This dataset contains Volatile Organic Compounds (VOC) concentrations taken from a large, population-scale study, which was conducted for a total of 19 weeks during the winter and summer of 2019. VOC concentration data were collected for 39 VOC species across 60 houses in Ashford, United Kingdom. Samples were collected in evacuated stainless-steel canisters over 72 hours using restricted flow inlets. A number of houses were randomly selected to also collect an outdoor sample. Each household, per campaign, was associated with at least three canister IDs and some with an additional outdoor sample. This dataset contains information on all VOCs collected, listing in which season each sample was taken, the associated canister ID and the analytical instrument with which each VOC was measured. Household, demographic, and product use information is available, as is a logbook outlining further sample information.

Plants use volatile organic compounds (VOCs) to cue natural enemies to their herbivore prey on plants. Simultaneously, herbivores utilize volatile cues to identify appropriate hosts. Despite extensive efforts to understand sources of variation in plant communication by VOCs, we lack an understanding of how ubiquitous belowground mutualists, such as arbuscular mycorrhizal fungi (AMF), influence plant VOC emissions. In a full factorial experiment, we subjected plants of two milkweed (Asclepias) species under three levels of AMF availability to damage by aphids (Aphis nerii). We then measured plant headspace volatiles and chemical defenses (cardenolides) and compared these to VOCs emitted and cardenolides produced by plants without herbivores. We found that AMF have plant species-specific effects on constitutive and aphid-induced VOC emissions. High AMF availability increased emissions of total VOCs, two green leaf volatiles (3-hexenyl acetate and hexyl acetate), and methyl salicylate in A...

This data set reports concentrations of biogenic volatile organic compounds (BVOCs) collected from tethered balloon-sampling platforms above selected forest and pasture sites in the Brazilian Amazon in March 1998, February 1999, and February 2000.

The air samples were collected from forested sites in Brazil: the Tapajos forest (Para) in the Tapajos/Xingu moist forest; Balbina (Amazonas) in the Uatuma moist forest; and Jaru (Rondonia) in the Purus/Madeira moist forest. Two other sites were also located in Rondonia: at a forest reserve (Rebio Jaru) and a pasture (Fazenda Nossa Senhora Aparecida).

The BVOCs measured included isoprene, alpha and beta pinene, camphene, sabinene, myrcene, limonene, and other monoterpenes. Approximately 24 to 40 soundings, including as many as four VOC samples collected simultaneously at various altitudes, were made at each site. There is one comma-delimited data file with this data set.

The Natural Volatile Organic Compounds (NVOC) emissions data sets include isoprene (isop90mn1.1a), terpene (terp90mn1.1a), and other natural volatile organic compounds, with a lifetime of less than one day (nvoc90mn1.1a). All of the data sets are on a monthly basis for the year 1990 on a one degree latitude by 1 degree longitude grid. NVOC emissions include isoprene, monoterpenes, other reactive VOC (ORVOC), and other VOC (OVOC). VOCs are emitted into the atmosphere from natural sources in marine and terrestrial environments. Natural sources of VOC emissions to the atmosphere include marine and fresh water, soil and sediments, microbial decomposition of organic matter, geological hydrocarbon reservoirs, plant foliage and woody material, and enhanced emissions from vegetation during harvesting or burning. NVOCs are important in tropospheric chemistry and in the global carbon cycle. VOC emissions are critical in controlling the OH concentration of the troposphere and so may play a major role in determining the growth rates of atmospheric CH4 (methane) and CO (carbon monoxide) concentrations. Several emissions inventories of VOCs have been published and they indicate that annual natural emissions of isoprene and monoterpenes exceed anthropogenic VOC emissions on a global scale. Each line of data in the GEIA inventory consists of an integer grid number and real specie values for the temporal resolution of the data (one month). Each file contains at most 360 x 180 data lines.

