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1. The risk of fibromyalgia in patients with iron deficiency anemia: a nationwide population-based cohort study

Author

Wei-Cheng Yao, Hsuan-Ju Chen, Kam-Hang Leong, Kai-Lan Chang, Yu-Ting Tina Wang, Li-Chin Wu, Po-Ya Tung, Chien-Feng Kuo, Che-Chen Lin, and Shin-Yi Tsai

Subjects
Medicine, Science
Abstract

Abstract Since iron is essential for neurotransmitter synthesis, decreased iron stores might lead to reduced production of biogenic amines which phenomenon was shown in Fibromyalgia (FM) patients. The aims are to investigate the association of iron deficiency anemia (IDA) and FM and to find the effects of different interventions. We conducted a study using the Taiwan National Health Insurance Research Database. The IDA cohort consisted of 13,381 patients with newly diagnosed IDA between 2000 and 2008. Each patient with IDA was frequency-matched with one people without IDA, by sex, age and index year. The Cox proportional hazards regression analysis was conducted to estimate the association between IDA and FM risk. The event was the occurrence of FM. The overall incidence density rate of FM in the IDA cohort was higher than in the non-IDA cohort with a multivariable Cox proportional hazards model measured adjusted hazard ratio [HR], 1.19; 95% confidence interval [CI], 1.13–1.25). When using non-IDA group as reference, we compared with different therapies for IDA. The adjusted HRs of FM were 1.38 (95% CI = 1.30–1.47), 1.10 (95% CI = 1.03–1.16), 1.18 (95% CI = 0.98–1.43) and 0.73 (95% CI = 0.58–0.90) for IDA patient without therapy, iron supplement alone, blood transfusion alone and both iron supplement and blood transfusion respectively. Our results suggest IDA is associated with an increased risk of FM. All patients should have iron supplementation both to correct anemia and replenish body stores.

Published
2021
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2. Impact of the COVID‐19 Economic Downturn on Tropospheric Ozone Trends: An Uncertainty Weighted Data Synthesis for Quantifying Regional Anomalies Above Western North America and Europe

Author

Kai‐Lan Chang, Owen R. Cooper, Audrey Gaudel, Marc Allaart, Gerard Ancellet, Hannah Clark, Sophie Godin‐Beekmann, Thierry Leblanc, Roeland Van Malderen, Philippe Nédélec, Irina Petropavlovskikh, Wolfgang Steinbrecht, René Stübi, David W. Tarasick, and Carlos Torres

Subjects
regional ozone anomalies, COVID‐19, data fusion, Geology, QE1-996.5, Geophysics. Cosmic physics, QC801-809
Abstract

Abstract This study quantifies the association between the COVID‐19 economic downturn and 2020 tropospheric ozone anomalies above Europe and western North America, and their impact on long‐term trends. Anomaly detection for an atmospheric time series is usually carried out by identifying potentially aberrant data points relative to climatological values. However, detecting ozone anomalies from sparsely sampled ozonesonde profiles (once per week at most sites) is challenging due to ozone's high temporal variability. We first demonstrate the challenges for summarizing regional trends based on independent time series from multiple nearby ozone profiling stations. We then propose a novel regional‐scale anomaly detection framework based on generalized additive mixed models, which accounts for the sampling frequency and inherent data uncertainty associated with each vertical profile data set, measured by ozonesondes, lidar or commercial aircraft. This method produces a long‐term monthly time series with high vertical resolution that reports ozone anomalies from the surface to the middle‐stratosphere under a unified framework, which can be used to quantify the regional‐scale ozone anomalies during the COVID‐19 economic downturn. By incorporating extensive commercial aircraft data and frequently sampled ozonesonde profiles above Europe, we show that the complex interannual variability of ozone can be adequately captured by our modeling approach. The results show that free tropospheric ozone negative anomalies in 2020 are the most profound since the benchmark year of 1994 for both Europe and western North America, and positive trends over 1994–2019 are diminished in both regions by the 2020 anomalies.

