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29 results for “ozone concentration”
Global Surface Ozone Concentration Dataset 1990-2017 Mapped at Fine Resolution through the Bayesian Maximum Entropy Data Fusion of Observations and Model Output
<p>This global surface ozone concentration dataset corresponds to the data developed in this paper:</p> <p>DeLang, M. N., J. S. Becker, K.-L. Chang, M. L. Serre, O. R. Cooper, M. G. Schultz, S. Schroder, X. Lu, L. Zhang, M. Deushi, B. Josse, C. A. Keller, J.-F. Lamarque, M. Lin, J. Liu, V. Marecal, S. A. Strode, K. Sudo, S. Tilmes, L. Zhang, S. Cleland, E. Collins, M. Brauer, and J. J. West (2021) Mapping yearly fine resolution global surface ozone through the Bayesian Maximum Entropy data fusion of observations and model output for 1990-2017, <em>Environmental Science & Technology</em>, 55, 4389-4398, doi: 10.1021/acs.est.0c07742.</p> <p>Ozone concentrations are estimated as described in the paper, with output shown for the Ozone Season Daily Maximum 8-hr metric (OSDMA8) for each year between 1990 and 2017, at 0.1 degree spatial resolution. Ozone is estimated through data fusion of output from several global models, with observations of ozone collected by TOAR. The data fusion involves application of the M3Fusion method to create a multi-model composite of several global models, followed by BME data fusion, as described in the paper. </p> <p>The *.nc file contains the latitude, longitude, ozone concentration estimate, and estimated variance for each 0.1 x 0.1 degree grid cell.</p> <p>Please contact Jason West (jasonwest@unc.edu) with questions about the dataset. We'd like to hear from you to know how you're using the data!</p> <p> </p> <p> </p>
Global Surface Ozone Concentration Dataset 1990-2017 Generated by Bayesian Maximum Entropy Data Fusion With RAMP Bias Correction
<p>This dataset reports estimates of surface ozone concentration at fine spatial resolution for 1990 to 2017, at 0.5 degree horizontal resolution. Also reported is the variance. Estimates correspond to this paper:</p> <p><span>Becker, J. S.</span><span>, DeLang, M. N., K.-L. Chang, M. L. Serre, O. R. Cooper, <u>H. Wang</u>, M. G. Schultz, S. Schroder, X. Lu, L. Zhang, M. Deushi, B. Josse, C. A. Keller, J.-F. Lamarque, M. Lin, J. Liu, V. Marecal, S. A. Strode, K. Sudo, S. Tilmes, L. Zhang, M. Brauer, and <span>J. J. West</span> (2023) Using Regionalized Air Quality Model Performance and Bayesian Maximum Entropy data fusion to map global surface ozone concentration, <em>Elementa Science of the Anthropocene</em>, 11: 1, doi: 10.1525/elementa.2022.00025.</span></p> <p>The dataset reports estimates of surface ozone for the OSDMA8 metric (the 6-month ozone-season average of the daily maximum 8-hr concentration), estimated through a data fusion of ozone observations from the Tropospheric Ozone Assessment Report (TOAR) database, and output from multiple global atmospheric models. Estimates are created in each year by a combination of M3Fusion to create a multi-model composite, Regional Air Quality Model Performance (RAMP) regional and nonlinear bias correction, and Bayesian Maximum Entropy (BME) data fusion in space and time. The estimates here are the final results using a weighted RAMP bias correction. </p>
Ambient air ozone concentrations using metal-oxide low-cost sensors: Spain and Italy, summer 2017
<p>Ozone concentrations in ambient air collected using low-cost sensor technologies, in the framework of EU project CAPTOR. Data collected during summer 2017 in NE Spain and N Italy. Sensors are metal-oxide. Data are calibrated using multiple linear regression, and validated against official reference data from each local air quality monitoring network. More details on the calibration and data validation may be found in A. Ripoll et al. / Science of the Total Environment 651 (2019) 1166–1179.</p> <p> </p>
Ambient air ozone concentrations using metal-oxide low-cost sensors: Spain and Italy, summer 2018
<p>Ozone concentrations in ambient air collected using low-cost sensor technologies, in the framework of EU project CAPTOR. Data collected during summer 2018 in NE Spain and N Italy. Sensors are metal-oxide. Data are calibrated using multiple linear regression, and validated against official reference data from each local air quality monitoring network. More details on the calibration and data validation may be found in A. Ripoll et al. / Science of the Total Environment 651 (2019) 1166–1179.</p>
Data in Support of Effects of Urbanization and Forest Fragmentation on Atmospheric Nitrogen Inputs and Ambient Nitrogen Oxide and Ozone Concentrations in Mixed Temperate Forests.
