Find research datasets worth reusing

opencc-by-4.0Jan 2022View details →

Output data of the models used in "Comparison of six approaches to predicting droplet activation of surface active aerosol. Part 1: moderately surface active organics" by Vepsäläinen et al. (2022)

Output data of the different models used in "Comparison of six approaches to predicting droplet activation of surface active aerosol. Part 1: moderately surface active organics" by Vepsäläinen et al. (2022). Output data is included for 50 nm particles containing malonic acid (mna), succinic acid (sca) and glutaric acid (glutarica), mixed with ammonium sulphate (AS) in different organic mass fractions.  A plotter that allows the user to plot the Köhler curves, surface tensions and organic partitioning factors during droplet growth from the model output data provided is included.

European Aerosol Phenomenology - 8: Harmonised Source Apportionment of Organic Aerosol using 22 Year-long ACSM/AMS Datasets

Organic aerosol (OA) is a key component of total submicron particulate matter (PM1), and comprehensive knowledge of OA sources across Europe is crucial to mitigate PM1 levels. Europe has a well-established air quality research infrastructure from which yearlong datasets using 21 aerosol chemical speciation monitors (ACSMs) and 1 aerosol mass spectrometer (AMS) were gathered during 2013–2019. It includes 9 non-urban and 13 urban sites. This study developed a state-of-the-art source apportionment protocol to analyse long-term OA mass spectrum data by applying the most advanced source apportionment strategies (i.e., rolling PMF, ME-2, and bootstrap). This harmonised protocol was followed strictly for all 22 datasets, making the source apportionment results more comparable. In addition, it enables quantification of the most common OA components such as hydrocarbon-like OA (HOA), biomass burning OA (BBOA), cooking-like OA (COA), more oxidised-oxygenated OA (MO-OOA), and less oxidised-oxygenated OA (LO-OOA). Other components such as coal combustion OA (CCOA), solid fuel OA (SFOA: mainly mixture of coal and peat combustion), cigarette smoke OA (CSOA), sea salt (mostly inorganic but part of the OA mass spectrum), coffee OA, and ship industry OA could also be separated at a few specific sites. Oxygenated OA (OOA) components make up most of the submicron OA mass (average = 71.1%, range from 43.7 to 100%). Solid fuel combustion-related OA components (i.e., BBOA, CCOA, and SFOA) are still considerable with in total 16.0% yearly contribution to the OA, yet mainly during winter months (21.4%). Overall, this comprehensive protocol works effectively across all sites governed by different sources and generates robust and consistent source apportionment results. Our work presents a comprehensive overview of OA sources in Europe with a unique combination of high time resolution (30–240 min) and long-term data coverage (9–36 months), providing essential information to improve/validate air quality, health impact, and climate models.

opencc-by-4.0Jul 2021View details →

Dataset: Time-dependent source apportionment of submicron organic aerosol for a rural site in an alpine valley using a rolling positive matrix factorisation (PMF) window

Uploaded igor pxp files are the data to generate all figures of the results from our publication in Atmospheric Chemistry and Physics with the name of "Time dependent source apportionment of submicron organic aerosol for a rural site in an alpine valley using a rolling PMF window" by Chen et al. (2021). This study deployed a novel and advanced source apportionment technique on a dataset measured in Magadino. Rolling PMF allows retrieving more realistic, time-dependent and detailed information of the organic aerosol sources. This work highlights the strength of the rolling PMF mechanism by comparing it with the results derived from conventional seasonal PMF. Overall, this comprehensive interpretation of chemical speciation monitor (ACSM) data could be a role model for similar analyses.

opencc-by-4.0Jul 2021View details →

Global health burden of ambient PM2.5 and the role of anthropogenic black carbon and organic aerosols

SI Dataset S1 (SI DataS1) Excess mortality from ambient PM2.5 exposure among adults, children, and neonates. SI Dataset S2 (SI DataS2) Pie charts showing distribution of excess death by disease among adults, children, and neonates. SI Dataset S3 (SI DataS3) Sector contribution to ambient PM2.5-related excess death under EqT and 2BSP assumptions SI Dataset S4 (SI DataS4) Excess death from ambient BC exposure and contributions of major anthropogenic sectors. SI Dataset S5 (SI DataS5) Excess death from ambient POA exposure and contribution of major anthropogenic sectors. SI Dataset S6 (SI DataS6) Excess death from ambient aSOA exposure and contribution of major anthropogenic sectors. SI Dataset S7 (SI DataS7) Sector contribution to excess death under EqT and 2BSP relative toxicity assumptions by major regions.

