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17 results for “ozone pollution”
Source Data and ambient ozone dataset generated in "Substantially underestimated global health risks of current ozone pollution"
<p>Existing assessments might have underappreciated ozone-related health impacts worldwide. Here our study assesses current global ozone pollution using the high-resolution (0.05°) estimation from a geo-ensemble learning model, with key focuses on population exposure and all-cause mortality burden. Our model demonstrates strong performance, achieving a mean bias of less than -1.5 parts per billion against in-situ measurements. We estimate that 66.2% of the global population is exposed to excess ozone for short term (> 30 days per year), and 94.2% suffers from long-term exposure. Furthermore, severe ozone exposure levels are observed in Cropland areas, particularly over Asia. Importantly, the all-cause ozone-attributable deaths significantly surpass previous recognition from specific diseases worldwide. Notably, mid-latitude Asia (30°N) and the western United States show high mortality burden, contributing substantially to global ozone-attributable deaths. Our study highlights current significant global ozone-related health risks and may benefit the ozone-exposed population in the future.</p>
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>
Data for publication "COVID-19 lockdowns highlight a risk of increasing ozone pollution in European urban areas"
<p>Data to accompany the "COVID-19 lockdowns highlight a risk of increasing ozone pollution in European urban areas" paper published in Atmospheric Chemistry and Physics. This repository contains metadata for the ambient air quality and surface meteorological sites used for the analysis.</p> <p>The observations have not been included due to licencing issues, but can be shared by reasonable request to the author. </p> <p>All files are .csv files, with UTF-8 encoding, and are self describing.</p> <p> </p>
Data for "Impacts of ozone-vegetation interactions on ozone pollution episodes in North China and the Yangtze River Delta"
<p>Data for "Impacts of ozone-vegetation interactions on ozone pollution episodes in North China and the Yangtze River Delta"</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>
Supplemental data and code for Climate change will amplify the inequitable exposure to compound heatwave and ozone pollution
<p>Supplemental data and code for Climate change will amplify the inequitable exposure to compound heatwave and ozone pollution</p>
Impact of Marine Shipping Emissions on Ozone Pollution during the Warm Seasons in China
<ul> <li><strong>all.city.2017-01.nc: </strong>The hourly observations of six major air pollutants at 336 cities in China in 2017, including latitude and longitude information for these cities. These observations are provided by CNEMC.</li> <li><strong>china_CMAQ_appmap_no_gat.nc: </strong>The mask file for mainland China.</li> <li><strong>CMAQ_HCHO_VCDs.zip</strong>: Daily HCHO VCDs data calculated from CMAQ output and its calculation script.</li> <li><strong>conc_evaluation.zip</strong>: The code lists of the cities located in all coastal regions, BTH, YRD, and PRD region, respectively (<strong>XXXXXX_citycode.csv</strong>), the statistical results of MDA8 O3 and NO2 in these regions and corrsponding calulation scripts.</li> <li><strong>Data_era5.zip: </strong>All the ERA5 data used in the article (which have been extracted into nc files), including hourly meteorological parameters for Beijing-Tianjin-Hebei, the Yangtze River Delta, and the Pearl River Delta regions, respectively.</li> <li><strong>GRIDCRO2D_2017100100_d01:</strong></li> <li><strong>met_evaluation.zip: </strong>The information of the meteorological stations located in all coastal regions (<strong>MET_STATION_COASTAL.csv</strong>), The statistical results of T2, RH2, WS10 in these stations and corrsponding calulation scripts.</li> <li><strong>nest_vs_base_evaluation.zip: </strong>Statistical results of the simulation performance of the nested and basic experiments for April, containing mesh data for the nested inner domains, results for the models of the nested experiments, the scripts used to calculate the statistical results, and the results of the calculations.