Urban ecosystems around the globe experience greater atmospheric nitrogen (N) deposition compared to rural areas and are particularly vulnerable to fragmentation due to land-use change. However, while the influences of urbanization and forest fragmentation on atmospheric inputs to temperate forests have been determined separately, the combined effects of the two changes on temperate forest ecosystems have yet to be assessed. To investigate these combined effects, we deployed throughfall collectors to measure atmospheric N inputs and passive samplers to measure nitrogen oxides (NOx) and ozone (O3) throughout the 2018 and 2019 growing seasons in seven temperate forest sites along an urbanization gradient from Boston to central Massachusetts. We found a positive relationship between the amount of impervious surface area surrounding each site (% ISA) and throughfall nitrate (NO3-) inputs at the forest edge, with urban edge NO3- inputs nearly double the rate at rural edge sites. There were higher rates of NO3- inputs in the rural forest interior than edge sites. Urban sites experienced significantly higher concentrations of NOx and O3 both in the interior and at the edge compared to rural sites. Atmospheric N inputs were significantly elevated in the early (May-July) compared to the late (August-November) growing season and concentrations of NOx and O3 were also elevated in the mid-growing season (June-September). Our results demonstrate that together, urbanization and forest fragmentation lead to greater rates of atmospheric N inputs and ambient pollutant concentrations of NOx and O3 in temperate forests of the northeastern U.S.
Data for the publication "Ozone concentration versus Temperature: Atmospheric aging of soot particles"
<p>The repository contains the data for the paper:</p> <p>Friebel, F and Mensah, A. A. Ozone concentration versus Temperature: Atmospheric aging of soot particles, <em>Langmuir </em> <strong>2019</strong>, 35, 45, 14437–14450 <a href="https://doi.org/10.1021/acs.langmuir.9b02372">https://doi.org/10.1021/acs.langmuir.9b02372</a></p>
Dataset 8 of 8: Ozone (O3) concentrations in California (2016-2019) (CARB 19RD004)
Open the record for dataset details and reuse information.
Dataset 7 of 8: Ozone (O3) concentrations in California (2012-2015) (CARB 19RD004)
Open the record for dataset details and reuse information.
Ozone Concentrations and Ozone Flux at the Univerity of Michigan Biological Station PROPHET Tower from 2002-2005
Measurements of ozone, sensible heat, and latent heat fluxes and plant physiological parameters were made at a northern mixed hardwood forest located at the University of Michigan Biological Station in northern Michigan from June 27 to September 28, 2002. These measurements were used to calculate total ozone flux and partitioning between stomatal and non-stomatal sinks. Total ozone flux varied diurnally with maximum values reaching 100 8mol m-2 h-1 at midday and minimums at or near zero at night. Mean daytime canopy conductance was 0.5 mol m-2 s-1. During daytime, non-stomatal ozone conductance accounted for as much as 66% of canopy conductance, with the non-stomatal sink representing 63% of the ozone flux. Stomatal conductance showed expected patterns of behaviour with respect to photosynthetic photon flux density (PPFD) and vapour pressure defecit (VPD). Non-stomatal conductance for ozone increased monotonically with increasing PPFD, increased with temperature (T) before falling off again at high T, and behaved similarly for VPD. Day-time non-stomatal ozone sinks are large and vary with time and environmental drivers, particularly PPFD and T. This information is crucial to deriving mechanistic models that can simulate ozone uptake by different vegetation types.
