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679 results for “Ozone”
Influence of Large-scale Land-sea Atmosphere Interaction on Ozone Pollution in Coastal Cities in the Northern Bohai Sea
<p><strong>O3_obs </strong>includes ozone observations for Qinhuangdao (QHD), Jinzhou (JZ), Yingkou (YK), Dalian (DL) from 29 August to 5 September 2017, and the information of four sites including station code, longitude and latitude. <strong>O3_sim</strong> includes ozone simulation in the four sites extracted according to location of them. <strong>Met_obs</strong> and <strong>Met_sim</strong> include the observations of 2 m temperature (℃), 2 m relative humidity (RH2) and 10 m wind speed for the 4 stations from 29 August to 5 September 2017, and the information of four stations including station code and their location. <strong>Slp_wind_9km.nc</strong> is mean sea-level pressure and wind in Phase Ⅰ and Phase Ⅱ. <strong>O3_wind_9km.nc</strong> is mean simulated surface ozone mixing ratios and wind at 10 m in 19:00-09:00 LT and 10:00-18:00 LT during Phase Ⅰ and Phase Ⅱ. <strong>Process_contribution </strong>includes mean surface O<sub>3</sub> mixing ratios and O<sub>3</sub> contribution at the bottom level in Phase Ⅰ, Phase Ⅱ, and at different heights (AGL) in Phase Ⅱ in four sites, respectively. <strong>O3_source_site</strong> includes time series of O<sub>3 </sub>source in QHD, JZ, YK, and DL. <strong>Mean_source_base_27km.nc </strong>is the mean O­<sub>3</sub> contribution in Phase Ⅰ and Phase Ⅱ from five primary exogenous source regions. <strong>Mean_source_control_27km.nc</strong> is the O<sub>3</sub> contribution in Phase Ⅱ from the BTH and NEC emissions in Phase I, in which BTH and NEC’s emissions in Phase Ⅱ are set zero. <strong>Trjectory_conc_pa</strong> includes three trajectories analyzed in this work and vertical O<sub>3</sub> and NO<sub>X</sub> mixing ratios, and the chemical generations and consumptions of O<sub>3</sub> within the air masses along the trajectories.</p>
Hourly LC impacts - Ozone Layer Depletion - current mix and future scenarios, average demand
<p>Dataset on LCA results of electricity generation and supply in Italy for 2018, 2019 and 2020 (current mix) and two future scenarios (2030) - Ozone Layer Depletion, average demand perspective.</p> <p>Modelling materials and methods are described in the paper "Life-cycle assessment of current and future electricity supply in Italy: addressing average and marginal hourly demand".</p>
Random and systematic uncertainties for OMPS-LP ozone profiles
<p>This data set contains random and systematic uncertainties for ozone profiles retrieved at the University of Bremen from OMPS-LP observations. The uncertainties are expressed in relative values and are reported as vertical profiles (every 5 km) and as a function of latitude (5 bands, i.e., SH polar, SH mid-latitude, tropics, NH mid-latitudes, NH polar) and season. The user can associate to any OMPS-LP ozone profile the uncertainty corresponding to the appropriate latitude-season bin. The description of the uncertainties is provided in the AMT paper "Assessment of the error budget for stratospheric ozone profiles retrieved from OMPS limb scatter measurements", Arosio et al. 2022.</p>
Illustrative dataset for Ozone radiative forcing calculations using SOCRATES-RF
<p>This dataset provides to the reader/user with two netCDF files which illustrate the structure and properties of the input datasets (used directly by the software SOCRATES-RF) in support of the publication: "<strong>Historical tropospheric and stratospheric ozone radiative forcing using the CMIP6 database</strong>". It comprises two examples of January (pre-industrial decade, 1850s): one based on CMIP5 ozone concentrations and other based on the recently available CMIP6 ozone dataset. Both were created with the procedure described on the supplementary information of the publication "Historical tropospheric and stratospheric ozone radiative forcing using the CMIP6 database", Checa-Garcia, R et al.