</li> <li><strong>OMIHCHOd_interpolate_data_control.nc: </strong>Daily HCHO VCDs data from OMHCHOd dataset spatially interpolated to have the same horizontal grid as the CMAQ results.</li> <li><strong>pattern_sa_polluted_clean.zip: type_stat_sa_newdef.ncl </strong>is to calculate ozone impacts of MSEs on polluted days and clean days under different weather patterns and the csv files are the results.</li> <li><strong>region_port_city.zip: </strong> The mask files for the three main regions, latitude/longitude for the 13 coastal port cities (<strong>port_city_lat_lon.csv</strong>), the results of seasonally averaged impacts of MSEs on ozone for the 3 regions and 13 cities (<strong>region_port_city_mda8o3_sa_rsa.csv</strong>), the ncl file with the same name is the script for the calculations.</li> <li><strong>sim_hourly_o3_no2_base.nc: </strong>The extracted data from the WRF-CMAQ results file, which includes modelled hourly surface ozone and NO2 concentrations for the simulation period.</li> <li><strong>sim_hourly_sa_o3_ocean_base.nc: </strong>The extracted data from the CMAQ-ISAM model simulation results and includes the hourly impact of marine shipping emissions of ozone obtained from the simulation.</li> <li><strong>T-PCA_BTH_YRD_PRD.zip: </strong>The daily weather pattern of Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD), and Pearl River Delta (PRD) regions obtained by the TPC-A method of classification, with 2 to 10 categories.</li> <li><strong>CJK_domain.ncl</strong> is used to produce the manuscript’s <strong>Figure 1</strong>.</li> <li><strong>sim_obs_dis_MDA8_uv_coastal.ncl </strong>is used to produce the manuscript’s <strong>Figure 2</strong>.</li> <li><strong>absolute_dis_MDA8h.ncl </strong>is used to produce the manuscript’s <strong>Figure 3</strong>.</li> <li><strong>region_city_o3_sa.ncl </strong>is used to produce the manuscript’s <strong>Figure 4</strong>.</li> <li><strong>type_mean_slp_o3_sa.ncl </strong>is used for the distribution maps in<strong> Figures 5, 6 and 7.</strong></li> <li><strong>bar_type_sa.ncl </strong>is used to produce of the bar charts in <strong>Figure 5</strong>, <strong>Figure 6</strong> and <strong>Figure 7</strong>, which were combined with the distribution maps obtained from <strong>type_mean_slp_o3_sa.ncl.</strong></li> <li><strong>cal_FDR_PCT.ncl </strong>is used to calculate the number of pairs of indistinguishable patterns obtained based on the FDR method for different number of classifications in the three regions (<strong>Figure S1</strong>).</li> <li><strong>OMI_xhcho.ncl </strong>is used to produce <strong>Figure S2</strong> in the Supplementary Materials.</li> <li><strong>MSEs_nox_voc_dis.ncl </strong>is used to produce <strong>Figure S3 </strong>in the Supplementary Materials.</li> <li><strong>relative_dis_DA8h.ncl </strong>is used to produce <strong>Figure S4</strong> in the Supplementary Materials.</li> <li><strong>region_city_o3_sa_rc.ncl </strong>is used to produce <strong>Figure S5</strong> in the Supplementary Materials.</li> <li><strong>type_mean_slp.ncl </strong>is used to produce <strong>Figure S6</strong> in the Supplementary Materials.</li> <li><strong>type_mean_met.ncl </strong>is used to produce <strong>Figures S7-S9</strong> in the Supplementary Materials, just replace the region name in this script.</li> <li><strong>stat_season_pattern.zip </strong>includes frequency of occurrence of the four synoptic patterns during three seasons, the ncl file is the script for the calculations, and the csv file is the output (<strong>Table S1</strong>).</li> </ul>
Data for "North China Plain as a hot spot of ozone pollution exacerbated by extreme high temperature"
<p>Data for "North China Plain as a hot spot of ozone pollution exacerbated by extreme high temperature"</p>
Data from: Joint ozone pollution and climate warming reduce yield but enhance grain protein content in a resistant wheat variety
Open the record for dataset details and reuse information.