Published
2022
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4. Multi-decadal surface ozone trends at globally distributed remote locations

Author

Owen R. Cooper, Martin G. Schultz, Sabine Schroeder, Kai-Lan Chang, Audrey Gaudel, Gerardo Carbajal Benítez, Emilio Cuevas, Marina Fröhlich, Ian E. Galbally, Suzie Molloy, Dagmar Kubistin, Xiao Lu, Audra McClure-Begley, Philippe Nédélec, Jason O'Brien, Samuel J. Oltmans, Irina Petropavlovskikh, Ludwig Ries, Irina Senik, Karin Sjöberg, Sverre Solberg, Gerard T. Spain, Wolfgang Spangl, Martin Steinbacher, David Tarasick, Valerie Thouret, and Xiaobin Xu

Subjects
tropospheric ozone, trends, global change, trace gas, Environmental sciences, GE1-350
Abstract

Extracting globally representative trend information from lower tropospheric ozone observations is extremely difficult due to the highly variable distribution and interannual variability of ozone, and the ongoing shift of ozone precursor emissions from high latitudes to low latitudes. Here we report surface ozone trends at 27 globally distributed remote locations (20 in the Northern Hemisphere, 7 in the Southern Hemisphere), focusing on continuous time series that extend from the present back to at least 1995. While these sites are only representative of less than 25% of the global surface area, this analysis provides a range of regional long-term ozone trends for the evaluation of global chemistry-climate models. Trends are based on monthly mean ozone anomalies, and all sites have at least 20 years of data, which improves the likelihood that a robust trend value is due to changes in ozone precursor emissions and/or forced climate change rather than naturally occurring climate variability. Since 1995, the Northern Hemisphere sites are nearly evenly split between positive and negative ozone trends, while 5 of 7 Southern Hemisphere sites have positive trends. Positive trends are in the range of 0.5-2 ppbv decade-1, with ozone increasing at Mauna Loa by roughly 50% since the late 1950s. Two high elevation Alpine sites, discussed by previous assessments, exhibit decreasing ozone trends in contrast to the positive trend observed by IAGOS commercial aircraft in the European lower free-troposphere. The Alpine sites frequently sample polluted European boundary layer air, especially in summer, and can only be representative of lower free tropospheric ozone if the data are carefully filtered to avoid boundary layer air. The highly variable ozone trends at these 27 surface sites are not necessarily indicative of free tropospheric trends, which have been overwhelmingly positive since the mid-1990s, as shown by recent studies of ozonesonde and aircraft observations.

Published
2020
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5. Estimates of ozone concentrations and attributable mortality in urban, peri-urban and rural areas worldwide in 2019

Author

Daniel A Malashock, Marissa N DeLang, Jacob S Becker, Marc L Serre, J Jason West, Kai-Lan Chang, Owen R Cooper, and Susan C Anenberg

Subjects
ozone, air pollution, disease burden, mortality, urban, suburban, Environmental technology. Sanitary engineering, TD1-1066, Environmental sciences, GE1-350, Science, Physics, QC1-999
Abstract

City-level estimates of ambient ozone concentrations and associated disease burdens are sparsely available, especially for low and middle-income countries. Recently available high-resolution gridded global ozone concentration estimates allow for estimating ozone concentrations and mortality at urban scales and for urban-rural catchment areas worldwide. We applied existing fine resolution global surface ozone estimates, developed by integrating observations (8834 sites globally) with nine atmospheric chemistry models, in an epidemiologically-derived health impact function to estimate chronic respiratory disease mortality worldwide in 2019. We compared ozone season daily maximum 8 h mixing ratio concentrations and ozone-attributable mortality for urban areas worldwide (including cities and densely-populated towns), and their surrounding peri-urban, peri-rural, and rural areas. In 2019, population-weighted mean ozone among all urban-rural catchment areas was greatest in peri-urban areas (52 ppb), followed by urban areas (cities and towns; 49 ppb). Of 423 100 estimated global ozone-attributable deaths, 37% (147 100) occurred in urban areas, where 40% of the world’s population resides, and 56% (254 000) occurred in peri-urban areas (

Published
2022
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6. Tropospheric Ozone Assessment Report: Tropospheric ozone from 1877 to 2016, observed levels, trends and uncertainties

Author

David Tarasick, Ian E. Galbally, Owen R. Cooper, Martin G. Schultz, Gerard Ancellet, Thierry Leblanc, Timothy J. Wallington, Jerry Ziemke, Xiong Liu, Martin Steinbacher, Johannes Staehelin, Corinne Vigouroux, James W. Hannigan, Omaira García, Gilles Foret, Prodromos Zanis, Elizabeth Weatherhead, Irina Petropavlovskikh, Helen Worden, Mohammed Osman, Jane Liu, Kai-Lan Chang, Audrey Gaudel, Meiyun Lin, Maria Granados-Muñoz, Anne M. Thompson, Samuel J. Oltmans, Juan Cuesta, Gaelle Dufour, Valerie Thouret, Birgit Hassler, Thomas Trickl, and Jessica L. Neu