</p> <p>The sources of information for these datasets are the CMIP5 / CMIP6 ozone dataset, the ERA-Interim reanalysis dataset (2000-01 to 2009-12) and the solar irradiance from SORCE and TIM projects. Please see the references:</p> <ul> <li>Cionni, I., Eyring, V., Lamarque, J. F., Randel, W. J., Stevenson, D. S., Wu, F., Bodeker, G. E., Shepherd, T. G., Shindell, D. T., and Waugh, D. W.: Ozone database in support of CMIP5 simulations: results and corresponding radiative forcing, Atmos. Chem. Phys., 11, 11267-11292, https://doi.org/10.5194/acp-11-11267-2011, 2011.</li> <li>Hegglin, M. I., D. Kinnison, D. Plummer, R.Checa-Garcia et al., Historical and future ozone database (1850-2100) in support of CMIP6, GMD, in preparation.</li> <li>Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B., Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P., Köhler, M., Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J.-J., Park, B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J.-N. and Vitart, F. (2011), The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q.J.R. Meteorol. Soc., 137: 553–597. doi: 10.1002/qj.828</li> <li>Kopp G., Heuerman K., Lawrence G. (2005) The Total Irradiance Monitor (TIM): Instrument Calibration. In: Rottman G., Woods T., George V. (eds) The Solar Radiation and Climate Experiment (SORCE). Springer, New York, NY</li> </ul>
TOMCAT model data & IASI/GOME-2B satellite data of European ozone between 2008 - 2023
<p>Daily mean data of ozone (O3) from the TOMCAT 3D chemical transport model (Chipperfield, 2006) and two satellite products, the Infrared Atmospheric Sounding Interferometer (IASI) on the MetOp-A & B satellites and the Global Ozone Monitoring Experiment-2 (GOME-2) on the MetOp-B satellite. The satellite observations are retrieved using schemes developed by the Rutherford Appleton Laboratory (RAL) see Miles et al. (2015) and Pope et al. (2021). The TOMCAT model data is available for 2017 - 2021, the IASI data is available for 2008 - 2023 and the GOME-2 data for 2015 - 2020. </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 used in paper "A comparative study of calibration methods for low-cost ozone sensors in IoT platforms"
<p>Data used in paper "A comparative study of calibration methods for low-cost ozone sensors in IoT platforms", submitted for publication. The data consists of: (i) raw data from three nodes with four MICS 2614 metal-oxide ozone sensors deployed in Spain, summer 2017, and (ii) raw data of five alphasense OX-B431 and NO2-B43F electro-chemical sensors, four deployed in Italy and one in Austria, summers 2017 and 2018. Moreover, we have added the calibrated data using four machine learning methods: Multiple Linear Regression (MLR), K-Nearest Neighbors (KNN), Random Forest (RF) and Support Vector Regression (SVR).</p>
BS Filled Total Column Ozone Database V3.4.1
<p>Version 3.4.1 of the BS <strong>Filled</strong> Total Column Ozone (TCO) database provides an extension in time of the BS Filled TCO database v3.4. Please refer to version 3.4. (doi:10.5281/zenodo.3908787) for details on the creation of the database.</p> <p><strong>Please note: </strong>For the reasons detailed in <a href="https://storage.bodekerscientific.com/Bodeker%20Scientific%20TCO%20V3.4.x%20and%20V3.5.x%20differences.pdf">this</a> document, versions 3.5.x of the BS Filled TCO<br> database and of the NIWA-BS TCO database should not be used henceforth for trend analysis and, as such, we have updated the version 3.4 of BS Filled TCO database to the end of 2019 (now referred to as version 3.4.1 of the database) as a replacement.</p> <p>You can also access the unfilled version of this database at doi:10.5281/zenodo.7447660.</p> <p><strong>Please email greg@bodekerscientific.com and let us know which data set you downloaded and what your intended purpose for the use of the data is. You will then receive updates if an improved version becomes available. </strong></p> <p> </p> <p><br> </p> <pre> </pre>