opencc-by-4.0Jul 2023View details →

Model output from CAABA/MECCA study "Development of a multiphase chemical mechanism to improve secondary organic aerosol formation in CAABA/MECCA (version 4.7.0)"

This dataset includes the main data obtained during the study "Development of a multiphase chemical mechanism to improve secondary organic aerosol formation in CAABA/MECCA (version 4.7.0)" (DOI:10.5194/gmd-2023-102). The updated model code can be found at zenodo.org (DOI:10.5281/zenodo.7944174). The data can be used to replicate the results shown in the manuscript. Contained are results produced by the updated CAABA/MECCA (version 4.7.0) and reference data from CAABA/MECCA version 4.5.5. In version 4.7.0, new biogenic and anthropogenic species are introduced to the model (limonene and long-chained alkanes) with refined multiphase chemistry, while new reaction pathways are added for existing compounds (isoprene, benzene and IEPOX). The output is generated to evaluate model results in terms of temperature- and NOx-dependency.

opencc-by-4.0Oct 2023View details →

Simulation dataset and plotting scripts used for journal article "Impact of acidity and surface modulated acid dissociation on cloud response to organic aerosol" by Sengupta et al. (2024)

Simulation data underlying all figures presented in "Impact of acidity and surface modulated acid dissociation on cloud response to organic aerosol" by Sengupta et al. (2024). DOI: 10.5194/acp-24-1467-2024 The data for each figure and the plotting scripts are included in a zip file labelled by the figure number as presented in the paper and accompanying supplement.

opencc-by-4.0Mar 2024View details →

Simulation dataset and plotting scripts used for journal article "Surface modulated dissociation of organic aerosol acids and bases in different atmospheric environments" by Sengupta and Prisle (2024)

Simulation data underlying all figures presented in "Surface modulated dissociation of organic aerosol acids and bases in different atmospheric environments" by Sengupta and Prisle (2024) <a href="http://dx.doi.org/10.1080/02786826.2024.2323641" target="_blank" rel="noopener noreferrer">http://dx.doi.org/10.1080/02786826.2024.2323641</a>.  The data for each figure and the plotting scripts are included in a zip file labelled by the figure number as presented in the paper and accompanying supplement.

opencc-by-4.0Feb 2022View details →

Chemical composition and sources of organic aerosol on the Adriatic coast in Croatia

Air pollution studies are still scarce in some areas in Europe like around the Adriatic Sea. Source apportionment of the fine particulate (PM2.5) organic aerosol (OA) was conducted near Rogoznica, a small touristic settlement on the Eastern Adriatic coast of Croatia, near Lake Rogoznica (43.53° N, 15.95° E). Filter-based offline analyses of PM by a high resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS) and an extractive electrospray ionization time-of-flight mass spectrometer (EESI-TOF) were used to apportion OA to its sources. We quantified the contributions of fresh biomass burning OA (BBOA) and three oxygenated OA (OOA), denoted as background OOA (bkgOOA), summer OOA (SOOA), and sulfur-containing OOA (SC-OOA). The bkgOOA component correlates with anthropogenically influenced secondary inorganic aerosol constituents (e.g. sulfate) and dominates OA both during the warm and cold seasons (44%). It exhibits characteristics of regional SOA and there are indications that during the warm season SOA from wildfires could be substantial contributors. EESI-TOF measurements of the levoglucosan related ion showed a high correlation with the BBOA factor. Secondary OA has a similar molecular composition during the cold and warm seasons – in line with the large contribution of bkgOOA throughout the investigated seasons. SOOA comprises 19% of total OA and increases exponentially with the local temperature, consistent with SOA production by oxidation of biogenic emissions. In addition to biogenic precursors, other precursors (alkanes and aromatic) plausibly also contribute to the SOOA enhancement during the warm season. SC-OOA is a minor contributor to OA (6%) and is most likely linked to emissions from a close-by marine lake.

