Aerosols and their interaction with clouds constitute the largest uncertainty in estimating the radiative forcing affecting the climate system. Secondary aerosol formation is responsible for a large fraction of the cloud condensation nuclei in the global atmosphere. Wetlands are important to the budgets of methane and carbon dioxide, but the potential role of wetlands in aerosol formation has not been investigated. Here we use direct atmospheric sampling at the Siikaneva wetland in Finland to investigate the emission of methane and volatile organic compounds, and subsequently formed atmospheric clusters and aerosols. We find that terpenes initiate stronger atmospheric new particle formation than is typically observed over boreal forests and that, in addition to large emissions of methane which cause a warming effect, wetlands also have a cooling effect through emissions of these terpenes. We suggest that new wetlands produced by melting permafrost need to be taken into consideration as sources of secondary aerosol particles when estimating the role of increasing wetland extent in future climate change.
Abstract. The Station for Measuring Ecosystem–Atmosphere Relations (SMEAR) II, located within the boreal forest of Finland, is a unique station in the world due to the wide range of long-term measurements tracking the Earth–atmosphere interface. In this study, we characterize the composition of organic aerosol (OA) at SMEAR II by quantifying its driving constituents. We utilize a multi-year data set of OA mass spectra measured in situ with an Aerosol Chemical Speciation Monitor (ACSM) at the station. To our knowledge, this mass spectral time series is the longest of its kind published to date. Similarly to other previously reported efforts in OA source apportionment from multi-seasonal or multi-annual data sets, we approached the OA characterization challenge through positive matrix factorization (PMF) using a rolling window approach. However, the existing methods for extracting minor OA components were found to be insufficient for our rather remote site. To overcome this issue, we tested a new statistical analysis framework. This included unsupervised feature extraction and classification stages to explore a large number of unconstrained PMF runs conducted on the measured OA mass spectra. Anchored by these results, we finally constructed a relaxed chemical mass balance (CMB) run that resolved different OA components from our observations. The presented combination of statistical tools provided a data-driven analysis methodology, which in our case achieved robust solutions with minimal subjectivity. Following the extensive statistical analyses, we were able to divide the 2012–2019 SMEAR II OA data (mass concentration interquartile range (IQR): 0.7, 1.3, and 2.6 µg m−3) into three sub-categories – low-volatility oxygenated OA (LV-OOA), semi-volatile oxygenated OA (SV-OOA), and primary OA (POA) – proving that the tested methodology was able to provide results consistent with literature. LV-OOA was the most dominant OA type (organic mass fraction IQR: 49 %, 62 %, and 73 %). The seasonal cycle of LV-OOA was bimodal, with peaks both in summer and in February. We associated the wintertime LV-OOA with anthropogenic sources and assumed biogenic influence in LV-OOA formation in summer. Through a brief trajectory analysis, we estimated summertime natural LV-OOA formation of tens of ng m−3 h−1 over the boreal forest. SV-OOA was the second highest contributor to OA mass (organic mass fraction IQR: 19 %, 31 %, and 43 %). Due to SV-OOA's clear peak in summer, we estimate biogenic processes as the main drivers in its formation. Unlike for LV-OOA, the highest SV-OOA concentrations were detected in stable summertime nocturnal surface layers. Two nearby sawmills also played a significant role in SV-OOA production as also exemplified by previous studies at SMEAR II. POA, taken as a mix of two different OA types reported previously, hydrocarbon-like OA (HOA) and biomass burning OA (BBOA), made up a minimal OA mass fraction (IQR: 2 %, 6 %, and 13 %). Notably, the quantification of POA at SMEAR II using ACSM data was not possible following existing rolling PMF methodologies. Both POA organic mass fraction and mass concentration peaked in winter. Its appearance at SMEAR II was linked to strong southerly winds. Similar wind direction and speed dependence was not observed among other OA types. The high wind speeds probably enabled the POA transport to SMEAR II from faraway sources in a relatively fresh state. In the event of slower wind speeds, POA likely evaporated and/or aged into oxidized organic aerosol before detection. The POA organic mass fraction was significantly lower than reported by aerosol mass spectrometer (AMS) measurements 2 to 4 years prior to the ACSM measurements. While the co-located long-term measurements