Model results and configuration files for "Large modeling uncertainty in projecting decadal surface ozone changes over urban and industrial regions of China"
<p>This repository includes files as described below:</p> <p><strong>1. namelist_CBMZ09_example.input, namelist_MOZART202_example.input:</strong></p> <p>Two WRF-chem namelist files for CBMZ and MOZART simulation.</p> <p>They are modified according to the namelist from <a href="https://github.com/wrfchem-leeds/WRFotron">https://github.com/wrfchem-leeds/WRFotron</a>.</p> <p><strong>2. wps_namelist_example.wps:</strong></p> <p>namelist for WRF Preprocessing System (WPS)</p> <p><strong>3. temporal_hourly_scale_factor_emission.csv:</strong></p> <p>Hourly scale factors for emissions.</p> <p>Hourly allocation is applied to all emission data (i.e., emissions for 2017, 2030 and perturbated emissions of NOx, VOCs).</p> <p><strong>4. vertical_emission_ratio.csv</strong></p> <p>Vertical shares (ratios) of emissions.</p> <p>Emissions from sectors of power and industry are vertically allocated based on this file. Vertical allocation is conducted for all emission data.</p> <p>These shares are suggested by MICS-ASIA III intercomparison framework.</p> <p><strong>5. 01_2030_2017_simulations.zip: </strong></p> <p>Simulated MDA8 ozone under future (2030) and 2017 emission scenarios by the two chemical mechanisms (i.e., CBMZ, MOZART).</p> <p><strong>6. 02_perturbations_of_NOxVOCs.zip:</strong></p> <p>Simulated MDA8 ozone given perturbations of NOx and VOCs emissions by the two chemical mechanisms.</p> <p><strong>7. 03_hourly_diff_O3_NOx_OH_HNO3.zip: </strong></p> <p>Differences of hourly simulated concentrations of O3, NOx, OH and HNO3 during July in the Base-2017 scenario between CBMZ and MOZART (CBMZ - MOZART).</p>
MEaSUREs blue band total column water vapor sample data for the Ozone Monitoring Instrument
<p>This dataset contains the MEaSUREs OMI Total Column Water Vapor (TCWV) data and their related data used in the paper titled “Development of the MEaSUREs blue band water vapor algorithm – Towards a long-term data record” by Wang et al. (2023). The unzipped archive contains the following three directories. </p> <ol> <li>OMI-H2O-L2/ contains the MEaSUREs Level 2 data (in molecules/cm2) in netCDF4 format for January and July 2005 and 2006. Selected supporting data are also included in each file.</li> <li>OMI-H2O-L3/ contains MRaSUREs Level 3 data (0.25 degree by 0.25 degree, in molecules/cm2) generated using the standard filtering criteria in netCDF4 format for January and July 2005 and 2006. Selected supporting data are also included.</li> <li>Model3_ncresult/ contains netCDF4 formatted files for the MEaSUREs OMI TCWV data (in mm), the AMSR_E TCWV data sampled onto the corresponding OMI pixel locations, and the LightGBM model 3 predictions for the OMI pixels.</li> </ol> <p>The linux command ‘ncdump -h filename’ can be used to examine the contents of netCDF4 files. Due to the current size limit of Zenodo, only a small subset of the MEaSUREs data is archived here. The full dataset will be released elsewhere, e.g., NASA EARTHDATA GES DISC.</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 GRL "Tropical Stratospheric Circulation and Ozone Coupled to Pacific Multi-Decadal Variability" paper
<p>Data of the CESM-WACCM sensitivity simulations. These simulations follow the REFC1 configuration from the Chemistry-Climate Model Initiative (CCMI), but for: fixed long-lived halogenated substances at year 1955 (REFC1-fODS); fixed long-lived halogens and nitrogen oxide emissions at year 1955 (REFC1-fODS-N2O); fixed stratospheric aerosol averaged over 1998–1999 (REFC1-fSAD); climatological sea surface temperatures (SSTs) and sea-ice concentrations (SICs) for the 1960–2010 period (REFC1-fSST); climatological SSTs and SICs for the 1960–2010 period, including a 28-months cyclical QBO (REFC1-fSST-QBO).</p>