The response of the ozone layer to quadrupled CO2 concentrations: implications for climate
<p>The quantification of the climate impacts exerted by stratospheric ozone changes in abrupt 4 × CO2 forcing experiments is an important step in assessing the role of the ozone layer in the climate system. Here, we build on our previous work on the change of the ozone layer under 4 × CO2 and examine the effects of ozone changes on the climate response to 4 × CO2, using the Whole Atmosphere Community Climate Model. We show that the global-mean radiative perturbation induced by the ozone changes under 4 × CO2 is small, due to nearly total cancellation between high and low latitudes, and between longwave and shortwave fluxes. Consistent with the small global-mean radiative perturbation, the effect of ozone changes on the global-mean surface temperature response to 4 × CO2 is negligible. However, changes in the ozone layer due to 4 × CO2 have a considerable impact on the tropospheric circulation. During boreal winter, we find significant ozone-induced tropospheric circulation responses in both hemispheres. In particular, ozone changes cause an equatorward shift of the North Atlantic jet, cooling over Eurasia, and drying over northern Europe. The ozone signals generally oppose the direct effects of increased CO2 levels and are robust across the range of ozone changes imposed in this study. Our results demonstrate that stratospheric ozone changes play a considerable role in shaping the atmospheric circulation response to CO2 forcing in both hemispheres and should be accounted for in climate sensitivity studies.</p>
Surface Ozone, NO2, and PM2.5 Concentrations Estimated by the Deep Learning model (Air Transformer) based on Satellite data.
<p>Surface ozone, NO2, and PM2.5 concentrations Estimated by the deep learning model (Air Transformer) based on massive ground-level monitoring, satellite observations, meteorological conditions, dynamic industrial emissions, and other ancillary data from May 2018 to June 2021.</p>
Rethinking the Roles of Transport and Photochemistry in Regional Ozone Pollution: Insights from Ozone Mass and Concentration Budgets
<p>1) Code (Fortran) to quantify ozone mass and concentration budgets in the atmospheric boundary layer of the user-defined region.</p> <p>2) Initial data of ozone mass and concentration budgets in the atmospheric boundary layer of the Pearl River Delta in Oct. 2015 and July 2016.</p>
Potent activity of high concentration ozone therapy against an-tibiotic-resistant bacteria
<p><strong>Figure S1. </strong>SANITECH O3-80-Sanitization equipment ozone generator coupled to two containers of approximately 1m<sup>3</sup> each, used for exposing samples to ozone.</p>
Impacts of biomass burning in peninsular Southeast Asia on PM2.5 concentration and ozone formation in southern China during springtime – A case study
<p>Abstract: Biomass burning (BB) affects fine particulate matter (PM<sub>2.5</sub>) and ozone (O<sub>3</sub>) formations by emitting their gaseous precursors and primary aerosols. Impacts of BB in peninsular Southeast Asia (BB-PSEA) are evaluated on the PM<sub>2.5</sub> and O<sub>3</sub> formations in southern China, using a source-oriented WRF-Chem model to simulate an air pollution episode from 21 to 25 March 2015. The source-oriented model separates the emission from the BB-PSEA and other sources and is able to evaluate the effect of aerosol-radiation interactions (ARI) and aerosol-photolysis interactions (API) from the BB-PSEA. Comparisons with observations reveal that the model performs well in simulating the air pollution episode. Sensitivity experiments show that BB-PSEA increases PM<sub>2.5</sub> concentrations by 39.3 μg m<sup>-3</sup> (68.0%) in Yunnan Province (YNP) and 8.4 μg m<sup>-3</sup> (24.1%) in other downwind areas (ODA) in southern China (including the provinces of Guizhou, Guangxi, Hunan, Guangdong, Jiangxi, Fujian, and Zhejiang) on the regional average. The PM<sub>2.5</sub> enhancement is mainly contributed by primary aerosols in YNP but by secondary aerosols in the ODA. The BB-PSEA increases O<sub>3</sub> concentrations of 18.1 μg m<sup>-3 </sup>(19.4%) in YNP and decreases O<sub>3 </sub>concentrations in the ODA by 3.7 μg m<sup>-3</sup> (5.3%). The O<sub>3</sub> increase in YNP is contributed by the gaseous emissions of the BB-PSEA, and the O<sub>3</sub> decrease in the ODA is caused by the effects of ARI and API of the BB-PSEA. The NH<sub>3</sub> emissions from the BB-PSEA plays a key role in enhancing secondary inorganic aerosols in southern China, and also determine the PM<sub>2.5</sub> increase in the ODA.</p>
Isoprene concentrations data used in the paper "Assessment of isoprene and near surface ozone sensitivities to water stress over the Euro-Mediterranean region"
<p>Isoprene concentrations collected by E. Bourtsoukidis and J. Williams during a field-campaign that took place in Cyprus (site field: Ineia; Latitude: 34.96° N, Longitude: 32.39° E) during the summer 2014 (from July 7 to August 3; data collected every 45 minutes) using the technique of gas chromatography - mass spectrometry (GC-MS) (Derstroff et al., 2017). These data have been used to validate isoprene concentrations simulated by the regional climate model RegCM applied in the study "<em>Assessment of isoprene and near surface ozone sensitivities to water stress over the Euro-Mediterranean region</em>" (https://doi.org/10.5194/egusphere-2022-1522).</p>
The response of the ozone layer to quadrupled CO2 concentrations: implications for climate
Open the record for dataset details and reuse information.