1) ModelWe use the latest Model of Emission of Gases and Aerosols from Nature (MEGANv3.2) to estimate the BVOC emissions from 2001 to 2020 with the input of time-varying satellite-retrieved vegetation and reanalysis meteorology data. Compared to the earlier version MEGANv2.1 (Guenther et al., 2012), MEGANv3.2 estimates vegetation emission factors based on variable plant species measurements instead of on fixed plant functional type (PFT, Guenther et al., 2020). Specifically, MEGANv3.2 uses the so-called Emission Factor Processor (EFP), to estimate the landscape average emission factors, which are based on the following three databases: (1) Growth form datasets for four PFTs: tree, shrub, grass, and crops; (2) Ecotype datasets: composed of a mix of emission-specific tree species/grass associated with specific emission capacities; and (3) Updated tree species/grass datasets corresponding to the biogenic emission classes. These updates can distinguish the differences in vegetation emission factors in regions with the same PFT but with varying plant species. The new version also considers the additional stress factors of emissions by using the simple threshold function, including high/low temperature, and strong wind.2) Model input data (Time-varying vegetation datasets, meteorological datasets, and CO2 concentration)The vegetation parameters driving MEGANv3.2 include LAI (leaf area index), VCF (vegetation cover fraction), and PFT. In this study, the Moderate-resolution Imaging Spectroradiometer (MODIS) vegetation retrievals from 2001 to 2020 were used. LAI data was obtained from Yuan et al. (2011), which improved the MODIS version 6 product MCD15A2H (Myneni et al., 2015) with a temporal resolution of 8 days and a spatial resolution of 0.5°×0.5°. The LAIv calculated in MEGANv3.2 is defined as LAI divided by VCF, representing the leaf area index per unit vegetation area. The VCF was from the yearly MODIS MOD44B version 6 dataset (DiMiceli et al., 2015).The PFT was obtained from the yearly MODIS MCD12C1 product with a spatial resolution of 0.05° (Friedl and Sulla-Menashe, 2015). The selected 17 MODIS IGBP (International Geosphere Biosphere Programme) global vegetation classification types were mapped to four main PFT classification types (i.e., tree, shrub, grass, and crop) in MEGANv3.2 based on methods from Sulla-Menashe and Friedl (2018). The reprocessed datasets were conservatively interpolated to a spatial resolution of 0.5°×0.5° as model inputs.The meteorological parameters driving the MEGANv3.2 model were from the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) (Gelaro et al., 2017). The selected variables used in MEGANv3.2 include 2 m temperature, surface downward shortwave radiation, surface soil moisture, water vapor mixing ratio, 10 m wind speed, precipitation, surface air pressure, low-level wind speed, cloud cover, and snow cover. Photosynthetically active radiation (PAR) in MEGANv3.2 was obtained by dividing the surface downward shortwave radiation by two. The temporal resolution of these variables is either 1-hourly or 3-hourly, and the 3-hourly data are linearly interpolated to the uniform 1-hourly data. All selected parameters were further interpolated from the original 0.5° × 0.625° to a spatial resolution of 0.5° × 0.5° (consistent with the resolution of the vegetation datasets) for driving the MEGANv3.2 model. In our study, MERRA-2 data from 2001 to 2020 were used.In addition, the global annual averaged CO2 concentration data are obtained from https://gml.noaa.gov/ccgg/trends/gl_data.html3) SimulationsTo isolate the contribution of different influencing factors (vegetation, meteorology, and CO2) to BVOC emission trends from 2001 to 2020, we have performed nine sensitivity experiments. These experiments consist of two groups.The first group contains four experiments:EMIT_ALL is the control experiment that considers the historical changes of all factors.EMIT_VEG, EMIT_MET, and EMIT_CO2 consider only the historical changes of vegetation parameters, meteorological factors, and CO2 concentration, respectively, while the other factors are fixed as those in 2001.In the second group, five experiments were conducted to isolate the contributions of individual vegetation parameters (i.e., PFT, LAIv) and meteorological factors (i.e., temperature, light, and soil moisture):For vegetation parameters, the experimental setup is the same as EMIT_VEG but with PFT (EMIT_VEG_FIX_PFT) or LAIv (EMIT_VEG_FIX_LAIv) fixed as that in 2001.For meteorological factors, the experimental setup is the same as EMIT_MET but with temperature (EMIT_MET_FIX_T2m), light (EMIT_MET_FIX_RAD), or soil moisture (EMIT_MET_FIX_SM) fixed as that in 2001.The model horizontal resolution is 0.5° × 0.5°, the temporal resolution is 1 hour, and the simulation period is 2001-2020. The input variables include the satellite-retrieved vegetation parameters and MERRA-2 reanalysis data as described above.4) BVOC emission inventory This BVOC emission data are available as monthly mean emission fluxes as well as monthly averaged daily profiles of emissions.Spatial coverage: Global (latmin:-90 latmax:90 lonmin:-180 lonmax:180)Spatial-resolution: 0.5°x0.5°Temporal coverage: 2001-2020Dimensions and their names: monthly: parameter (20) x month (12) x lat (180) x lon (360)monthly24h: parameter (20) x month (12) x hour (24) x lat (180) x lon (360)Data Format: NetCDFUnits: μg m-2 s-1Biogenic - 20 parameters'ISOP' = isoprene'MBO' = 2-methyl-3-buten-2-ol'MT_PINE' = monoterpenes: pines (alpha and beta)'MT_ACYC' = monoterpenes: acyclic (e.g., myrcene, ocimenes)'MT_CAMP' = monoterpenes: carene, camphene, others'MT_SABI' = monoterpenes: sabinene, limonene, terpinenes, others'MT_AROM' = C10 aromatic: cymenes, cymenenes & C8-C13 oxygenated (e.g., camphor)'MT_OXY' = oxygenated monoterpenes and monoterpenoid-related compounds (e.g., estragole) 'SQT_HR' = highly reactive sesquiterpenes (e.g., caryophyllene)'SQT_LR' = less reactive sesquiterpenes (e.g., longifolene, copaene) and salates'METOH' = methanol'ACTO' = acetone'ETOH' = acetaldehyde and ethanol'ACID' = organic acids: formic acid, acetic acid, pyruvic acid'LVOC' = C2 to C4 HC (e.g., ethene, ethane)'OXPROD' = oxidation products: aldehydes 'STRESS' = stress compounds (e.g., linalool)'OTHER' = other VOC (e.g., indole, pentane, methyl bromide)'CO' = carbon monoxide'NO' = nitric oxideReference:Wang, H., Liu, X., Wu, C., and Lin, G.: Regional to global distributions, trends, and drivers of biogenic volatile organic compound emission from 2001 to 2020, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-1830, 2023.