Data for: Projecting changes in the frequency and magnitude of ozone pollution events under uncertain climate sensitivity
<p>Climate change is projected to worsen ozone pollution over many populated regions, with larger impacts at higher concentrations. More intense and frequent ozone episodes risk setbacks to human health and environmental policy achievements. However, assessing these changes is complicated by uncertain climate sensitivity, closely related to climate model response, and internal variability in simulations projecting climate's influence on air quality. Here, leveraging a global modeling framework that one-way couples a human activity model, an Earth system model of intermediate complexity, and an atmospheric chemistry model, we investigate the role of climate sensitivity in climate-induced changes to high ozone pollution episodes in the United States using multiple greenhouse gas emissions scenarios, representations of climate sensitivity, and initial condition members. We bias correct and evaluate historical model simulations, identifying modeled and observed O<sub>3</sub> episodes using extreme value theory, and extend the approach to projections of mid- and end-century climate impacts. Results show that the influence of climate sensitivity can be as significant as that of greenhouse gas emissions scenario absent precursor emissions changes. Climate change is projected to increase the magnitude of the highest annually occurring O<sub>3</sub> concentrations by over 2.3 ppb on average across the U.S. at mid-century under a high climate sensitivity and moderate emissions scenario, but the increase is limited to less than 0.3 ppb under lower climate sensitivity. Further, we show that areas in the U.S. currently meeting air quality standards risk being pushed into non-compliance due to a climate-induced increase in frequency of high ozone days.</p>
Effects of ozone air pollution on crop pollinators and pollination
<p>Article dataset.</p>
Ground-based observation data from the article " Elucidating the impacts of various atmospheric ventilation conditions on local and transboundary ozone pollution patterns: A case study of Beijing, China"
<p>Hourly ground-level O<sub>3</sub>, CO and NO<sub>2</sub> observational data were retrieved from the China Environmental Monitoring Station, ultimately comprising 714 stations in North China and 35 stations in Beijing after data quality control. The hourly data from meteorological stations in China were obtained from the China Meteorological Data Service Centre, including observational values of such weather elements as temperature, pressure, relative humidity, wind, total cloud cover, and precipitation.</p>
Investigation of the summer 2018 European ozone air pollution episodes using novel satellite data and modelling - Dataset
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Air Pollution Study: The Effect of Ozone on the Lung
ClinicalTrials.gov study NCT02673775. IPD Sharing: NO. Countries: 1. Publications: 0.
Data for: Projecting changes in the frequency and magnitude of ozone pollution events under uncertain climate sensitivity
Open the record for dataset details and reuse information.
Salbutamol Use in Ozone Air Pollution by People With Asthma and/or Exercise Induced Bronchoconstriction (EIB)
ClinicalTrials.gov study NCT05087693. IPD Sharing: NO. Countries: 1. Publications: 0.
Data from: Geographic sources of ozone air pollution and mortality burden in Europe
<p>This dataset contains all the maximum daily 8-hour mean O3 concentrations (MDA8 O3) presented in the paper entitled " Geographic sources of ozone air pollution and mortality burden in Europe". In this paper, the CMAQv5.0.2-ISAM was run to calculate the MDA8 O3 concentrations by country for the weeks 18-39 from 2015 to 2017 (approximately corresponding months from May to September). </p> <p>Within each tar file, you'll find the daily MDA8 O3 concentrations, presenting the contributions from 35 individual countries, contributions from seas and oceans (SEA), contributions from neighboring countries within the simulation domain (NOEU35), and contributions from the boundaries of the simulation domain (BCON) for each respective year. Each netcdf file follows the nomenclature "sconco3_Countrycode_Day.nc" , where day "1" corresponds to the first day of the week 18 of each year and the country codes are: Albania (AL), Austria (AT), Belgium (BE), Bulgaria (BG), Switzerland (CH), Cyprus (CY), Czechia (CZ), Germany (DE), Denmark (DK), Estonia (EE), Greece (EL), Spain (ES), Finland (FI), France (FR), Croatia (HR), Hungary (HU), Ireland (IE), Iceland (IS), Italy (IT), Liechtenstein (LI), Lithuania (LT), Luxembourg (LU), Latvia (LV), Montenegro (ME), Malta (MT), Netherlands (NL), Norway (NO), Poland (PL), Portugal (PT), Romania (RO), Serbia (RS), Sweden (SE), Slovenia (SI), Slovakia (SK), United Kingdom (UK).</p> <p> </p> <p>Acknowledgments</p> <p>BSC co-authors acknowledge support Ministerio para la Transición Ecológica y el Reto Demográfico (MITECO) as part of the Plan Nacional del Ozono project (BOE-A-2021-20183), as well as through the VITALISE project (PID2019-108086RA-I00, MCIN/AEI/10.13039/501100011033) funded by the Agencia Estatal de Investigacion (AEI). We also acknowledge the AXA Research Fund and Red Temática ACTRIS España (CGL2017-90884-REDT), and H2020 ACTRIS IMP (No 871115), the Department of Research and Universities of the Government of Catalonia through the Atmospheric Composition Research Group (code 2021 SGR 01550).</p> <p> </p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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International Brain Laboratory public data
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OpenNeuro
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