mechanism is consistent with previous reports demonstrating suppression of biogenic SOA formation in the presence of anthropogenic emissions. Our results indicate a possible second suppressing effect, and we suggest that the presence of anthropogenic gas-phase species may have suppressed biogenic SOA formation by alterations to the gas-phase chemistry of α-pinene. This hypothesized change in oxidation pathways led to the formation of α-pinene oxidation products that most likely did not have vapor pressures low enough to partition into the particle phase. Overall, the presence of gasoline-vehicle exhaust caused a more than 50 % suppression in α-pinene SOA mass yield compared to the α-pinene SOA mass yield measured in the absence of any anthropogenic influence.

Description: Understanding long-term variations in aerosol loading is essential for evaluating the health and climate effects of airborne particulates as well as the effectiveness of pollution control policies. The expected satellite lifetime is about 10 to 15 years. Therefore, to study the variations of atmospheric constituents over longer periods information from different satellites must be utilized. Here we introduce a method to construct a combined annual and seasonal long time series of AOD at 550 nm using the Along-Track Scanning Radiometers (ATSR: ATSR-2 and AATSR combined) and the MODerate resolution Imaging Spectroradiometer on Terra (MODIS/Terra), which together cover the 1995–2017 period. The long-term (1995–2017) combined AOD time series are presented for all of mainland China, for southeastern (SE) China and for 10 selected regions in China. Linear regression was applied to the combined AOD time series constructed for individual L3 (1∘ × 1∘) pixels to estimate the AOD tendencies for two periods: 1995–2006 (P1) and 2011–2017 (P2), with respect to the changes in the emission reduction policies in China. During P1, the annually averaged AOD increased by 0.006 (or 2 % of the AOD averaged over the corresponding period) per year across all of mainland China, reflecting increasing emissions due to rapid economic development. In SE China, the annual AOD positive tendency in 1995–2006 was 0.014 (3 %) per year, reaching maxima (0.020, or 4 %, per year) in Shanghai and the Pearl River Delta regions. After 2011, during P2, AOD tendencies reversed across most of China with the annually averaged AOD decreasing by −0.015 (−6 %) per year in response to the effective reduction of the anthropogenic emissions of primary aerosols, SO2 and NOx. The strongest AOD decreases were observed in the Chengdu (−0.045, or −8 %, per year) and Zhengzhou (−0.046, or −9 %, per year) areas, while over the North China Plain and coastal areas the AOD decrease was lower than −0.03 (approximately −6 %) per year. In the less populated areas the AOD decrease was small. The AOD tendency varied by both season and region. The increase in the annually averaged AOD during P1 was mainly due to an increase in summer and autumn in SE China (0.020, or 4 %, and 0.016, or 4 %, per year, respectively), while during winter and spring the AOD actually decreased over most of China. The AOD negative tendencies during the 2011–2017 period were larger in summer than in other seasons over the whole of China (ca. −0.021, or −7 %, per year) and over SE China (ca. −0.048, or −9 %, per year). The long-term AOD variations presented here show a gradual decrease in the AOD after 2011 with an average reduction of 30 %–50 % between 2011 and 2017. The effect is more visible in the highly populated and industrialized regions in SE China, as expected.

Description: The volatility of organic aerosols remains poorly understood due to the complexity of speciation and multiphase processes. In this study, we extracted humic-like substances (HULIS) from four atmospheric aerosol samples collected at the SORPES station in Nanjing, eastern China, and investigated the volatility behavior of particles at different sizes using a Volatility Tandem Differential Mobility Analyzer (VTDMA). In spite of the large differences in particle mass concentrations, the extracted HULIS from the four samples all revealed very high-oxidation states (O : C 〉 0.95), indicating secondary formation as the major source of HULIS in Yangtze River Delta (YRD). An overall low volatility was identified for the extracted HULIS, with the volume fraction remaining (VFR) higher than 55 % for all the regenerated HULIS particles at the temperature of 280 °C. A kinetic mass transfer model was applied to the thermodenuder (TD) data to interpret the observed evaporation pattern of HULIS, and to derive the mass fractions of semi-volatile (SVOC), low-volatility (LVOC) and extremely low-volatility components (ELVOC). The results showed that LVOC and ELVOC dominated (more than 80 %) the total volume of HULIS. Atomizing processes led to a size-dependent evaporation of regenerated HULIS particles, and resulted in more ELVOC in smaller particles. In order to understand the role of interaction between inorganic salts and atmospheric organic mixtures in the volatility of an organic aerosol, the evaporation of mixed samples of ammonium sulfate (AS) and HULIS was measured. The results showed a significant but nonlinear influence of ammonium sulfate on the volatility of HULIS. The estimated fraction of ELVOC in the organic part of the largest particles (145 nm) increased from 26 %, in pure HULIS samples, to 93 % in 1 : 3 (mass ratio of HULIS : AS) mixed samples, to 45 % in 2 : 2 mixed samples, and to 70 % in 3 : 1 mixed samples, suggesting that the interaction with ammonium sulfate tends to decrease the volatility of atmospheric organic compounds. Our results demonstrate that HULIS are important low-volatility, or even extremely low-volatility, compounds in the organic-aerosol phase. As important formation pathways of atmospheric HULIS, multiphase processes, including oxidation, oligomerization, polymerization and interaction with inorganic salts, are indicated to be important sources of low-volatility and extremely low-volatility species of organic aerosols.