The National Air Pollution Surveillance (NAPS) program is the main source of ambient air quality data in Canada. The NAPS program, which began in 1969, is now comprised of nearly 260 stations in 150 rural and urban communities reporting to the Canada-Wide Air Quality Database (CWAQD). Managed by Environment and Climate Change Canada (ECCC) in collaboration with provincial, territorial, and regional government networks, the NAPS program forms an integral component of various diverse initiatives; including the Air Quality Health Index (AQHI), Canadian Environmental Sustainability Indicators (CESI), and the US-Canada Air Quality Agreement. Once per year, typically autumn, the Continuous data set for the previous year is reported on ECCC Data Mart. Beginning in March of 2020 the impact of the COVID-19 pandemic on NAPS Operations has resulted in reduced data availability for some sites and parameters. For additional information on NAPS data products contact the NAPS inquiry centre at RNSPA-NAPSINFO@ec.gc.ca Last updated March 2023. Supplemental Information Monitoring Program Overview The NAPS program is comprised of both continuous and (time-) integrated measurements of key air pollutants. Continuous data are collected using gas and particulate monitors, with data reported every hour of the year, and are available as hourly concentrations or annual averages. Integrated samples, collected at select sites, are analyzed at the NAPS laboratory in Ottawa for additional pollutants, and are typically collected for a 24 hour period once every six days, on various sampling media such as filters, canisters, and cartridges. Continuous Monitoring Air pollutants monitored continuously include the following chemical species: • carbon monoxide (CO) • nitrogen dioxide (NO2) • nitric oxide (NO) • nitrogen oxides (NOX) • ozone (O3) • sulphur dioxide (SO2) • particulate matter less than or equal to 2.5 (PM2.5) and 10 micrometres (PM10) Each provincial, territorial, and regional government monitoring network is responsible for collecting continuous data within their jurisdiction and ensuring that the data are quality-assured as specified in the Ambient Air Monitoring and Quality Assurance/Quality Control Guidelines. The hourly air pollutant concentrations are reported as hour-ending averages in local standard time with no adjustment for daylight savings time. These datasets are posted on an annual basis. Integrated Monitoring Categories of chemical species sampled on a time-integrated basis include: • fine (PM2.5) and coarse (PM10-2.5) particulate composition (e.g., metals, ions), and additional detailed chemistry provided through a subset of sites by the NAPS PM2.5 speciation program; • semi-volatile organic compounds (e.g., polycyclic aromatic hydrocarbons such as benzo[a]pyrene); • volatile organic compounds (e. g., benzene) The 24-hour air pollutant samples are collected from midnight to midnight. These datasets are generally posted on a quarterly basis. Data Disclaimer NAPS data products are subject to change on an ongoing basis, and reflect the most up-to-date and accurate information available. New versions of files will replace older ones, while retaining the same location and filename. The ‘Data-Donnees’ directory contains continuous and integrated data sorted by sampling year and then measurement. Pollutants measured, sampling duration and sampling frequency may vary by site location. Additional program details can be found at ‘ProgramInformation-InformationProgramme’ also in the data resources section. Citations National Air Pollution Surveillance Program, (year accessed). Available from the Government of Canada Open Data Portal at open.canada.ca.

The Mediterranean region is recognized as a global biodiversity hotspot. However, over the last decades, the cessation of traditional farming in the north part of Mediterranean basin has given way to strong afforestation leading to occurrence of abandoned agricultural lands colonized by pioneer expansionist species like Pinus halepensis. This pine species is known to synthesize a wide range of secondary metabolites and previous studies have demonstrated strong allelopathic potentialities of its needle and root leachates. Pinus halepensis is also recognized to release significant amounts of volatile organic compounds (VOC) with potential allelopathic effects that has never been investigated. In this context, the objectives of the present study were to improve our knowledge about the VOC released from P. halepensis needles and roots, determine if these VOC affect the seed germination and root growth of two herbaceous target species (Lactuca sativa and Linum strictum), and evaluate if soil microorganisms modulate the potential allelopathic effects of these VOC. Thirty terpenes were detected from both needle and root emissions with β-caryophyllene as the major volatile. Numerous terpenes, such as β-caryophyllene, -terpinene or -pinene showed higher headspace concentrations according to the gradient green needles < senescent needles < needle litter. Seed germination and root growth of the two target species were mainly reduced in presence of P. halepensis VOC. In strong contrast with the trend reported with needle leachates in literature, we observed an increasing inhibitory effect of P. halepensis VOC with the progress of needle physiological stages (i.e. green needle < senescent needle < needle litter). Surprisingly, several inhibitory effects observed on filter paper were also found or even amplified when natural soil was used as a substrate, highlighting that soil microorganisms do not necessarily limit the negative effects of VOC released by P. halepensis on herbaceous target species.

Fusarium circinatum, a fungal pathogen deadly to many Pinus species, can cause significant economic and ecological losses, especially if it were to become more widely established in Europe. Early detection tools with high-throughput capacity can increase our readiness to implement mitigation actions against new incursions. This study sought to develop a disease detection method based on volatile organic compound (VOC) emissions to detect F. circinatum on different Pinus species. VOCs emitted from four different Fusarium species (Fusarium circinatum, Fusarium graminearum, Fusarium bulbicola and Fusarium oxysporum f.sp. pini) grown on Elliott's media agar (in vitro), and three Pinus species (Pinus radiata, Pinus sylvestris, Pinus pinea) inoculated with either i) Fusarium circinatum or ii) mock treatment (in vivo). The four Fusarium species were grown on media and analysed in order to compare their respective VOCs profiles, while the pinus seedlings were analysed in order to determine whether Fusarium circinatum-inoculated seedlings' VOCs profiles could be distinguished from mock inoculated seedlings. The VOCs were sampled using static headspace sampling, enclosing the samples individually in (relatively inert) high-density poly-ethylene bags along with SPME fibers. Divinylbenzene/carboxen/polydimethylsiloxane SPME fibers needle size was 24 ga, 2 cm long and coated with 30 μm (CAR/PDMS layer), 50 μm (DVB layer) (Merck KGaA, Darmstadt, Germany). Immediately after sampling, the SPME fibers were manually injected through an ultra-inert, splitless, straight, 2 mm liner (Agilent, Santa Clara, USA) on a 6890N GC (Agilent Technologies, Santa Clara, USA) coupled with a 5973 MS (Agilent Technologies, Santa Clara, USA). The column was a HP-5ms ultra inert 60m GC column, 0.25 mm, 0.25 µm, 7 inch cage (Agilent, Santa Clara, USA). A C8-C20 hexane mix (Merck KGaA, Darmstadt, Germany). GC-MS was performed through MSD ChemStation version E.02.02.1431 (Agilent Technologies, Santa Clara, USA) with an initial oven temperature of 50°C, followed by an 8°C/min increase to 100°C, subsequently increasing by 4°C/min to 160°C, a final ramp of 16°C/min to 280°C and hold for 2.5 min. GC-MS data were transformed to .cdf files and processed (ADAP chromatogram builder, chromatogram deconvolution, multivariate curve resolution) and aligned (ADAP aligner) with MZMine 2 (v 2.53). Randomforest was applied to see what (if any) compounds could be useful for distinguishing between mock- or F. circinatum-inoculated seedlings (in vivo), or Fusarium species (in vitro). These compounds were tentatively identified by matching mass spectrometry data and back-calculated retention indices with literature values from authentic standards found in Nist20 and Wiley12 MS databases.