Subjects
ozone, troposphere, measurements, trends, historical, climate, Environmental sciences, GE1-350
Abstract

From the earliest observations of ozone in the lower atmosphere in the 19th century, both measurement methods and the portion of the globe observed have evolved and changed. These methods have different uncertainties and biases, and the data records differ with respect to coverage (space and time), information content, and representativeness. In this study, various ozone measurement methods and ozone datasets are reviewed and selected for inclusion in the historical record of background ozone levels, based on relationship of the measurement technique to the modern UV absorption standard, absence of interfering pollutants, representativeness of the well-mixed boundary layer and expert judgement of their credibility. There are significant uncertainties with the 19th and early 20th-century measurements related to interference of other gases. Spectroscopic methods applied before 1960 have likely underestimated ozone by as much as 11% at the surface and by about 24% in the free troposphere, due to the use of differing ozone absorption coefficients. There is no unambiguous evidence in the measurement record back to 1896 that typical mid-latitude background surface ozone values were below about 20 nmol mol–1, but there is robust evidence for increases in the temperate and polar regions of the northern hemisphere of 30–70%, with large uncertainty, between the period of historic observations, 1896–1975, and the modern period (1990–2014). Independent historical observations from balloons and aircraft indicate similar changes in the free troposphere. Changes in the southern hemisphere are much less. Regional representativeness of the available observations remains a potential source of large errors, which are difficult to quantify. The great majority of validation and intercomparison studies of free tropospheric ozone measurement methods use ECC ozonesondes as reference. Compared to UV-absorption measurements they show a modest (~1–5% ±5%) high bias in the troposphere, but no evidence of a change with time. Umkehr, lidar, and FTIR methods all show modest low biases relative to ECCs, and so, using ECC sondes as a transfer standard, all appear to agree to within one standard deviation with the modern UV-absorption standard. Other sonde types show an increase of 5–20% in sensitivity to tropospheric ozone from 1970–1995. Biases and standard deviations of satellite retrieval comparisons are often 2–3 times larger than those of other free tropospheric measurements. The lack of information on temporal changes of bias for satellite measurements of tropospheric ozone is an area of concern for long-term trend studies.

Published
2019
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7. Regional trend analysis of surface ozone observations from monitoring networks in eastern North America, Europe and East Asia

Author

Kai-Lan Chang, Irina Petropavlovskikh, Owen R. Cooper, Martin G. Schultz, and Tao Wang

Subjects
tropospheric ozone, regional variation, irregular monitoring network, space-time model, Environmental sciences, GE1-350
Abstract

Surface ozone is a greenhouse gas and pollutant detrimental to human health and crop and ecosystem productivity. The Tropospheric Ozone Assessment Report (TOAR) is designed to provide the research community with an up-to-date observation-based overview of tropospheric ozone’s global distribution and trends. The TOAR Surface Ozone Database contains ozone metrics at thousands of monitoring sites around the world, densely clustered across mid-latitude North America, western Europe and East Asia. Calculating regional ozone trends across these locations is challenging due to the uneven spacing of the monitoring sites across urban and rural areas. To meet this challenge we conducted a spatial and temporal trend analysis of several TOAR ozone metrics across these three regions for summertime (April–September) 2000–2014, using the generalized additive mixed model (GAMM). Our analysis indicates that East Asia has the greatest human and plant exposure to ozone pollution among investigating regions, with increasing ozone levels through 2014. The results also show that ozone mixing ratios continue to decline significantly over eastern North America and Europe, however, there is less evidence for decreases of daytime average ozone at urban sites. The present-day spatial coverage of ozone monitors in East Asia (South Korea and Japan) and eastern North America is adequate for estimating regional trends by simply taking the average of the individual trends at each site. However the European network is more sparsely populated across its northern and eastern regions and therefore a simple average of the individual trends at each site does not yield an accurate regional trend. This analysis demonstrates that the GAMM technique can be used to assess the regional representativeness of existing monitoring networks, indicating those networks for which a regional trend can be obtained by simply averaging the trends of all individual sites and those networks that require a more sophisticated statistical approach.

