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29 results for “ozone concentration”
POD6, POD0, O3 concentrations, and Jarvis functions in order to assess the global flux-based ozone risk for wheat up to 2100 under different climate scenarios
<p>Model output associated with the study <em>“Global flux-based assessment reveals declining ozone risk for wheat in future climate change scenarios”</em> (Guaita <em>et al.</em>, 2025).</p> <p>The output is provided under the <strong>Creative Commons Attribution 4.0 International (CC BY 4.0)</strong> license. Please cite <strong>both this repository and the associated paper</strong> when referencing this output.</p> <p><strong>Associated paper:</strong></p> <blockquote> <p><strong>Guaita, P., et al.</strong> (2025).<br><em>Global flux-based assessment reveals declining ozone risk for wheat in future climate change scenarios.</em><br><em>Global Change Biology (Under review)</em>.<br><a href="https://doi.org/10.xxxx/xxxxx" target="_new" rel="noopener">https://doi.org/10.xxxx/xxxxx</a></p> </blockquote> <p><strong>Model documentation:</strong></p> <blockquote> <p><strong>Guaita, P. R., Marzuoli, R., & Gerosa, G.</strong> (2023).<br><em>A regional scale flux-based O₃ risk assessment for winter wheat in northern Italy, and effects of different spatio-temporal resolutions.</em><br><em>Environmental Pollution</em>, 333, 121860.<br><a href="https://doi.org/10.1016/j.envpol.2023.121860" target="_new" rel="noopener">https://doi.org/10.1016/j.envpol.2023.121860</a></p> </blockquote> <p><strong>Model code:</strong><br>See the GitHub repository <a href="https://github.com/prguaita/O3-Deposition-model-for-wheat"><em>O3-Deposition-model-for-wheat</em></a> (© 2025 Guaita & Gerosa. All rights reserved).</p> <p>⚠️ <strong>Warning:</strong><br>Do <strong>not</strong> cite the preprint <a href="https://egusphere.copernicus.org/preprints/2024/egusphere-2024-2573/?utm_source=chatgpt.com" target="_new" rel="noopener">https://egusphere.copernicus.org/preprints/2024/egusphere-2024-2573/</a> — this version is <strong>deprecated</strong>.</p>
Ozone concentrations and meteorological factors in coastal areas of China in July of 2015-2022
<p>Ozone concentrations and meteorological factors in four coastal areas of China (the coast of Fujian Province (c-FJ), the coast of Yangtze River Delta region (c-YRD), the coast of Shandong Peninsula (c-SD) and Beijing-Tianjin-Hebei (c-BTH) in July of 2015-2022. Totally 25 coastal cities were picked as typical cities representative of the studied regions (Ningde, Fuzhou, Putian, Quanzhou, Xiamen and Zhangzhou in c-FJ; Wenzhou, Taizhou, Ningbo, Zhoushan, Jiaxing, Shanghai, Nantong, Yancheng and Lianyungang in c-YRD; Rizhao, Qingdao, Weifang, Weihai, Yantai and Dongying in c-SD; Cangzhou, Tianjin, Tangshan and Qinhuangdao in c-BTH) and the data was averaged to obtain the O<sub>3</sub> concentrations and meteorological conditions regionally.</p>
Data from: Invasive herbaceous respond more negatively to elevated ozone concentration than native species
Open the record for dataset details and reuse information.
The Harmonized Atmospheric Ozone Column Concentration Dataset from 2005 to 2022 with OMI and Sentinel-5P TROPOMI products on the Tibetan Plateau
Open the record for dataset details and reuse information.
Ambient air ozone concentrations using electrochemical low-cost sensors: Italy and Austria, 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 Italy and Austria. Sensors are electrochemical. Data are calibrated using multiple linear regression, and validated against official reference data from each local air quality monitoring network.</p>
Ambient air ozone concentrations using electrochemical low-cost sensors: Italy and Austria, 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 Italy and Austria. Sensors are electrochemical. Data are calibrated using multiple linear regression, and validated against official reference data from each local air quality monitoring network.</p>
Ambient air ozone concentrations using electrochemical low-cost sensors: Italy and Austria, 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 Italy and Austria. Sensors are electrochemical. Data are calibrated using multiple linear regression, and validated against official reference data from each local air quality monitoring network.</p>
Pulmonary and Inflammatory Responses Following Exposure to a Low Concentration of Ozone or Clean Air at Rest
ClinicalTrials.gov study NCT06943989. IPD Sharing: NO. Countries: 1. Publications: 0.
Pulmonary and Inflammatory Responses Following Exposure to a Low Concentration of Ozone or Clean Air
ClinicalTrials.gov study NCT05680831. IPD Sharing: NO. Countries: 1. Publications: 0.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.