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2,518 results for “Smoking”
Data from: Wildfire smoke impacts the body condition and capture rates of birds in California
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Evidence for multi-decadal fuel buildup in a large California wildfire from smoke radiocarbon measurements
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Lake exposure to wildfire smoke in North America, 2019-2021
Wildfire activity is increasing globally. The resulting smoke plumes can travel hundreds to thousands of kilometers, reflecting or scattering sunlight and depositing ash within ecosystems. Several key physical, chemical, and biological processes in lakes are controlled by factors affected by smoke. The spatial and temporal scales of lake exposure to smoke are extensive and underrecognized. We introduce the concept of the lake smoke-day, or the number of days any given lake is exposed to smoke in any given fire season, and quantify the total lake smoke-day exposure in North America from 2019-2021. Because smoke can be transported at continental to intercontinental scales, even regions that may not typically experience direct burning of landscapes by wildfire are at risk of smoke exposure. We found that 99.3% of North America was covered by smoke, affecting a total of 1,333,687 lakes >=10 ha. An incredible 98.9% of lakes experienced at least 10 smoke-days a year, with 89.6% of lakes receiving over 30 lake smoke-days, and lakes in some regions experiencing up to 4 months of cumulative smoke-days. Herein we review the mechanisms through which smoke and ash can affect lakes by altering the amount and spectral composition of incoming solar radiation and depositing carbon, nutrients, or toxic compounds that could alter chemical conditions and impact biota. We develop a conceptual framework that synthesizes known and theoretical impacts of smoke on lakes to guide future research. Finally, we identify emerging research priorities that can help us better understand how lakes will be affected by smoke as wildfire activity increases due to climate change and other anthropogenic activities.
ECMWF data for analysing smoked-charged vortices after the 2019-2020 Australian wildfires
<p>The dataset contains GRIB2 files produced from the operational IFS model and assimilation system of the European Centre for Medium Range Weather Forecast (ECMWF).</p> <p>OPZLWDA2020mmdd-SH.grd files contain log of surface pressure, zonal wind, meridional wind, temperature, relative vorticity and ozone mixing ratio for the 137 levels of the model on a 1°x1° grid in the southern hemisphere for the long window analysis at 6UTC and 18UTC every day from 1st January 2020 to 31 March 2020.</p> <p>OPZFCST2020mmdd-SH.grd files contain log of surface pressure, temperature, relative vorticity and ozone mixing ratio for the 137 levels of the model on a 1°x1° grid in the southern hemisphere every day for the 10-day forecast run starting at 00UTC every four days from 7 January 2020 to 31 March 2020.</p> <p>For basic access, these files are readable using python tools. The package pygrib available under conda-forge is recommended. The eccode library that allows read from C or Fortran is freely available from ECMWF and is installed along pygrib. Reading with eccode library is also possible in C and Fortran.</p> <p>A simple reader using pygrib is provided. </p> <p>Dedicated packages for the project are available on github/bernard-legras/STC/STC-Australia with dependencies in github/bernard-legras/STC/STC/pylib. The package that reads and process ECMWF data is github/bernard-legras/STC/STC/pylib/ECMWF_N.py. This package needs setup modification to discover the files where they have been copied.</p> <p>All requirements should be made to bernard.legras@lmd.ipsl.fr</p>
Fire-driven behavioral response to smoke in a Mediterranean lizard
<p>The evolutionary role of fire in animals has been poorly explored. Reptiles use sensory cues such as smell (chemoreception) to detect threats and flee. In Mediterranean ecosystems, fire is a threat faced by reptiles. We hypothesized that the Mediterranean lizard <i>Psammodromus algirus</i> recognizes the threat of fire by detecting the smoke, which triggers a behavioral response that enhances survival in fire-prone ecosystems. We predicted that lizards from fire-prone ecosystems will be more sensitive to fire stimulus than those from ecosystems that rarely burn. We conducted a terrarium experiment in which lizards from habitats with contrasted fire regimes (fire-prone vs. non-fire-prone) were exposed to smoke vs. control (false smoke) treatment. We found that in populations from fire-prone habitats, more lizards reacted to smoke, and their behavioral response was more intense than in lizard populations from non-fire-prone habitats. Our results suggest that an enhanced response to smoke may be adaptive in lizards from fire-prone ecosystems as it increases the chance for survival. We provide evidence that fire is likely an evolutionary driver shaping behavioral traits in lizard populations exposed to frequent wildfires. Understanding ecological and evolutionary processes shaping animal populations is relevant for species conservation in a changing fire regime world.</p>