The above described pipeline applied here, entailing gas chromatography – mass spectrometry of VOCs, automated data analysis and machine learning, distinguished diseased from healthy seedlings of Pinus sylvestris and Pinus radiata. In P. radiata, this distinction was possible even before the seedlings became visibly symptomatic, suggesting the possibility for this method to identify latently infected, yet healthy looking plants. Pinus pinea, which is known to be relatively resistant to F. circinatum, remained asymptomatic and showed no changes in VOCs over 28 days. In a separate analysis of in vitro VOCs collected from different species of Fusarium, we showed that even closely related Fusarium spp. can be readily distinguished based on their VOC profiles. The results further substantiate the potential for volatilomics to be used for early disease detection and diagnostic recognition.

GC-MS data were collected both in vitro (fungal species grown on identical media) and in vivo (pine seedlings inoculated with Fusarium circinatum or mock). This GC-MS data could then be used to compare what volatile compounds were emitted from each sample and, that way, determine whether these "chemical fingerprints" of volatile compound blends differed between fungal species, or sick and healthy pine seedlings, respectively. Each data file therefore contain all the chemical compounds that can be detected by using our instruments (see general description), their mass spectas, relative abundance and retention times. No sorting of these chemical compounds have been performed, nor any other processing of this raw data for publication.

The dataset includes GC-MS data according to the Mass Spectrometry Development Kit (MSDK) data model in NetCDF format. Files can be read in software that uses MSDK, such as AMDIS or MZMine. See https://msdk.github.io/ for more possibilities.

There are 5 or 6 replicates for each time point and pine species included in the in vivo-analyses. For the in vitro analyses, there are 3 replicates per fusarium species/media blank and time point. All in vivo files are named in the format "#DAABB*" where:

= days post inoculation (7, 14 or 28)

D = Days AA = Pine species (Sy=Pinus sylvestris, Ra=Pinus radiata, Pi=Pinus pinea) BB = Inoculation type (Fc=Fusarium circinatum, Mo=Mock inoculation) * = Replicate number (1-6) Example: 14DPiFc4.CDF = Analysed 14 days post inoculation, Pinus pinea inoculated with F. circinatum, replicate number 4. All in vitro files are named in the format "AAAA#*" except the media blank that is named "emabl#*" where: AAAA = Fusarium species (fcir=Fusarium circinatum, fgra=Fusarium graminearum, foxy=Fusarium oxysporum f.sp. pini, fbul=Fusarium bulbicola)

= days post inoculation (7, 14 eller 21)

• = replicate number (1-3) Example: fcir72.CDF = Fusarium circinatum, Analysed 7 days post inoculation, replicate number 2

Biogenic volatile organic compounds (VOCs) comprise a significant proportion of trace gases in the atmospheric environment and play an important role in the formation of secondary air pollutants. Emissions of monoterpenes from vegetation were studied at adjacent sites in Botswana as part of the SAFARI 2000 (Southern African Regional Science Initiative). Using a LI-COR leaf cuvette, VOC emissions were measured from the dominant tree species (Colophospermum mopane) and other vegetation near Maun, Botswana. The aims of this work were to: (1) determine the VOC emission potential of C. mopane; (2) investigate any differences in VOC emission potential between the tall and short C. mopane morphology types; (3) investigate environmental controls of VOC emissions from C. mopane; and (4) screen other non-dominant vegetation for high VOC emission potential. The data are contained in an ASCII text file (maun_leaf-level_voc.csv) in comma-delimited format with column headers. The data file contains leaf-level VOC emission rates for C. mopane and other plant species growing near Maun, Botswana recorded under different types of experiments associated with the measurements (e.g., preliminary light dependency, emission potential, tall/short and water potential, light dependency, screening). In addition, the data file contains physical measurements, such as leaf area and dry biomass, which are used in calculating leaf-level emissions rates. Environmental parameters in the leaf cuvette (PAR, temperature, relative humidity, and CO2 concentration) are also recorded. All measurements were made during the wet season campaign (February) of 2001.