OSIRIS Ozone v5.07
<p>OSIRIS spectra of limb scattered sunlight are used to retrieve ozone number density vertical profiles over the altitude range from 10 to 60 km at a vertical resolution of approximately 1.5 km using the a Multiplicative Algebraic Reconstruction Technique, which is a one dimensional modification of a two-dimensional tomographic retrieval algorithm. This technique allows for the consistent merging of the absorption information from radiance measurements at wavelengths in the Chappuis and the Hartley-Huggins bands at each iteration of the inversion. A set of weighting factors is used to determine the importance of each line of sight and each element of the measurement vector for the retrieved state at each altitude. Retrieval algorithm details can be found in <a href="https://www.atmos-chem-phys.net/9/6521/2009/">Degenstein et al., 2009</a>.</p> <p>Version 5.07, which does not implement a tangent altitude registration correction algorithm has been superceeded by version 5.10.</p>
Dataset for ozone imacts on NPP in the North China Plain
<p>Ozone pollution led to an average 25% decrease in NPP in the North China Plain (NCP). This decline is consistent with previous estimates that ranged from 10% to 25%. Productivity of forest trees, crops, and grasses declined significantly, ranging from 36.7% to 47.1%. These results have implications for Chinese crops, as lower crop productivity can negatively affect crop yields. Our study highlights the urgent need for action to mitigate the detrimental effects of ozone pollution on ecosystem health and productivity.</p>
Dataset for ozone imacts on NPP in the North China Plain
<p>Ozone pollution led to an average 25% decrease in NPP in the North China Plain (NCP). This decline is consistent with previous estimates that ranged from 10% to 25%. Productivity of forest trees, crops, and grasses declined significantly, ranging from 36.7% to 47.1%. These results have implications for Chinese crops, as lower crop productivity can negatively affect crop yields. Our study highlights the urgent need for action to mitigate the detrimental effects of ozone pollution on ecosystem health and productivity.</p>
Lifetimes and timescales of tropospheric ozone: Ozone emission experiments
<p>The lifetime of tropospheric O<sub>3</sub> is difficult to quantify because we model O<sub>3</sub> as a secondary pollutant, without direct emissions. For other reactive greenhouse gases like CH<sub>4</sub> and N<sub>2</sub>O, we readily model lifetimes and timescales that include chemical feedbacks based on direct emissions. Here, we devise a set of artificial experiments with a chemistry-transport model where O<sub>3</sub> is directly emitted into the atmosphere at a quantified rate. We create three primary emission patterns for O<sub>3</sub>, mimicking secondary production by surface industrial pollution, that by aviation, and primary injection through stratosphere-troposphere exchange (STE). The perturbation lifetimes for these O<sub>3</sub> sources includes chemical feedbacks and varies from 6 to 27 days depending on source location and season. Previous studies derived lifetimes around 24 days estimated from the mean odd-oxygen loss frequency. The timescales for decay of excess O<sub>3</sub> varies from 10–20 days in NH summer to 30–40 days in NH winter. For each season, we identify a single O<sub>3</sub> chemical mode applying to all experiments. Understanding how O<sub>3</sub> sources accumulate (the lifetime) and disperse (decay timescale) provides some insight into how changes in pollution emissions, climate, and stratospheric O<sub>3</sub> depletion over this century will alter tropospheric O<sub>3</sub>. This work incidentally found two distinct mistakes in how we diagnose tropospheric O<sub>3</sub>, but not how we model it. First, the chemical pattern of an O<sub>3</sub> perturbation or decay mode does not resemble our traditional view of the odd-oxygen family of species that