Smoke sauna, Helsinki, Finland
Small old smoke sauna in courtyard of 18th century [Pakin talo](https://www.youtube.com/watch?v=Pgt1qX6PRY4) in Itä-Pakila, Helsinki, Finland. More info: * [Location in Google Maps](https://goo.gl/maps/nTf4aucG3fuKmG5p6) * [Wikipedia: Smoke sauna](https://en.wikipedia.org/wiki/Finnish_sauna#Smoke_sauna) * [Finnish Heritage Agency site info](https://www.kyppi.fi/to.aspx?id=112.1000001709) (only in finnish) Source: Objaverse 1.0 / Sketchfab
I.G. Prence Smoking Pipe
This clay smoking pipe was found during the 2010 archaeological field school. The stem is decorated by panels with a maker's mark, and place of manufacture (on the opposite side of thestem from the maker's mark), each within deep, rouletted lines, and finally book-ended by two rows of oak leaves in relief. The bowl also has floral designs, likely oak leaves as well. The mark reads "I.G. PRENCE" with the place of manufacture reading "IN GOTHA". I.G. PRENCE is likely a misspelling of I.G. Prince, a major pipe producer in 19th century Netherlands. The misspelling as well as the place of manufacture as Gotha, this pipe is likely an imitation or a knock-off. The overall style of the stem is just like pipes produced by the French company Peter Dorni. A pipe stem fragment with the same oak leaf motifs was found elswhere on campus and dates to no later than 1876, indicating that the Prence pipe was created around the same time. Photogrammetry model by Jack A. Biggs with Agisoft Metashape. Source: Objaverse 1.0 / Sketchfab
Single-piece smoking pipe
ID no.: MAK/S/201/1 Archaeological Museum in Kraków https://muzea.malopolska.pl/en/objects-list/1565 Digitalisation: RDW MIC, Virtual Małopolska project Source: Objaverse 1.0 / Sketchfab
Composite smoking pipe – "lulka"
ID no.: MAK/S/56/1 Archaeological Museum in Kraków https://muzea.malopolska.pl/en/objects-list/1566 Digitalisation: RDW MIC, Virtual Małopolska project Source: Objaverse 1.0 / Sketchfab
Fire-D: Analysis and ML-Ready NASA-Centric Remote Sensing of Wildfire and Smoke
<p>Earth science remote sensing imagery is rich in structural and spectral information, making such data an ideal platform for benchmarking for a broad range of machine learning (ML) tasks, from pattern retrieval to physics-informed classification to anomaly detection to transfer learning. Nevertheless, the utility of Earth science remote sensing data remains largely unexplored by the broader ML community. Our goal is to bridge this gap and bring a rich variety of multisource multi-resolution Earth image data to a wider range of ML researchers who are non-experts in remote sensing, thereby increasing the utility and societal impact of such data products. In particular, motivated by the emerging wildfire crisis, we present radiometrically and geometrically calibrated radiance data from airborne and orbital instruments from the National Aeronautics and Space Administration (NASA), the National Oceanic and Atmospheric Administration (NOAA), and the Korean Meteorological Administration (KMA).</p> <p>Given the scarce occurrence of wildfires and complex spatio-temporal dependencies in radiance data, these datasets are especially well suited for benchmarking unsupervised and self-supervised learning tasks both on images and non-Euclidean objects. Our experiments on these datasets indicate that contrastive learning and transfer learning algorithms can capture the structures of views and scenes, map pixel space of multi-sensor imagery to a high-level embedding space for further downstream tasks, and facilitate more cohesive integration of the state-of-the-art ML approaches into wildfire risk analytics.</p> <p>All NASA-based observations are freely usable under the <a href="https://science.data.nasa.gov/license/">Creative Commons Zero License</a>.There are also no restrictions on the use of <a href="https://registry.opendata.aws/noaa-goes/">GOES Data</a>. <a href="https://registry.opendata.aws/noaa-gk2a-pds/">GK2A data</a> are also open data without any restrictions on its use.