Dataset for "Experimental determination of the relationship between organic aerosol viscosity and ice nucleation at upper free tropospheric conditions"

Data and scripts used to create the figures in the manuscript titled: "Experimental determination of the relationship between organic aerosol viscosity and ice nucleation at upper free tropospheric conditions"

opencc-by-4.0May 2024View details →

Atmospheric aerosol chemical characterization and organic aerosol source apportionment by HR-TOF-AMS in the Po Valley during RHAPS (2021)

Time series of non-refractory submicrometric aerosol (PM1) chemical components (sulfate, nitrate, ammonium, chloride, and organic aerosol, OA) from RHAPS campaigns (winter and summer 2021) at Bologna (BO) and San Pietro Capofiume (SPC), Po Valley, Italy. Time series and profiles of OA source factors derived from PMF.

opencc-by-4.0Sep 2024View details →

Global warming effects of cropland expansion by reduction of biogenic secondary organic aerosols

This is the dataset to support our paper title of “Global warming effects of cropland expansion by reduction of biogenic secondary organic aerosols”. Cropland expansion has been the most significant global land use change since industrialization. However, evaluations of radiative forcing from land use changes have often neglected the radiative effects of secondary organic aerosols (SOA) linked to cropland expansion. Sensitivity experiments using an Earth system model that incorporates advanced SOA processes reveal approximately a 10% reduction in the global biogenic SOA burden due to cropland expansion since industrialization. This reduction weakens SOA’s role in scattering radiation and forming clouds, leading to a decline in its cooling effect by 146 mW m⁻², which is equivalent to 8% of the warming caused by CO₂ emissions since industrialization. This effect is expected to increase by nearly half under future climate warming and reduced emissions scenarios. Therefore, policies addressing food security and climate change must consider the radiative impacts of biogenic SOA associated with cropland expansion.   The dataset consists of three zip files, which include model code and output from sensitivity simulations conducted with the Community Earth System Model (CESM) version 1.2.2, using the IMPACT aerosol module and an offline radiative model. The files are described as follows:   Model code.zip: Contains the source code for the IMPACT aerosol module, which was integrated as an additional aerosol module within CESM version 1.2.2, available from the NCAR repository.   PD_Cases.zip: This file contains two folders (Concentration and Radiation), which hold the simulation results for concentration and radiation in present-day cases. The Concentration folder includes 13 subfolders. The Radiation folder contains four subfolders representing different radiation simulation results. These include simulations with and without the direct radiative effects of SOA (labeled ADEwSOA and ADEwoSOA respectively) and simulations with and without the indirect radiative effects of SOA (labeled AIEwSOA and AIEwoSOA respectively). Each of these subfolders contains 13 additional subfolders, which share the same names as those in the Concentration folder. These 13 subfolders correspond to specific sensitivity experiments, the details of which are explained below. Within each subfolder, you will find the five-year averaged model output for all 12 months. 18L20E20C represents simulations with pre-industrial land use, and present-day emissions and climate conditions; 20L20E20C represents simulations with present-day land use, emissions, and climate conditions. Eight subfolders for single vegetation type transition experiments include model output for cases where land use transitions from deciduous broadleaf forest to cropland (DBF2CRO), evergreen broadleaf forest to cropland (EBF2CRO), evergreen needleleaf forest to cropland (ENF2CRO), grassland to cropland (GRA2CRO), shrubland to cropland (SHR2CRO), deciduous broadleaf forest to grassland (DBF2GRA), evergreen broadleaf forest to grassland (EBF2GRA), and evergreen needleleaf forest to grassland (ENF2GRA). Three subfolders for latitude-specific experiments cover conversions for all vegetation types in tropical (20°S–20°N, LLAT), mid-latitude (50°S–20°S and 20°N–50°N, MLAT), and high-latitude (south of 50°S and north of 50°N, HLAT) regions.   FU_Cases.zip: This file contains two folders (Concentration and Radiation), which hold the simulation results for concentration and radiation in future cases. The Concentration folder includes 4 subfolders. The Radiation folder contains four subfolders representing different radiation simulation results. These include simulations with and without the direct radiative effects of SOA (labeled ADEwSOA and ADEwoSOA respectively) and simulations with and without the indirect radiative effects of SOA (labeled AIEwSOA and AIEwoSOA respectively). Each of these subfolders contains 4 additional subfolders, which share the same names as those in the Concentration folder. These 4 subfolders correspond to specific sensitivity experiments, the details of which are explained below. Within each subfolder, you will find the five-year averaged model output for all 12 months. 18L20E21C represents simulations with pre-industrial land use, and present-day emissions, and future climate conditions; 18L21E21C represents simulations with pre-industrial land use, future emissions, and future climate conditions; 20L20E21C represents simulations with present-day land use, and present-day emissions, and future climate conditions; 20L21E21C represents simulations with present-day land use, future emissions, and future climate conditions.