Regional E-Atlas of the Greater Phoenix Region: Estimated concentrations of ozone in the Greater Phoenix area, July 26, 1996.
The data represent modeled concentrations of ozone in the Greater Phoenix area for a sample date (July 26, 1996). Data are presented in part per million by volume (PPMV).
Three year average ozone concentrations within the central Arizona-Phoenix area, period 2003 to 2005
These data is part of the effort to provide the most detailed data on air quality in CAPLTER. This dataset represents spatially interpolated ozone (O3) levels as measured by monitroring stations of the Maricopa County Air Quality Department. O3 is a naturally occurring compound in which three oxygen atoms combine together. This is an unstable combination, and ozone is continually going through a natural cycle of being formed and then converting back to the more stable 'normal' double oxygen compound (O2). The cycle occurs fairly rapidly. In the stratosphere (6 miles and more above the earth), naturally occurring ozone has a beneficial effect of screening out harmful ultraviolet light from the sun. However, ground-level ozone is a pollutant and is a major component of the brown haze that is often observed in the Maricopa County valley. Ozone is not directly emitted into the air, but rather forms in a complex reaction that involves heat, sunlight, and a 'soup' of toxic pollutants, especially volatile organic compounds (VOCs). Some of the most common sources of VOCs are gasoline vapors, chemical solvents, and combustion products of fuels and consumer products. Ozone is created by sunlight acting on nitrates (NOX) and VOCs from motor vehicles and stationary sources, and can be carried hundreds of miles from their origins. Ozone affects the respiratory system in people and animals, and also affects the growth of plants.
Data from: Invasive herbaceous respond more negatively to elevated ozone concentration than native species
<p>Aim: Many studies show that increase in ground-level ozone (O<sub>3</sub>) has adverse effects on plant growth. Due to high phenotypic plasticity, invasive species is considered to be more adaptable to elevated O3 than native species. This idea is only tested by the very limited studies comparing invasive weeds with crops. However, whether it holds remains unclear when comparing invasive species with their co-occurring native species in natural systems.</p> <p>Location: China</p> <p>Methods: We performed an open-top chamber experiment growing six congeneric pairs of invasive and native species with and without competition under ambient (approximately 43 ppb) and elevated O<sub>3</sub> (approximately 89 ppb) concentrations to test whether the growth responses to elevated O<sub>3</sub> concentrations differ between invasive and native species.</p> <p>Results: Our results revealed that elevated O<sub>3</sub> had a significant negative effect on both invasive and native species. In particular, elevated O<sub>3</sub> reduced the aboveground biomass and damaged the leaves of invasive species significantly more than those of native species.</p> <p>Main conclusions: Our study indicates that elevated O<sub>3</sub> concentration has a stronger adverse effect on invasive species than on native species. Therefore, increasing O<sub>3</sub> pollution might suppress plant invasion, and thus invasive species might expand their distribution more easily to the area with lower O<sub>3</sub> pollution in the future.</p>
Dataset for "Effects of two different biogenic emission models on modelled ozone and aerosol concentrations in Europe"
<p>Data for figures in the manuscript "Effects of two different biogenic emission models on modelled ozone and aerosol concentrations in Europe"</p> <p>Jiang, J., Aksoyoglu, S., Ciarelli, G., Oikonomakis, E., El-Haddad, I., Canonaco, F., O'Dowd, C., Ovadnevaite, J., Minguillón, M. C., Baltensperger, U., and Prévôt, A. S. H.: Effects of two different biogenic emission models on modelled ozone and aerosol concentrations in Europe, Atmos. Chem. Phys., 2019.</p> <p> </p> <p> All the data are stored in .mat file, and the variable names are self-explanatory.</p> <p> </p> <p> </p> <p> </p>
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International Brain Laboratory public data
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OpenNeuro
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