<br><br>For the Planet data, we cannot not share the Radiances, but all masks within this dataset are freely usable with no restrictions.</p> <p> </p> <p>Use:</p> <p>On the data input, input geometrically and radiometrically calibrated radiance data has been pulled from various NASA, NOAA, Planet, and KMA archives. For instruments that have multiple different spatial resolutions within their spectral bands (GOES and GK2A), all bands have been resampled to the lowest collective spatial resolution.</p> <p>Geometric and radiometric calibration has been done by the science data processing pipelines of the various missions, and would not need to be done by anyone else looking to curate the same data. Further information for each instrument can be found in each of the publicly available Level-1 algorithm theoretical basis documents (ATBDs)</p> <p>All input and label data have been put in GeoTiff format. Each band is in a separate raster band and each scene is in a separate GeoTiff file. Label files and input files are in separate tar files, labeled respectively, and the file names match for input and labels, with the exception of an additional .fire and .smoke in the respective label filenames and subfolders.<br><br>The <a href="https://www.earthdata.nasa.gov/about/esdis/esco/standards-practices/geotiff">GeoTiff</a> data format natively contains geolocation metadata internally, and can be interfaced with via C/C++/Python <a href="https://gdal.org/en/stable">GDAL</a> packages, or other python packages that wrap GDAL, like <a href="https://rasterio.readthedocs.io/en/stable/">rasterio</a> and <a href="https://corteva.github.io/rioxarray/stable/">rioxarray</a> . The documentation for <a href="https://nicks-personal-organization-2.gitbook.io/sit-fuse">SIT-FUSE</a> , the package with which the labels were generated, also has examples on how to read and interface with various data formats, including GeoTiffs. Lastly, this data can be interfaced with using Geographic Information Systems (GIS), like the free and open-source <a href="https://qgis.org/">QGIS</a>.</p> <p>An example of programmatic data access and usage can be found in the dataset's associated <a href="https://github.com/Fire-D-Dataset/FIRE-D">GitHub repository</a>. </p> <p>A working example using data from this repository for ML tasks is available <a href="https://drive.google.com/drive/folders/16aJO6LhrxJ3gsWoTU9BNN3hsb8W0refG?usp=sharing">here</a>.</p> <p>Timing information can be found in the file names, which all use the standard formats from the various instruments' L1B datasets.</p> <p>V2 includes additional GOES-18 radiance data and associated smoke and fire labels for the recent LA fires (Palisades and Eaton fires in January of 2025).</p> <p>V3 provides a reorganization of all data, and an inclusion of improved and additional data from airborne and satellite platforms in 2019, associated with this study: https://arxiv.org/pdf/2501.15343 . </p> <p>V4 provides additional AVIRIS-C Radiances and fixes the spatial range of the GOES-17 radiances to match that of the associated labels. The AVIRIS-C radiances are split across 5 tar files, ordered temporally - all associated labels are in a single tar file.</p> <p><br>Current fire coverage includes:</p> <ul> <li>2019: Williams Flats, Sheridan, Horsefly, and Mosquito (US)</li> <li>2022: Uljin Forest Fire (S. Korea; largest fire on record in S. Korea)</li> <li>2025: Palisades and Eaton Fires (US)</li> </ul> <p>Additional data for the 2025 Palisades and Eaton fires from the TEMPO instrument is currently being validated and will be released in a V4 shortly.</p> <p>Croissant file for dataset metadata specification is also included</p> <p>Validation:</p> <p>These labels have been extensively validated and further information can be referenced in associated publications:<br><a href="https://doi.org/10.3390/rs13122364">https://doi.org/10.3390/rs13122364</a><br><a href="https://doi.org/10.3390/rs17071267">https://doi.org/10.3390/rs17071267</a></p> <p> </p> <p> </p>
Old Hookah (Smoking Pipe)
hookah is a single- or multi-stemmed instrument for vaporizing and smoking flavored cannabis, tobacco or sometimes opium, whose vapor or smoke is passed through a water basin. I make this model for a animation cartoon, which name was 'Bangali Hasir Golpo'. I use for this Autodesk Maya and substance printer. Source: Objaverse 1.0 / Sketchfab
Wildfire, prescribed burn, and agricultural burn smoke PM2.5 estimates for CA, WA, and OR 2014-2020