IntroductionE-cigarettes or electronic nicotine delivery systems (ENDS) have gained popularity especially among young adults and adolescents, even though they are promoted as a safe alternative of traditional cigarettes, studies have found that e-cigarettes generate aerosols that contain harmful components.1–4 Among the complex emission mixtures, volatile organic compounds (VOCs) can be hazardous and may induce short- and/or long-term adverse health effects. Therefore, it is important to understand the VOC emissions from vaping activities, which sets a foundation for assessing the health impacts of ENDS users and bystanders. Chemical Insights, a unit of UL Research Institutes, has conducted a research initiative on characterizing VOC emissions from different types of e-cigarettes. To increase data transparency and share useful information, these research data are made available to stakeholders such as researchers, educators, and general public who may need VOC emission data.MethodsVOC emissions from each puffing activity were evaluated using validated exposure chambers which operated at static status; mainstream emissions from e-cigarettes were generated using a custom-made automatic device that controls the puffing topography.1,5 VOCs were collected on Tenax® TA (60/80 mesh) sorbent tubes and then thermally desorbed and analyzed by thermal desorption-gas chromatography/mass spectrometry (TD-GC/MS) following the US EPA Methods TO-176 and TO-17. Individual VOCs were quantified using multi-point calibration curves with authentic standards if available. Total VOC (TVOC) was the sum of toluene equivalent response in C6 to C16 range. Low-molecular-weight carbonyls (aldehydes) samples were collected on 2,4-dinitrophenylhydrazine (DNPH) cartridges and analyzed using high-performance liquid chromatography (HPLC) following EPA Method TO-11A8. The laboratory quality program enables the accuracy of the identification and quantification of analyzed VOCs and aldehydes. Emission factor of each VOC was calculated using the measurement data and normalized to puff numbers.1,3DatabaseThis database provides VOC emission profiles from popular e-cigarettes that are available in the market, including pod types, mod types, and disposable types, with various e-liquid flavors. This database can be used as generic information to learn the facts of vaping. In addition, this primary emission information can be used for further health-related studies and estimating second-hand exposure. Please see ULRI_ECIG_NOTE file for details of data dictionary.Data portalThe data portal provides an interactive way of viewing and screening data by selecting the parameters of interest. Users can download the data as needed.ReferencesJeon, J.; He, X.; Shinde, A.; Meister, M.; Barnett, L.; Zhang, Q.; Black, M.; Shannahan, J.; Wright, C. The Role of Puff Volume in Vaping Emissions, Inhalation Risks, and Metabolic Perturbations: A Pilot Study. Sci Rep 2024, 14 (1), 18949. https://doi.org/10.1038/s41598-024-69985-1.He, X.; Meister, M.; Jeon, J.; Shinde, A.; Zhang, Q.; Chepaitis, P.; Black, M.; Shannahan, J.; Wright, C. Multi-Omics Assessment of Puff Volume-Mediated Salivary Biomarkers of Metal Exposure and Oxidative Injury Associated with Electronic Nicotine Delivery Systems. Environmental Health Perspectives 2025, 133 (1), 017005. https://doi.org/10.1289/EHP14321.Jeon, J.; Zhang, Q.; Chepaitis, P. S.; Greenwald, R.; Black, M.; Wright, C. Toxicological Assessment of Particulate and Metal Hazards Associated with Vaping Frequency and Device Age. Toxics 2023, 11 (2), 155. https://doi.org/10.3390/toxics11020155.He, X.; Meister, M.; Jeon, J.; Alqahtani, S.; Cushenan, P.; Weaver, S.; Luo, R.; Black, M.; Shannahan, J.; Wright, C. Unveiling Oral Health Impacts of Vaping in African Americans through Untargeted Metabolomics and Proteomics. Environ. Health 2025. https://doi.org/10.1021/envhealth.4c00276.Zhang, Q.; Jeon, J.; Goldsmith, T.; Black, M.; Greenwald, R.; Wright, C. Characterization of an Electronic Nicotine Delivery System (ENDS) Aerosol Generation Platform to Determine Exposure Risks. Toxics 2023, 11 (2), 99. https://doi.org/10.3390/toxics11020099.US EPA. Compendium of Methods for the Determination of Toxic Organic Compounds in Ambient Air Second Edition Compendium Method TO-17 Determination of Volatile Organic Compounds in Ambient Air Using Active Sampling Onto Sorbent Tubes, 1999.US EPA. Compendium of Methods for the Determination of Toxic Organic Compounds in Ambient Air - Second Edition. Compendium Method TO-1 Method for the Determination of Volatile Organic Compounds (VOCs) in Ambient Air Using Tenax® Adsorption and Gas Chromatography/Mass Spectrometry (GC/MS), 1999.US EPA. Compendium of Methods for the Determination of Toxic Organic Compounds in Ambient Air Second Edition Compendium Method TO-11A Determination of Formaldehyde in Ambient Air Using Adsorbent Cartridge Followed by High Performance Liquid Chromatography (HPLC), 1999.

Ambient volatile organic compounds (VOCs) are crucial precursors for the formation of secondary organic aerosol (SOA) and ozone (O3). We have conducted in-situ observations and compiled a comprehensive dataset of ambient volatile organic compound (VOC) compositions and concentrations in Beijing, China, spanning the period from 2016 to 2020. The dataset covers a wide range of VOC species including 29 alkanes, 11 alkenes, 1 alkyne, 16 aromatics, 28 halohydrocarbons, 13 oxygenated VOCs (OVOCs), and 1 nitrogenous VOC (acetonitrile). The presentation and analysis of this dataset is available in a paper submitted to Earth System Science Data (Simon et al, in prep). The findings and analysis of this dataset have been documented in a paper that has been submitted to Earth System Science Data (Liu et al., in prep).

If you use the dataset for related scientific research, please cite the corresponding reference:

Liu et al (in prep); Long-term variations of ambient volatile organic compounds (VOCs) from 2016 to 2020 in Beijing, China; Earth System Science Data.

This dataset contains NSF/NCAR GV HIAPER (Gulfstream-V High-performance Instrumented Airborne Platform for Environmental Research) Trace Organic Gas Analyzer (TOGA) Volatile Organic Compounds (VOC) Analyzer Data collected during the CONvective TRansport of Active Species in the Tropics experiment (CONTRAST) from 11 January through 28 February 2014. This dataset is in ICARTT format. Please see the readme files for details on instruments, parameters, quality assurance, quality control, contact information, and dataset comments.

Non-methane volatile organic compounds (NMVOC) include a large number of chemical species differing for their chemical composition and properties. The disaggregation of total NMVOC emissions into species is thus required to better model ozone and secondary organic aerosols formation. Region- and sector-specific NMVOC speciation profiles are here developed and applied to the EDGARv4.3.2 database, with the same sector resolution as the total NMVOC.

This dataset is associated with a publication currently under peer review (DOI and link to the publication will be updated upon its publication).

Permafrost serves as a significant carbon reservoir, storing up to 1700 petagrams of carbon accumulated over millennia. As global warming accelerates permafrost thaw, this carbon can be mobilized, with a fraction being transformed into volatile organic compounds (VOCs). These VOCs can influence atmospheric oxidizing capacity and contribute to the formation of secondary organic aerosols.

In this study, active layer soils—the seasonally unfrozen layer above the permafrost—were collected from two contrasting Greenlandic permafrost locations (Disko Island, and Kangerlussuaq) and incubated to investigate their role in soil-atmosphere VOC exchange. Laboratory incubations were conducted under controlled conditions, where a VOC mixture gas was continuously purged through jars containing the soil samples. Gas concentrations were monitored at the inlet and outlet using a PTR-ToF-MS, allowing for the estimation of VOC uptake rates based on the differences in VOC concentrations.