opencc-by-4.0May 2023View details →

Dataset from: Ultraviolet refractive index values of organic aerosol extracted from deciduous forestry, urban and marine environments

The refractive index values of atmospheric aerosols are required to address the large uncertainties in the magnitude of atmospheric radiative forcing and measurements of the refractive index dispersion with wavelength of particulate matter sampled from the atmosphere are rare over ultraviolet wavelengths. An ultraviolet-optimized spectroscopic system illuminates optically-trapped single particles from a range of tropospheric environments to determine the particle’s optical properties. Aerosol from remote marine, polluted urban, and forestry environments is collected on quartz filters, and the organic fraction is extracted and nebulized to form micron-sized spherical particles. The radius and the real component of refractive index dispersion with wavelength of the optically trapped particles are determined to a precision of 0.001 µm and 0.002 respectively over a near-ultraviolet-visible wavelength range of 0.320–0.480 µm. Remote marine aerosol is observed to have the lowest refractive index (n=1.442 (λ=0.350 µm)), with above-canopy rural forestry aerosol (n=1.462–1.481 (λ=0.350 µm)) and polluted urban aerosol (n=1.444–1.485 (λ=0.350 µm)) showing similar refractive index dispersions with wavelength. In-canopy rural forestry aerosol is observed to have the highest refractive index value (n=1.508 (λ=0.350 µm)). The study presents the first single particle measurements of the dispersion of refractive index with wavelength of atmospheric aerosol samples below wavelengths of 0.350 µm. The Cauchy dispersion equation, commonly used to describe the visible refractive index variation of aerosol particles, is demonstrated to extend to ultraviolet wavelengths below 0.350 µm for the urban, forestry, and atmospheric aerosol water-insoluble extracts from these environments. A 1D radiative-transfer calculation of the difference in top-of-the-atmosphere albedo between atmospheric core-shell mineral aerosol with and without films of this material demonstrates the importance of organic films forming on mineral aerosol. The raw experimental spectra collected and analysed in this study are provided, as well as files for the calibrated wavelengths.

opencc-by-4.0Nov 2021View details →

Data supporting the study "The evolution of surface structure during atmospheric ageing of nano-scale coatings of an organic surfactant aerosol proxy" by Milsom et al.

Reduced neutron reflectometry (NR) data associated with the study "The evolution of surface structure during atmospheric ageing of nano-scale coatings of an organic surfactant aerosol proxy" by Milsom et al.. One folder contains the raw data for fitted parameters obtained from NR curves and supporting figure 3 in the study. The other contains a set of sub-folders which have reduced NR data along with python scripts which were used to create and fit the interfacial model to the data. Fitting bounds for each parameter are found in these scripts.

opencc-by-4.0Jan 2022View details →

Comparing black-carbon- and aerosol-absorption-measuring instruments – a new system using lab-generated soot coated with controlled amounts of secondary organic matter

A preprint of the publication can be found here: <a href="https://amt.copernicus.org/preprints/amt-2021-214/">AMTD - Response of black carbon and aerosol absorption measuring instruments to laboratory-generated soot coated with controlled amounts of secondary organic matter (copernicus.org)</a> (doi.org/10.5194/amt-2021-214). The files correspond to the raw data sets used for Figures 3 and 4 of the aforementioned publication. The date and start/stop time of the measurements are listed in the file "overview_measurements".

Dataset supporting the publication, "Constraints on the role of Laplace pressure in multiphase reactions and viscosity of organic aerosols"

This dataset supports the publication, "Constraints on the role of Laplace pressure in multiphase reactions and viscosity of organic aerosols". This project was funded by the U.S. National Science Foundation Postdoctoral Fellowship Award #AGS-1624696.