<p>Wildfires, prescribed burns, and agricultural burns all impact ambient air quality across the Western U.S.; however, little is known about how communities across the region are differentially exposed to smoke from each of these fire types. To address this gap, we quantify smoke exposure stemming from wildfire, prescribed, and agricultural burns across Washington, Oregon, and California from 2014-2020 using a fire type-specific biomass burning emissions inventory and the GEOS-Chem chemical transport model. We examine fire type-specific PM<sub>2.5</sub> concentration by race/ethnicity, socioeconomic status, and in relation to the Center for Disease Control's Social Vulnerability Index. Overall, population average PM<sub>2.5</sub> concentrations are greater from wildfires than from prescribed and agricultural burns. While we found limited evidence of exposure disparities among sub-groups across the full study area, we did observe disproportionately higher exposures to wildfire-specific PM<sub>2.5</sub> exposures among Native communities in all three states and, in California, higher agricultural burn-specific PM<sub>2.5</sub> exposures among lower socioeconomic groups. We also identified, for all three states, areas of significant spatial clustering of smoke exposures from all fire types and increased social vulnerability. These results provide a first look at the differential contributions of smoke from wildfires, prescribed burns, and agricultural burns to PM<sub>2.5</sub> exposures among demographic subgroups, which can be used to inform more tailored exposure reduction strategies across sources.</p>
Datasets for The Dynamics of Megafire Smoke Plumes in Climate Models: Why a Converged Solution Matters for Physical Interpretations
<p>The data in this repository contains the information needed to reproduce the core results of the paper, "The Dynamics of Megafire Smoke Plumes in Climate Models: Why a Converged Solution Matters for Physical Interpretations" submitted to the Journal of Advances in Modeling Earth Systems (JAMES) on 10/3/2022.</p> <p> </p> <p>The "intsmoke*.txt" files are text files with the globally integrated smoke mass above 150 hPa for all simulations in the paper. A header is provided in each file.</p> <p>The "spectra*.mat" files are matlab files that contain fields used to plot the kinetic energy spectra for all simulations. The variable "kes" is the kinetic energy as a function of wavelength, "wvl" are the associated wavelengths of "kes". The variables "hcut" and "lcut" represent the indices of the data "kes" and "wvl" that are used to make the kinetic energy spectra plots in the paper.</p> <p>The "budget*.nc" files contain the budget terms for the relative, vertical vorticity evolution equation at the appropriate date/time. The fields in the netcdf file are self-describing. These budget terms are for the "optimal simulation" described in the paper.</p> <p>The "vort*.nc" file contains the vorticity fields just before the plume forcing starts based on the date/time on the file.</p>
Smoke responsiveness of peatland bryophyte spores
<p class="MsoNormal"><span>Northern peatlands are globally important carbon stores, but with increasing fire frequency, the re-establishment of bryophytes (notably <em>Sphagnum</em>) becomes crucial for their carbon sequestration. Smoke-responsive germination is a common trait in seeds in fire-prone ecosystems but has not been demonstrated in bryophyte spores. To investigate the potential role of smoke in post-fire peatland recovery, we tested the germination of spores of fifteen bryophyte species after treatment with smoke-water. Comparison of smoke responsiveness between spores of different laboratory storage times and burial depths/age (3-200 yrs old) were subsequently tested. Smoke increased germination percentage for 10 of the 15 study species, and increased germination speed for four of these. Smoke responsiveness increased along the fire frequency gradient from open expanse to forest margin, consistent with the theory that this selects for the maintenance of fire-adapted traits. Smoke enhanced germinability of 1-yr but not 4-yr laboratory-stored spores. Smoke, however, considerably increased germinability of spores naturally buried in peat for decades and up to <em>c</em>. 200 yrs. The effect of fire may be overlooked in non-fire-prone ecosystems, e.g. those in which wetland bryophytes dominate. Hence, an expected increase in fire frequency may lead to shifts in species dominance, for which our study provides one mechanism, which may affect long-term carbon sequestration in peatlands.</span></p>
The Smoking Room