of black carbon supported the hypothesis of higher POA loadings prior to year 2012, it is also possible that short-term (POA) pollution plumes were averaged out due to the slow time resolution of the ACSM combined with the further 3 h data averaging needed to ensure good signal-to-noise ratios (SNRs). Despite the length of the ACSM data set, we did not focus on quantifying long-term trends of POA (nor other components) due to the high sensitivity of OA composition to meteorological anomalies, the occurrence of which is likely not normally distributed over the 8-year measurement period. Due to the unique and realistic seasonal cycles and meteorology dependences of the independent OA subtypes complemented by the reasonably low degree of unexplained OA variability, we believe that the presented data analysis approach performs well. Therefore, we hope that these results encourage also other researchers possessing several-year-long time series of similar data to tackle the data analysis via similar semi- or unsupervised machine-learning approaches. This way the presented method could be further optimized and its usability explored and evaluated also in other environments.
Boreal forest acts as a carbon sink and contributes to the formation of secondary organic aerosols via emission of aerosol precursor compounds. However, these influences on the climate system are poorly quantified. Here we show direct observational evidence that aerosol emissions from the boreal forest biosphere influence warm cloud microphysics and cloud-aerosol interactions in a scale-dependent and highly dynamic manner. Analyses of in-situ and ground-based remote sensing observations from the SMEAR II station in Finland, conducted over eight months in 2014, reveal significant increases in aerosol load over the forest one to three days after aerosol-poor marine air enters the forest environment.We find that these changes are consistent with secondary organic aerosol formation and, together with water vapor emissions from evapotranspiration, are associated with changes in the radiative properties of warm, low-level clouds. The feedbacks between boreal forest emissions and aerosol-cloud interactions and the highly dynamic nature of these Petäjä et al. (2021) Influence of biogenic emissions from boreal forests on aerosol-cloud-interactions, Nature Geosci. (accepted)
Abstract. Cyclohexene (C6H10) is commonly used as a proxy for biogenic monoterpenes, when studying their oxidation mechanisms and secondary organic aerosol (SOA) formation. The ozonolysis of cyclohexene has been shown to be effective at producing highly oxygenated organic molecules (HOMs), a group of molecules known to be important in the formation of SOA. Here, we provide an in-depth look at how the formation and fate of the broad range of observed HOMs changed with perturbations from NOx and seed particles. HOMs were produced in a chamber from cyclohexene ozonolysis and measured with a chemical ionisation mass spectrometer (CIMS) using nitrate (NO3-) as reagent ion. As high-resolution CIMS instruments provide mass spectra with numerous ion signals and a wealth of information that can be hard to manage, we employed a primarily statistical approach for the data analysis. To utilise as many individual HOM signals as possible, each compound was assigned a parameter describing the quality of the observed signal. These parameters were then used as weights or to determine the inclusion of a given signal in further analyses. Under unperturbed ozonolysis conditions, known HOM peaks were observed in the chamber, including C6H8O9 as the largest HOM signal and C12H20O9 as the largest “dimer” product. With the addition of nitric oxide (NO) into the chamber, the spectrum changed considerably, as expected. Dimer product signals decreased overall, but an increase in dimers with nitrate functionalities was seen, as a result of NO3 radical oxidation. The response of monomer signals to NO addition varied, and while nitrate-containing monomers increased, non-nitrate signals either increased or decreased, depending on the individual molecules. The addition of seed aerosol increased the condensation sink, which markedly decreased the signals of all low-volatility compounds. Larger molecules were seen to have a higher affinity for condensation, but a more detailed analysis showed that the uptake was controlled mainly by the number of oxygen atoms in each molecule. Nitrates required higher mass and higher oxygen content to condense at similar rates as the non-nitrate HOMs. We also tested two existing elemental-composition-based parameterisations for their ability to reproduce the condensation observed in our cyclohexene system. Both predicted higher volatilities than observed, most likely due to the number of oxygen atoms enhancing the product uptake more than the models would suggest.