Published
2017
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8. Using Regionalized Air Quality Model Performance and Bayesian Maximum Entropy Data Fusion to Map Global Surface Ozone Concentration and Associated Uncertainty

Author

Jacob S. Becker, Marissa N. DeLang, Kai-Lan Chang, Marc L. Serre, Owen R. Cooper, Hantao Wang, Martin G. Schultz, Sabine Schröder, Xiao Lu, Lin Zhang, Makoto Deushi, Beatrice Josse, Christoph A. Keller, Jean-Francois Lamarque, Meiyun Lin, Junhua Liu, Virginie Marécal, Sarah A. Strode, Kengo Sudo, Simone Tilmes, Li Zhang, Michael Brauer, and J. Jason West

Subjects
Environment Pollution, Earth Resources and Remote Sensing
Abstract

Estimates of ground-level ozone concentrations have been improved through data fusion of observations and atmospheric chemistry models. Our previous global ozone estimates for the Global Burden of Disease study corrected for bias uniformly across continents and then corrected near monitoring stations using the Bayesian Maximum Entropy (BME) framework for data fusion. Here, we use the Regionalized Air Quality Model Performance (RAMP) framework to correct model bias over a much larger spatial range than BME can, accounting for the spatial inhomogeneity of bias and nonlinearity as a function of modeled ozone. RAMP bias correction is applied to a composite of 9 global chemistry-climate models, based on the nearest set of monitors. These estimates are then fused with observations using BME, which matches observations at measurement stations, with the influence of observations declining with distance in space and time. We create global ozone maps for each year from 1990 to 2017 at fine spatial resolution. RAMP is shown to create unrealistic discontinuities due to the spatial clustering of ozone monitors, which we overcome by applying a weighting for RAMP based on the number of monitors nearby. Incorporating RAMP before BME has little effect on model performance near stations, but strongly increases R2 by 0.15 at locations farther from stations, shown through a checkerboard cross-validation. Corrections to estimates differ based on location in space and time, confirming heterogeneity. We quantify the likelihood of exceeding selected ozone levels, finding that parts of the Middle East, India, and China are most likely to exceed 55 parts per billion (ppb) in 2017. About 96% of the global population was exposed to ozone levels above the World Health Organization guideline of 60 µg m−3 (30 ppb) in 2017. Our annual fine-resolution ozone estimates may be useful for several applications including epidemiology and assessments of impacts on health, agriculture, and ecosystems.

Published
2023
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9. Trend Detection of Atmospheric Time Series: Incorporating Appropriate Uncertainty Estimates and Handling Extreme Events

Author

Kai-Lan Chang, Martin G Schultz, Xin Lan, Audra McClure-Begley, Irina Petropavlovskikh, Xiaobin Xu, and Jerry R Ziemke

Subjects
Geophysics
Abstract

This paper is aimed at atmospheric scientists without formal training in statistical theory. Its goal is to, 1) provide a critical review of the rationale for trend analysis of the time series typically encountered in the field of atmospheric chemistry; 2) describe a range of trend-detection methods; and 3) demonstrate effective means of conveying the results to a general audience. Trend detections in atmospheric chemical composition data are often challenged by a variety of sources of uncertainty, which often behave differently to other environmental phenomena such as temperature, precipitation rate, or stream flow, and may require specific methods depending on the science questions to be addressed. Some sources of uncertainty can be explicitly included in the model specification, such as autocorrelation and seasonality, but some inherent uncertainties are difficult to quantify, such as data heterogeneity and measurement uncertainty due to the combined effect of short- and long-term natural variability, instrumental stability, and aggregation of data from sparse sampling frequency. Failure to account for these uncertainties might result in an inappropriate inference of the trends and their estimation errors. On the other hand, the variation in extreme events might be interesting for different scientific questions, for example, the frequency of extremely high surface ozone events and their relevance to human health. In this study we aim to, 1) review trend detection methods for addressing different levels of data complexity in different chemical species; 2) demonstrate that the incorporation of scientifically interpretable covariates can outperform pure numerical curve fitting techniques in terms of uncertainty reduction and improved predictability; 3) illustrate the study of trends based on extreme quantiles that can provide insight beyond standard mean or median based trend estimates; and 4) present an advanced method of quantifying regional trends based on the inter-site correlations of multi-site data. All demonstrations are based on time series of observed trace gases relevant to atmospheric chemistry, but the methods can be applied to other environmental data sets.