**Welcome to [The Hallwyl Museum](https://goo.gl/qbZSY4) in Stockholm, Sweden.** Once the home of [Walther](https://goo.gl/79nUeT) and [Wilhelmina von Hallwyl](https://goo.gl/Y8iiJs). On festive occasions the gentlemen sat here after dinner, smoking cigars and talking. On regular days, however, this was the family sitting room, where they gathered to drink coffee and play board games in the evenings. The smoking room is inspired by the [Orient](https://goo.gl/pDNk9v). It was common in the late 19th century to decorate with oriental features and the room is typical for the ideals of the time. The style is associated with [colonialism](https://goo.gl/epxUNw) and militarism and was therefore considered to be appropriate for a room for gentlemen. The walls are clad in silk which was meant to give the room a feeling of an officer's tent.  The images for this project were captured using an [array](https://goo.gl/ePHjDr) of four Panasonic Lumix GX8 cameras. Source: Objaverse 1.0 / Sketchfab
Data from: Germination of 10 midland plant species from the eastern Mediterranean Basin: Effects of smoke, syringaldehyde, karrikinolide, and cyanohydrin
<p>The beneficial effects of smoke and its constituents, karrikinolide (KAR<sub>1</sub>) and cyanohydrin glyceronitrile, on the germination of Mediterranean lowland species are well-documented. However, very little is known about the role of these signals on the germination of plants at higher altitudes. In addition, lignin-derived chemicals, such as syringaldehyde (SAL), have recently been proposed as overlooked cues for smoke-induced germination. To address these gaps in the literature, we investigated the effects of smoke-water and SAL on the germination of 10 midland species growing on serpentine soils. We also sought to determine whether SAL interacts with KAR<sub>1</sub> and/or mandelonitrile (MAN, a cyanohydrin) to enhance seed germination. The results show that smoke-water significantly improved the germination of three species (<em>Barbarea duralii</em>, <em>Digitalis cariensis</em>, and <em>Turritis laxa</em>). SAL, on the other hand, had no positive effect on the germination of the 10 species tested. Furthermore, three smoke-sensitive species and <em>Verbascum cariense</em> responded to KAR<sub>1</sub> and/or MAN. Finally, SAL did not exert any synergistic effects on germination in interaction with KAR<sub>1</sub> and MAN. In conclusion, we provide evidence that smoke is an important germination cue also for Mediterranean midland species. Moreover, SAL did not play a role in stimulating germination in smoke-sensitive species, either independently or in combination with other smoke chemicals.</p>
Large simulated radiative effects of smoke in the south-east Atlantic: supporting data
<p>Datasets required to reproduce key figures from journal article acp-2018-305. Contact corresponding author for further information.</p> <p>In the filenames, 'BB' indicates simulation results with biomass burning aerosol, 'noBB' without, and 'noAA' are simulations without absorbing effects of aerosol. The 300x300 regional domain is a rotated pole centred on 10S, 12W.</p>
Impact of exposure to second-hand smoke during childhood on blood DNA methylation
<p>Meta-analysis results for CpGs associated with household postnatal exposure to second hand smoke (SHS). Cohorts order are GenR, PIAMA, HELIX, BAMSE_Medall, BAMSE_EpiGene, and ALSPAC. Effect size represents the change in DNAm by number of household smokers (from 0 smokers to 2 smokers or more). Gene and CpG Island relative position are indicated (TSS200 is the region from Transcription start site (TSS) to − 200 nt upstream of TSS; TSS1500 covers − 200 to − 1500 nt upstream of TSS; S_Shore corresponds to the 2 kb regions immediately downstream of the CpG island; N_Shore corresponds to the 2 kb regions immediately upstream of the CpG island). Chr stands for Chromosome, SE stands for standard error, I2 stands for heterogeneity index, and FDR stands for False Discovery Rate. (DNA data obtained through Iluumina 450K array profiling)</p>
Updated Smoke Exposure Estimate for Indonesian Peatland Fires using a Network of Low-cost PM2.5 sensors and a regional air quality model - Model Simulation Data
<p>WRF-Chem simulated daily mean PM2.5 concentrations for:</p> <p>1) with fires </p> <p>2) without fires</p> <p>simulations. </p>
Updated Smoke Exposure Estimate for Indonesian Peatland Fires using a Network of Low-cost PM2.5 sensors and a regional air quality model - Purple Air data
<p>Daily mean PM2.5 concentrations collected by Purple Air sensors between 2023-08-16 and 2023-12-01. Concentrations have been RH adjusted using the Nilson et al (2022) adjustment. </p>
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