The results demonstrated that these soils actively function as VOC sinks, despite variations in their physicochemical properties. Soils from upper active layers showed relatively higher uptake capacities, with soil moisture, organic matter, and microbial carbon content identified as key factors influencing uptake rates. Additionally, uptake coefficients for several major VOC species were calculated, providing valuable data for future model development. Correlation analysis and varying uptake coefficients suggest that the sink is likely biotic, with selective preferences for different VOCs. The findings indicate that the development of a deeper active layer under climate change could enhance the soil’s sink capacity and mitigate net VOC emissions from permafrost thaw.

Detailed methods and interpretations of the results can be found in the associated publication.

TOGA VOC data measured with TOGA-TOF (Trace Organic Gas Analyzer with Time-of-Flight Mass Spectrometer). These data were collected on board the NSF/NCAR GV HIAPER aircraft during the TI3GER (Technological Innovation Into Iodine and GV Environmental Research) field project from 2 through 27 April 2022. This data set is in ICARTT format. Please see the header portion of the data files for details on instruments, parameters, quality assurance, quality control, contact information, and data set comments.

In the fig (Moraceae) and fig‐wasp (Agaonidae) mutualism, scent is believed to be of primary importance in pollinator attraction and maintenance of species specificity. Scent divergence between closely related Ficus species seems sufficient in promoting reproductive isolation through pollinator behaviour, starting the process of speciation. We investigated volatile organic compound (VOC) variation from figs in several Ficus species endemic to Papua New Guinea. Sister species of section Papuacyse and subspecies of Ficus trichocerasa substitute each other along the continuously forested Mt. Wilhelm elevational gradient. We placed these species in a phylogenetic context to draw conclusions of scent divergence between close relatives. In addition, pollinator response to VOCs emitted by figs of different species was tested. Volatile profiles differed significantly between focal species, although with a varying degree of overlap between (sub)species and elevations. Pollinators were generally attracted to VOCs emitted only by their hosts except in one case where pollinating fig wasps were also attracted to the sister species of its host. Wasp morphological traits, however, indicate that it is mechanically impossible for this species to oviposit in figs of this atypical encounter. Synthesis. This study demonstrates that while scent is an effective signal for partner recognition, there are multiple barriers which help maintain prepollination isolation in fig and pollinating fig‐wasp interactions. Speciation along this elevational gradient is reinforced by divergence in key reproductive isolation mechanisms on both sides of the mutualism.