Published
2021
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10. Mapping Yearly Fine Resolution Global Surface Ozone through the Bayesian Maximum Entropy Data Fusion of Observations and Model Output for 1990–2017

Author

Marissa N. DeLang, Jacob S. Becker, Kai-Lan Chang, Marc L. Serre, Owen R. Cooper, Martin G. Schultz, Sabine Schröder, Xiao Lu, Lin Zhang, Makoto Deushi, Beatrice Josse, Christoph A. Keller, Jean-François Lamarque, Meiyun Lin, Junhua Liu, Virginie Marécal, Sarah A. Strode, Kengo Sudo, Simone Tilmes, Li Zhang, Stephanie E. Cleland, Elyssa L. Collins, Michael Brauer, and J. Jason West

Subjects
Meteorology And Climatology
Abstract

Estimates of ground-level ozone concentrations are necessary to determine the human health burden of ozone. To support the Global Burden of Disease Study, we produce yearly fine resolution global surface ozone estimates from 1990 to 2017 through a data fusion of observations and models. As ozone observations are sparse in many populated regions, we use a novel combination of the M3Fusion and Bayesian Maximum Entropy (BME) methods. With M3Fusion, we create a multi-model composite by bias-correcting and weighting nine global atmospheric chemistry models based on their ability to predict observations (8,834 sites globally)in each region and year. BME is then used to integrate observations, such that estimates match observations at each monitoring site with the observational influence decreasing smoothly across space and time until the output matches the multi-model composite. After estimating at 0.5° resolution using BME, we add fine spatial detail from an additional model, yielding estimates at 0.1° resolution. Observed ozone is predicted more accurately (R2=0.81 at test point, 0.63 at 0.1°,0.62 at 0.5°) than the multi-model mean (R2=0.28 at 0.5°). Global ozone exposure is estimated to be increasing, driven by highly populated regions of Asia and Africa, despite decreases in the United States and Russia.

Published
2021
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11. Tropical tropospheric ozone distribution and trends from in situ and satellite data.

Author

Gaudel, Audrey, Bourgeois, Ilann, Meng Li, Kai-Lan Chang, Ziemke, Jerald, Sauvage, Bastien, Stauffer, Ryan M., Thompson, Anne M., Kollonige, Debra E., Smith, Nadia, Hubert, Daan, Keppens, Arno, Cuesta, Juan, Heue, Klaus-Peter, Veefkind, Pepijn, Aikin, Kenneth, Peischl, Jeff, Thompson, Chelsea R., Ryerson, Thomas B., and Frost, Gregory J.

Abstract

Tropical tropospheric ozone (TTO) is important for the global radiation budget because the longwave radiative effect of tropospheric ozone is higher in the tropics than mid-latitudes. In recent decades the TTO burden has increased, partly due to the ongoing shift of ozone precursor emissions from mid-latitude regions toward the equator. In this study, we assess the distribution and trends of TTO using ozone profiles measured by high quality in situ instruments from the IAGOS (In-Service Aircraft for a Global Observing System) commercial aircraft, the SHADOZ (Southern Hemisphere ADditional OZonesondes) network, and the ATom (Atmospheric Tomographic Mission) aircraft campaign, as well as six satellite records reporting tropical tropospheric column ozone (TTCO): TROPOMI, OMI, OMI/MLS, OMPS/MERRA2, CrIS, and IASI/GOME2. With greater availability of ozone profiles across the tropics we can now demonstrate that tropical India is among the most polluted regions (e.g., Western Africa, tropical South Atlantic, Southeast Asia, Malaysia/Indonesia) with present-day 95th percentile ozone values reaching 80 nmol mol-1 in the lower free troposphere, comparable to mid-latitude regions such as Northeast China/Korea. In situ observations show that TTO increased between 1994 and 2019, with the largest mid- and upper tropospheric increases above India, Southeast Asia and Malaysia/Indonesia (from 3.4 ± 0.8 to 6.8 ± 1.8 nmol mol-1 decade-1), reaching 11 ± 2.4 and 8 ± 0.8 nmol mol-1 decade-1 close to the surface (India and Malaysia/Indonesia, respectively). The longest continuous satellite records only span 2004-2019, but also show increasing ozone across the tropics when their full sampling is considered, with maximum trends over Southeast Asia of 2.31 ± 1.34 nmol mol-1 decade-1 (OMI) and 1.69 ± 0.89 nmol mol-1 decade-1 (OMI/MLS). In general, the sparsely sampled aircraft and ozonesonde records do not detect the 2004-2019 ozone increase, which could be due to the genuine trends on this timescale being masked by the additional uncertainty resulting from sparse sampling. The fact that the sign of the trends detected with satellite records changes above three IAGOS regions, when their sampling frequency is limited to that of the in situ observations, demonstrates the limitations of sparse in situ sampling strategies. This study exposes the need to maintain and develop high frequency continuous observations (in situ and remote sensing) above the tropical Pacific Ocean, the Indian Ocean, Western Africa and South Asia in order to estimate accurate and precise ozone trends for these regions. In contrast, Southeast Asia and Malaysia/Indonesia are regions with such strong increases of ozone that the current in situ sampling frequency is adequate to detect the trends on a relatively short 15-year time scale. [ABSTRACT FROM AUTHOR]