includes NO<sub>2</sub>. Instead, a positive O<sub>3</sub> perturbation is accompanied by a decrease in NO<sub>2</sub>. Second, heretofore we diagnosed the importance of STE flux to tropospheric O<sub>3</sub> with a synthetic 'tagged' tracer O3S, which had full stratospheric chemistry and linear tropospheric loss based on odd-oxygen loss rates. These O3S studies predicted that about 40 % of tropospheric O<sub>3</sub> was of stratospheric origin, but our lifetime and decay experiments show clearly that STE fluxes add about 8 % to tropospheric O<sub>3</sub>, providing further evidence that tagged tracers do not work when the tracer is a major species with chemical feedbacks on its loss rates, as shown for CH<sub>4</sub>. </p>
Potential Ozone Depletion from Satellite Demise during Atmospheric Reentry in the Era of Mega-Constellations
<p>Dataset support to "Potential Ozone Depletion from Satellite Demise during Atmospheric Reentry in the Era of Mega-Constellations"</p>
ATMOZ Gorshelev Huggins Ozone Band Absorption Cross-Section
<p>See PDF file for more information.</p>
Data for: Evaluation of a full-scale wastewater treatment plant with ozonation and different post-treatments using a broad range of in vitro and in vivo bioassays
<p>This repository contains research data linked to the following publication: Kienle, C., Werner, I., Fischer, S., Lüthi, C., Schifferli, A., Besselink, H., Langer, M., McArdell, C.S. and Vermeirssen, E.L.M. 2022. Evaluation of a full-scale wastewater treatment plant with ozonation and different post-treatments using a broad range of <em>in vitro</em> and <em>in vivo</em> bioassays. Water Research, 118084. https://doi.org/10.1016/j.watres.2022.118084</p> <p>Abstract: Micropollutants present in the effluent of wastewater treatment plants (WWTPs) after biological treatment are largely eliminated by effective advanced technologies such as ozonation. Discharge of contaminants into freshwater ecosystems can thus be minimized, while simultaneously protecting drinking water resources. However, ozonation can lead to reactive and potentially toxic transformation products. To remove these, the Swiss Federal Office for the Environment recommends additional "post-treatment" of ozonated WWTP effluent using sand filtration, but other treatments may be similarly effective. In this study, 48 h composite wastewater samples were collected before and after full-scale ozonation, and after post-treatments (full-scale sand filtration, pilot-scale fresh and pre-loaded granular activated carbon, and fixed and moving beds). Ecotoxicological tests were performed to quantify the changes in water quality following different treatment steps. These included standard <em>in vitro</em> bioassays for the detection of endocrine, genotoxic and mutagenic effects, as well as toxicity to green algae and bacteria, and flow-through <em>in vivo</em> bioassays using oligochaetes and early life stages of rainbow trout.</p> <p>Results show that ozonation reduced a number of ecotoxicological effects of biologically treated wastewater by 66 - 93 %: It improved growth and photosynthesis of green algae, decreased toxicity to luminescent bacteria, reduced concentrations of hormonally active contaminants and significantly changed expression of biomarker genes in rainbow trout liver. Bioassay results showed that ozonation did not produce problematic levels of reaction products overall. Small increases in toxicity observed in a few samples were reduced or eliminated by post-treatments. However, only relatively fresh granular activated carbon (analyzed at 13,000 - 20,000 bed volumes) significantly reduced effects additionally (by up to 66 %) compared to ozonation alone. Inhibition of algal photosynthesis, rainbow trout liver histopathology and biomarker gene expression proved to be sufficiently sensitive endpoints to detect the change in water quality achieved by post-treatment.</p>
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