Abstract. The Station for Measuring Ecosystem Atmosphere Relations (SMEAR) II is a unique station in the world due to the wide range of long-term measurements tracking the Earth-atmosphere interface. In this study, we characterize the composition of organic aerosol (OA) at SMEAR II by quantifying its driving constituents. We utilize a multi-year data set of OA mass spectra measured in situ with an Aerosol Chemical Speciation Monitor (ACSM) at the station. To our knowledge, this mass spectral time series is the longest of its kind published to date, and its detailed analysis required development of a new methodology. To this purpose, we developed an efficient and robust data analysis framework utilizing machine learning tools. These included unsupervised feature extraction and classification stages to manage and process the large amounts of data. The extensive chemometric analysis was conducted with a combination of Positive Matrix Factorization (PMF), rolling window analysis, bootstrapping, K-Means clustering, data weighting and diagnostics based algorithmic choice-making, among others. This combination of statistical tools provided a data driven analysis methodology to achieve robust solutions with minimal subjectivity. Following the extensive statistical analyses, we were able to divide the 2012–2019 SMEAR II OA data (mass concentration interquartile range (IQR): 0.7, 1.3, 2.6 µg m−3) to three sub-categories: low-volatility oxygenated OA (LV-OOA), semi-volatile oxygenated OA (SV-OOA), and primary OA (POA). LV-OOA was the most dominant OA type (organic mass fraction IQR: 49, 62, and 73 %). The seasonal cycle of LV-OOA was bimodal, with peaks both in summer and in February. We associated the wintertime LV-OOA with anthropogenic sources and assumed biogenic influence in LV-OOA formation in summer. Through a brief trajectory analysis, we estimated summertime natural LV-OOA formation of tens of ng m−3 h−1 over the boreal forest. SV-OOA was the second highest contributor to OA mass (organic mass fraction IQR: 19, 31, and 43 %). Due to SV-OOA’s clear peak in summer, we estimate biogenic processes as the main drivers in its formation. Unlike for LV-OOA, the highest SV-OOA concentrations were detected in stable summertime nocturnal surface layers. However, also the nearby sawmills likely played a significant role in SV-OOA production as also exemplified by previous studies at SMEAR II. POA, taken as a mix of two different OA types reported previously, hydrocarbon-like OA (HOA) and biomass burning OA (BBOA), made up a minimal OA mass fraction (IQR: 2, 6, and 13 %). Both POA organic mass fraction and mass concentration peaked in winter. Its appearance at SMEAR II was linked to strong southerly winds. The high wind speeds probably enabled the POA transport to SMEAR II from faraway sources in a relatively fresh state. In case of slower wind speeds, POA likely evaporated or aged into oxidized organic aerosol before detection. The POA organic mass fraction was significantly lower than reported by aerosol mass spectrometer (AMS) measurements two to four years prior to the ACSM measurements. While the co-located long-term measurements of black carbon supported the hypothesis of higher POA loadings prior to year 2012, it is also possible that ACSM was less efficiently capturing short term (POA) pollution plumes. Despite the length of the ACSM data set, we did not focus on quantifying long-term trends of POA (nor other components) due to the high sensitivity of OA composition to meteorological anomalies, the occurrence of which is likely not normally distributed over the eight year measurement period. We hope that our successfully applied methodology encourages also other researchers possessing several-year-long time series of similar data to tackle the data analysis via similar semi- or unsupervised machine learning approaches. This way aerosol chemometric analysis procedures would be further developed into yet more streamlined and autonomous directions.
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