Published
2024
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12. Technical note: Challenges of detecting free tropospheric ozone trends in a sparsely sampled environment.

Author

Kai-Lan Chang, Cooper, Owen R., Gaudel, Audrey, Petropavlovskikh, Irina, Effertz, Peter, Morris, Gary, and McDonald, Brian C.

Abstract

High quality long-term observational records are essential to ensure appropriate and reliable trend detection of tropospheric ozone. However, the necessity for maintaining high sampling frequency, in addition to continuity, is often under-appreciated. A common assumption is that so long as long-term records (e.g., span of a few decades) are available, (1) the estimated trends are accurate and precise, and (2) the impact of small-scale variability (e.g., weather) can be eliminated. In this study we show that the undercoverage bias (e.g., a type of sampling error resulting from statistical inference based on 5 sparse or insufficient samples, such as once-per-week sampling frequency) can persistently reduce the trend accuracy of free tropospheric ozone, even if multi-decadal time series are considered. We use over 40 years of nighttime ozone observations measured at Mauna Loa, Hawaii (representative of the lower free troposphere) to make this demonstration, and quantify the bias in monthly means and trends under different sampling strategies. We also show that short-term meteorological variability remains a cause for an inflated long-term trend uncertainty. To improve the trend precision and accuracy due to sampling 10 bias, two remedies are proposed: (1) a data variability attribution of colocated meteorological influence can efficiently reduce estimation uncertainty and moderately reduce the impact of sparse sampling; and (2) an adaptive sampling strategy based on anomaly detection enables us to greatly reduce the sampling bias, and produce more accurate trends using fewer samples compared to an intense regular sampling strategy. [ABSTRACT FROM AUTHOR]

Published
2024
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13. Global Climate