This contains gridded non-methane volatile organic compound (NMVOC) emission inventories for India derived as part of burning studies performed during the APHH-INDIA campaign. For data files with more than 1 million rows, NASA AMES metadata headers have been provided as a separate document, which has the identical name of the data it applies to but also includes _metadata. For years 1993, 1994, 1999, 2002, 2005, 2006, 2007, 2010, 2011 and 2016 inventories have been produced in terms of total NMVOC emission from each source sector (kg/km2). There are also two upper limit scenarios of emissions from cow dung cake combustion based on data from PPAC and PPAC supplemented with additional cow dung cake consumption for states now covered by this survey. The speciation factors of NMVOCs released from particular sources are also provided so that these years can be speciated by source simply by multiplying the total emission from each source by the ratio of species released from the source. This allows future users to produce speciated emission inventories for years other than 2011 if they need. Gridded inventories are also provided for emissions of 21 polycyclic aromatic hydrocarbons for the year 2011 from fuelwood, cow dung cake, charcoal, liquefied petroleum gas and municipal solid waste. These are provided as total PAH emissions from a source with speciation factors also provided to allow speciation should it be required by multiplying the total NMVOC emission from a source by the speciation factors from that source. Gridded inventories are provided for crop residue burning at 1km2 and 10km2. These were calculated with total agricultural area identified in a state from either NASA MODIS (1 km2) or Ramankutty et al. (2008) (10 km2). A second inventory was produced at 10km2 as it was felt that the NASA data offered little variation within respective states. These have been split into total emissions from each of the 5 emission factors applied, RiceEFyearlyVOCKG (for rice), WheatEFyearlyVOCKG (for wheat, coarse cereal and maize), JowarEFyearlyVOCKG (for Jowar and Bajra), MeanEFyearlyVOCKG (for 9 oilseeds, groundnut, rapeseed, mustard, sunflower, cotton, jute and mesta) and SugarcaneEFyearlyVOCKG (for sugarcane). The inventories were produced using emission factors developed as part of the APHH-INDIA project as well as from a different publication focussed on the burning of crops. The inventories have been developed in the following manner. The emission factors used in this study come from a variety of recently published sources. All emission factors applied in this study included measurement by PTR-ToF-MS, a technique well suited to species released in significant quantities from solid fuel combustion such as small oxygenated species, phenolics and furanics. These species are often missed by GC measurement alone. Preference has been given to emission factors from studies which: (1) have many measurements (n), (2) use samples collected from India or (3) use samples collected from similar countries. Fully speciated emission factors are available from the references given. For residential fuel combustion, the emission factors measured by Stewart et al. (2021a) were used and were developed from 76 combustion experiments of fuel wood, cow dung cake, LPG and MSW samples collected from around Delhi. This study was extremely detailed and measured online, gas-phase, speciated NMVOC emission factors for up to 192 chemical species using dual-channel gas chromatography with flame ionisation detection (DC-GC-FID, n = 51), two-dimensional gas chromatography (GC×GC-FID, n = 74), proton-transfer-reaction time-of-flight mass spectrometry (PTR-ToF-MS, n = 75) and solid-phase extraction two-dimensional gas chromatography with time-of-flight mass spectrometry (SPE-GC×GC-ToF-MS, n = 28). Comparison of these emission factors to those obtained in similar studies is provided in Stewart et al. (2021a). The emission factors used as part of this study are larger than those measured by Stockwell et al. (2016), Fleming et al. (2018) and several other studies which were based on gas chromatography techniques alone. The emission factors here measure many more NMVOC species, use techniques which target a range of species which more traditional GC analyses do not detect and make online measurements which minimise loss of intermediate-volatility and semi-volatile organic species, which may be lost through the collection of whole air samples, but have been shown to represent a large proportion of total emissions from biomass burning (Stockwell et al., 2015). Emission factors for combustion of crop residues on fields were taken from measurements by Stockwell et al. (2015) made using PTR-ToF-MS of 115 NMVOCs (Stockwell et al., 2015) for wheat straw (n = 6), sugarcane (n=2), rice straw (n=7) and millet (n=2). This study also included the mean crop residue emission factor for 19 food crops, for use when no current emission factor had been comprehensively measured using PTR-ToF-MS. The emission factor applied (38.8 g kg-1) was evaluated against that for crop residues used for domestic combustion in Delhi (37.9 g kg-1). Whilst the values measured by Stockwell et al. (2015) and Stewart et al. (2021a) were comparable, the value from Stockwell et al. (2015) was used as the crop types were more reflective of the crop residues burnt on fields after harvest, compared to those burnt to meet residential energy requirements. The mean emission factor for crop residue combustion on fields was used for specific crop types with smaller levels of cultivation. Emissions from coal burning were estimated using a mean emission factor from the combustion of bituminous coal from China (n = 14), a neighbouring Asian country, made using PTR-ToF-MS. Whilst the chemical composition of the coal may be more important than the development status of the country, there was overall a low level of reported residential coal use and this estimate was included for completeness. A total of 89 NMVOCs were identified, which represented 90-96% of the total mass spectra (Cai et al., 2019). Indian specific PAH emission factors were recently measured in gas- and particle-phases using PTR-ToF-MS and GC×GC-ToF-MS (Stewart et al., 2021). This dataset provided PAH emission factors collected from combustion of fuel wood (n = 16), cow dung cake (n = 3), crop residue from domestic combustion (n = 3), MSW (n = 3), LPG (n = 1) and charcoal (n = 1) samples. High resolution, gridded population data for India (WorldPop, 2017) was used at a resolution of 1 km2. Officially, urban populations in India are defined as having a population density > 400 people km-2, 75% of men employed in non-agricultural industries and a population of town > 5000 people. Rural populations in India cannot be identified simply by having a population density of < 400 people km-2, as some states such as Uttar Pradesh have an average population density of around 800 people km-2. Rural grid squares were therefore identified by calculating the population density threshold in each state in which the sum of the 1km2 grid squares below this threshold correctly reproduced the rural populations in these states from the 2001 and 2011 censuses (Government of India, 2014). A small uncertainty existed over the exact population of India and we used population statics indicated by the 2011 census. NMVOC and PAH emissions from domestic solid fuel combustion were plotted against this high-resolution population data in the R statistical programming language at 1 km2 for 2001 and 2011, with the population datasets scaled to the percentage changes in Indian population indicated by the World Bank for additional years of interest. Preference was given to large fuel usage surveys which included tens to hundreds of thousands of respondents. The Household Consumption of Goods and Services in India survey by the National Sample Survey Office (NSSO, 2007a, 2012a, 2014) gave state-wise kg capita-1 fuel wood, LPG, charcoal and coal burning statistics for rural and urban environments and was used for the years 2004-2005, 2009-2010 and 2011-2012. NMVOC emissions for these years were calculated by multiplying the NMVOC emission factor for the fuel, by the yearly fuel consumption per capita by the population of the grid cell. Data were collected from additional large surveys previously conducted. These surveys collected data in terms of the number of households using specific fuels per 1000 households in different Indian states in rural and urban environments. The Fifth Quinquennial Survey on Consumer Expenditure provided data for 1993-1994 (NSSO, 1997), the Energy Sources of Indian Households for Cooking and Lighting provided data for years 2004-2005, 2009-2010 and 2010-2011 (NSSO, 2007b, 2012b, 2015) and the Household Consumer Expenditure and Employment-Unemployment Situation in India for 2002 and 2006-2007 (NSSO, 2003, 2008). The National Family Health Survey presented India-wide fuel use as a percentage of the population. To reflect spatial variation in fuel use, the raw data from these surveys were accessed (from the DHS Programme, U.S. Agency for International Development), extracted through the SPSS statistics software package and processed in the R programming language. This increased fuel usage data availability as the number of households per 1000 households using specific fuels in Indian states and covered the years 1992-1993, 1998-1999, 2005-2006 and 2015-2016 (International Institute for Population Sciences, 1995, 2000, 2007, 2017). These were extensive datasets with 1992-1993, 1998-1999 and 2005-2006 surveying just under 100,000 households and 2015-2016 around 600,000 households. To allow the incorporation of data from years which were based on the number of households using a particular fuel per 1000 households (1993, 1994, 1999, 2002, 2006, 2007 and 2016), a scaling factor was developed. The scaling factor was based on the ratio of fuel use in the

IntroductionWood panels, commonly used in cold climates like the Nordic countries for their aesthetic surface and availability, emit volatile organic compounds (VOCs) that can impact indoor air quality and may contribute to health risks, especially with repeated or prolonged exposure. While research has primarily focused on untreated fresh wood, little attention has been given to the VOC emissions from commercial wood panels. This study aims to investigate the VOC emission pattern, intensity, and profile of nine commercially untreated and treated wood panels commonly used in indoor environments, focusing on how wood type and surface treatments influence emission characteristics.MethodsThe study utilizes Proton Transfer Reaction Time-of-Flight Mass Spectrometry combined with passive sampling, offering a more comprehensive analysis of volatile organic compounds, including both volatile and very volatile compounds, which traditional gas chromatography cannot capture. Advanced statistical methods, such as Bayesian posterior, principal component analysis, and hierarchical clustering analysis, were employed to identify key emission contributors and classify emission patterns.ResultsThe findings reveal that emission intensity and profiles are influenced by wood type and surface treatments. Pine and oak emitted higher proportions of VOCs, while spruce primarily emitted VVOCs. Glazing, staining, and painting significantly affect emission intensity, with glazing reducing pine total emissions by 81% and increasing them in spruce by 65%. Staining pine reduced VOC emissions by 74% but increased VVOC emissions by 63%, shifting the emission profile. Despite high emission intensity from untreated pine, painting reduced TVOC emissions by 93%, aligning its profile with lower-emission woods like aspen and spruce, making it more suitable for indoor use.DiscussionThe right treatment can transform high-emission woods into materials resembling low-emission species, offering a practical means to mitigate indoor VOC loads.