Highly time-resolved chemical speciation and source apportionment of organic aerosol components in Delhi, India, using extractive electrospray ionization mass spectrometry

In recent years, the Indian capital city of Delhi has been impacted by very high levels of air pollution, especially during winters. Comprehensive knowledge of the composition and sources of the organic aerosol (OA), which constitutes a substantial fraction of total particulate mass (PM) in Delhi, is central to formulating effective public health policies. Previous source apportionment studies in Delhi identified key sources of primary OA (POA) and showed that secondary OA (SOA) played a major role but were unable to resolve specific SOA sources. We address the latter through the first field deployment of an extractive electrospray ionization timeof- flight mass spectrometer (EESI-TOF) in Delhi, together with a high-resolution aerosol mass spectrometer (AMS). Measurements were conducted during the winter of 2018/19, and positive matrix factorization (PMF) was used separately on AMS and EESI-TOF datasets to apportion the sources of OA. AMS PMF analysis yielded three primary and two secondary factors which were attributed to hydrocarbon-like OA (HOA), biomass burning OA (BBOA-1 and BBOA-2), more oxidized oxygenated OA (MO-OOA), and less oxidized oxygenated OA (LOOOA). On average, 40%of the total OA mass was apportioned to the secondary factors. The SOA contribution to total OA mass varied greatly between the daytime (76.8 %, 10:00–16:00 local time (LT)) and nighttime (31.0 %, 21:00–04:00 LT). The higher chemical resolution of EESI-TOF data allowed identification of individual SOA sources. The EESI-TOF PMF analysis in total yielded six factors, two of which were primary factors (primary biomass burning and cooking-related OA). The remaining four factors were predominantly of secondary origin: aromatic SOA, biogenic SOA, aged biomass burning SOA, and mixed urban SOA. Due to the uncertainties in the EESI-TOF ion sensitivities, mass concentrations of EESI-TOF SOA-dominated factors were related to the total AMS SOA (i.e. MO-OOA C LO-OOA) by multiple linear regression (MLR). Aromatic SOA was the major SOA component during the daytime, with a 55.2% contribution to total SOA mass (42.4% contribution to total OA). Its contribution to total SOA, however, decreased to 25.4% (7.9% of total OA) during the nighttime. This factor was attributed to the oxidation of light aromatic compounds emitted mostly from traffic. Biogenic SOA accounted for 18.4% of total SOA mass (14.2% of total OA) during the daytime and 36.1% of total SOA mass (11.2% of total OA) during the nighttime. Aged biomass burning and mixed urban SOA accounted for 15.2% and 11.0% of total SOA mass (11.7% and 8.5% of total OA mass), respectively, during the daytime and 15.4% and 22.9% of total SOA mass (4.8% and 7.1% of total OA mass), respectively, during the nighttime. A simple dilution–partitioning model was applied on all EESI-TOF factors to estimate the fraction of observed daytime concentrations resulting from local photochemical production (SOA) or emissions (POA). Aromatic SOA, aged biomass burning, and mixed urban SOA were all found to be dominated by local photochemical production, likely from the oxidation of locally emitted volatile organic compounds (VOCs). In contrast, biogenic SOA was related to the oxidation of diffuse regional emissions of isoprene and monoterpenes. The findings of this study show that in Delhi, the nighttime high concentrations are caused by POA emissions led by traffic and biomass burning and the daytime OA is dominated by SOA, with aromatic SOA accounting for the largest fraction. Because aromatic SOA is possibly more toxic than biogenic SOA and primary OA, its dominance during the daytime suggests an increased OA toxicity and health-related consequences for the general public. This repository has excel file corresponding to each figures presented in main text published version.

opencc-zeroJul 2024View details →

dryad36/100

Biogenic secondary organic aerosol participates in plant interactions and herbivory defense

Biogenic secondary organic aerosols (SOAs) can be formed from the oxidation of plant volatiles in the atmosphere. Herbivore-induced plant volatiles (HIPVs) can elicit plant defenses, but whether such ecological functions persist after they form SOAs was previously unknown. Here we show that Scots pine seedlings damaged by large pine weevils feeding on their roots release HIPVs that trigger defenses in neighboring conspecific plants. Interestingly, the biological activity persisted after HIPVs had been oxidized to form SOAs, which was indicated by receivers displaying enhanced photosynthesis, primed volatile defenses, and reduced weevil damage. The elemental composition and quantity of SOAs likely determines their biological functions. This work demonstrates that plant-derived SOAs can mediate interactions between plants, highlighting its ecological significance in ecosystems.