Author

Robert J. H. Dunn, Freya Aldred, Nadine Gobron, John B. Miller, Kate M. Willett, Melanie Ades, Robert Adler, R. P. Allan, John Anderson, Orlane Anneville, Yasuyuki Aono, Anthony Argüez, Carlo Arosio, John A. Augustine, Cesar Azorin-Molina, Jonathan Barichivich, Aman Basu, Hylke E. Beck, Nicolas Bellouin, Angela Benedetti, Kevin Blagrave, Stephen Blenkinsop, Olivier Bock, Xavier Bodin, Michael G. Bosilovich, Olivier Boucher, Gerald Bove, Dennis Buechler, Stefan A. Buehler, Laura Carrea, Kai-Lan Chang, Hanne H. Christiansen, John R. Christy, Eui-Seok Chung, Laura M. Ciasto, Melanie Coldewey-Egbers, Owen R. Cooper, Richard C. Cornes, Curt Covey, Thomas Cropper, Molly Crotwell, Diego Cusicanqui, Sean M. Davis, Richard A. M. de Jeu, Doug Degenstein, Reynald Delaloye, Markus G. Donat, Wouter A. Dorigo, Imke Durre, Geoff S. Dutton, Gregory Duveiller, James W. Elkins, Thomas W. Estilow, Nava Fedaeff, David Fereday, Vitali E. Fioletov, Johannes Flemming, Michael J. Foster, Stacey M. Frith, Lucien Froidevaux, Martin Füllekrug, Judith Garforth, Jay Garg, Matthew Gentry, Steven Goodman, Qiqi Gou, Nikolay Granin, Mauro Guglielmin, Sebastian Hahn, Leopold Haimberger, Brad D. Hall, Ian Harris, Debbie L. Hemming, Martin Hirschi, Shu-pen (Ben) Ho, Robert Holzworth, Filip Hrbáček, Daan Hubert, Petra Hulsman, Dale F. Hurst, Antje Inness, Ketil Isaksen, Viju O. John, Philip D. Jones, Robert Junod, Andreas Kääb, Johannes W. Kaiser, Viktor Kaufmann, Andreas Kellerer-Pirklbauer, Elizabeth C. Kent, Richard Kidd, Hyungiun Kim, Zak Kipling, Akash Koppa, Jan Henning L’Abée-Lund, Xin Lan, Kathleen O. Lantz, David Lavers, Norman G. Loeb, Diego Loyola, Remi Madelon, Hilmar J. Malmquist, Wlodzimierz Marszelewski, Michael Mayer, Matthew F. McCabe, Tim R. McVicar, Carl A. Mears, Annette Menzel, Christopher J. Merchant, Diego G. Miralles, Stephen A. Montzka, Colin Morice, Leander Mösinger, Jens Mühle, Julien P. Nicolas, Jeannette Noetzli, Tiina Nõges, Ben Noll, John O’Keefe, Tim J. Osborn, Taejin Park, Cecile Pellet, Maury S. Pelto, Sarah E. Perkins-Kirkpatrick, Coda Phillips, Stephen Po-Chedley, Lorenzo Polvani, Wolfgang Preimesberger, Colin Price, Merja Pulkkanen, Dominik G. Rains, William J. Randel, Samuel Rémy, Lucrezia Ricciardulli, Andrew D. Richardson, David A. Robinson, Matthew Rodell, Nemesio J. Rodríguez-Fernández, Karen H. Rosenlof, Chris Roth, Alexei Rozanov, This Rutishäuser, Ahira Sánchez-Lugo, Parnchai Sawaengphokhai, Verena Schenzinger, Robert W. Schlegel, Udo Schneider, Sapna Sharma, Lei Shi, Adrian J. Simmons, Carolina Siso, Sharon L. Smith, Brian J. Soden, Viktoria Sofieva, Tim H. Sparks, Paul W. Stackhouse, Ryan Stauffer, Wolfgang Steinbrecht, Andrea K. Steiner, Kenton Stewart, Pietro Stradiotti, Dimitri A. Streletskiy, Hagen Telg, Stephen J. Thackeray, Emmanuel Thibert, Michael Todt, Daisuke Tokuda, Kleareti Tourpali, Mari R. Tye, Ronald van der A, Robin van der Schalie, Gerard van der Schrier, Mendy van der Vliet, Guido R. van der Werf, Arnold. van Vliet, Jean-Paul Vernier, Isaac J. Vimont, Katrina Virts, Sebastiàn Vivero, Holger Vömel, Russell S. Vose, Ray H. J. Wang, Markus Weber, David Wiese, Jeanette D. Wild, Earle Williams, Takmeng Wong, R. I. Woolway, Xungang Yin, Ye Yuan, Lin Zhao, Xinjia Zhou, Jerry R. Ziemke, Markus Ziese, Ruxandra M. Zotta, Natural Environment Research Council (UK), European Commission, Department of Energy (US)an), Estonian Research Council, National Research Foundation of Korea, European Research Council, King Abdullah University of Science and Technology, Ministerio de Ciencia, Innovación y Universidades (España), Agencia Estatal de Investigación (España), Fundación BBVA, Royal Society (UK), and NASA Astrobiology Institute (US)