Here, we conducted in situ measurements of soil and pond VOC emissions across an actively degrading permafrost peatland in subarctic Norway. We used a permafrost thaw gradient that covered bare soil and vegetated palsa plateaus, underlain by intact permafrost, and increasingly degraded permafrost landscapes: thaw slumps, thaw ponds, and vegetated thaw ponds.

This dataset includes two excel files: 1) the first one "Finnmark_source_data" is the source data for figures in the publication https://doi.org/10.1016/j.geoderma.2023.116355. ii) the second one "Rawdata_of_emission_rate" is the emission rate of the 210 VOC species identified in this study.

Results showed that every peatland landscape type was an important and consistent source of atmospheric VOCs, with a large variety species, such as methanol, acetone, monoterpenes, sesquiterpenes, isoprene, hydrocarbons, oxygenated VOCs, etc. VOC composition varied considerably across the measurement period and across the permafrost thaw gradient. We observed enhanced terpenoid emissions following thaw slump degradation, highlighting the potential atmospheric impact of permafrost thaw, due to the high chemical reactivities of terpenoid compounds. Overall, our study demonstrates that VOCs are being emitted in significant quantities and with largely similar composition upon permafrost thawing, inundation, and subsequent vegetation development, despite major differences in microclimate, hydrological regime, vegetation, and permafrost occurrence.

Should you have any questions regarding the dataset, please free feel to contact Yi jiao at yi.jiao@bio.ku.dk or the PI of this project Prof. Rinnan at riikkar@bio.ku.dk

IntroductionVolatile organic compounds (VOCs) are compounds that have a high vapor pressure and low water solubility, which are emitted from solid or liquid sources. Furniture and building materials are a major source of indoor VOCs, which contribute to the background VOC levels at normal room conditions, and occupants are exposed to these VOCs when indoors. Among the various VOCs, some of them may have short- and/or long-term adverse health effects. Therefore, it is important to understand the VOC emissions from common indoor sources. However, accurate monitoring of VOCs is not available for many researchers and the public. Chemical Insights, a unit of UL Research Institutes, has conducted a research initiative on characterizing VOCs emissions from upholstered furniture during normal use. To increase data transparency and share useful information, these research data are made available to stakeholders such as researchers, educators, and general public who may need indoor VOC source data.MethodsFour types of upholstered chairs with different fire-resistant technologies were selected. Emissions from chairs were characterized using validated exposure chamber; an agitation robot was used to mimic a person sitting on the chair.1,2 VOCs were collected on Tenax® TA (60/80 mesh) sorbent tubes and then thermally desorbed and analyzed by thermal desorption-gas chromatography/mass spectrometry (TD-GC/MS) following the US EPA Methods TO-173 and TO-14. Individual VOCs were quantified using multi-point calibration curves with authentic standards if available. Total VOC (TVOC) was the sum of toluene equivalent response in C6 to C16 range. Low-molecular-weight carbonyls (aldehydes) samples were collected on 2,4-dinitrophenylhydrazine (DNPH) cartridges and analyzed using high-performance liquid chromatography (HPLC) following EPA Method TO-11A5. The laboratory quality program enables the accuracy of the identification and quantification of analyzed VOCs and aldehydes. Emission rate of each detected VOC was calculated in unit of µg/h. Details of sampling and analysis methods can be found in peer-reviewed publications.1,2DatabaseThis database includes VOC emissions from upholstered chairs and cushions using different flame retardant technologies that are typically found in residential and commercial buildings. This data provides VOC emission information from different upholstered chair types. Homeowners, builders, designers and facility managers can gain knowledge on VOC emission levels from these furniture sources, which will further guide material selection, planning and design. Please see ULRI_CHAIR_NOTE file for details of data dictionary.Data portalThe data portal provides an interactive way of viewing and screening data by selecting the parameters of interest. Users can download the data as needed.ReferencesDavis, A.; Ryan, P. B.; Cohen, J. A.; Harris, D.; Black, M. Chemical Exposures from Upholstered Furniture with Various Flame Retardant Technologies. Indoor Air 2021, 31 (5), 1473–1483. https://doi.org/10.1111/ina.12805.Harris, D.; Davis, A.; Ryan, P. B.; Cohen, J.; Gandhi, P.; Dubiel, D.; Black, M. Chemical Exposure and Flammability Risks of Upholstered Furniture. Fire and Materials 2021, 45 (1), 167–180. https://doi.org/10.1002/fam.2907.US EPA. Compendium of Methods for the Determination of Toxic Organic Compounds in Ambient Air Second Edition Compendium Method TO-17 Determination of Volatile Organic Compounds in Ambient Air Using Active Sampling Onto Sorbent Tubes, 1999.US EPA. Compendium of Methods for the Determination of Toxic Organic Compounds in Ambient Air - Second Edition. Compendium Method TO-1 Method for the Determination of Volatile Organic Compounds (VOCs) in Ambient Air Using Tenax® Adsorption and Gas Chromatography/Mass Spectrometry (GC/MS), 1999.US EPA. Compendium of Methods for the Determination of Toxic Organic Compounds in Ambient Air Second Edition Compendium Method TO-11A Determination of Formaldehyde in Ambient Air Using Adsorbent Cartridge Followed by High Performance Liquid Chromatography (HPLC), 1999.