Subjects
Atmospheric Science
Abstract

© Copyright 2022. Sociedad Meteorológica Estadounidense (AMS). Para obtener permiso para reutilizar cualquier parte de este Trabajo, comuníquese con permisos@ametsoc. org . Cualquier uso del material en este Trabajo que se determine como "uso justo" según la Sección 107 de la Ley de derechos de autor de EE. UU. (17 Código de EE. UU. § 107) o que cumpla las condiciones especificadas en la Sección 108 de la Ley de derechos de autor de EE. ) no requiere el permiso de la AMS. La republicación, la reproducción sistemática, la publicación en forma electrónica, como en un sitio web o en una base de datos de búsqueda, u otros usos de este material, excepto los exentos de la declaración anterior, requieren un permiso por escrito o una licencia de la AMS. Todas las publicaciones periódicas y monográficas de AMS están registradas en el Centro de Autorización de Derechos de Autor (https://www.copyright.com ). Se proporcionan detalles adicionales en la declaración de política de derechos de autor de AMS, disponible en el sitio web de AMS ( https://www.ametsoc.org/PUBSCopyrightPolicy ) ., In 2021, both social and economic activities began to return towards the levels preceding the COVID-19 pandemic for some parts of the globe, with others still experiencing restrictions. Meanwhile, the climate has continued to respond to the ongoing increase in greenhouse gases and resulting warming. La Niña, a phenomenon which tends to depress global temperatures while changing rainfall patterns in many regions, prevailed for all but two months of the year. Despite this, 2021 was one of the six-warmest years on record as measured by global mean surface temperature with an anomaly of between +0.21° and +0.28°C above the 1991–2020 climatology., Lake surface water temperatures from satellite data have been generated within the GloboLakes project funded by the UK National Environment Research Council (NE/J023345/2), with extensions funded by the EU Copernicus Climate Change Service (C3S) programme...

Published
2022

14. The global distribution and trends of ozone health-based metrics: New results from the TOAR-II Database

Author

Owen Cooper, Kai-Lan Chang, Sabine Schröder, Niklas Selke, J. Jason West, and Marc Serre

Abstract

The first phase of the Tropospheric Ozone Assessment Report (TOAR-I), an activity of the International Global Atmospheric Chemistry Project (IGAC), provided the first comprehensive view of surface ozone’s global distribution and trends, based on all available surface ozone observations. TOAR-I focused on a present-day period of 2010-2014, and calculated trends for a range of periods, but primarily focused on the most recent years of 2000-2014, plus long-term trends from the 1970s/1980s through 2014. Subsequent studies of ozone trends using data after the TOAR-I cut-off of 2014, have shown a wide range of trends, both positive and negative, at monitoring sites around the world. To keep up with the rapid changes of ozone at urban, rural and remote locations this study provides current world-wide ozone trends using observations through 2021, archived in the newly updated TOAR-II Database of Surface Observations. Focus is placed on two ozone metrics relevant to human health impacts: 1) the annual peak of the 6-month running mean of maximum daily 8-hour average ozone; this metric is used by Global Burden of Disease (GBD) to estimate mortality due to long-term ozone exposure; 2) the number of days per year that exceed 70 ppbv, based on the maximum daily 8-hour average ozone value; this value corresponds to the primary U.S. National Ambient Air Quality Standards for ozone and is relevant to short-term ozone exposure. Global maps will indicate the regions of the world where the potential for ozone impacts on human health are greatest (and least), and will show regions where ozone air quality is either improving or degrading. Despite our effort to use all available surface ozone observations, large data gaps exist across many regions of the world, especially in developing nations, and GBD maps generated by data fusion will be used to identify, and to estimate ozone levels in the data-poor regions.

Published
2023

15. Decreasing trends in extreme ozone events across the United States

Author

Kai-Lan Chang and Owen Cooper

Abstract

This work presents a comprehensive regional trend analysis of surface ozone observations across the contiguous United States, using 25 years of data from 62 rural monitoring sites, and based on quantile regression and change point analysis. We highlight that the ozone exceedance events (based on the daily maximum 8-hour average and a threshold of 70 ppb) in recent years (2017-2021) are largely diminished compared to two decades ago (1995-2003) in the eastern US and across much of the western US. Ozone exceedance events in recent years are mainly observed in California during the fire season. Results based on thresholds of 60, 50 and 35 ppb will also be discussed.

Published
2023

16. Fingerprints of the COVID-19 economic downturn and recovery on ozone anomalies at remote high-altitude sites in North America and Western Europe

Author

Davide Putero, Owen R. Cooper, Gregory Beachley, Kai-Lan Chang, Cédric Couret, Peter Effertz, Dan Jaffe, Jason Lynch, Irina Petropavlovskikh, Melissa Puchalski, Timothy Sharac, Barkley C. Sive, Martin Steinbacher, Carlos Torres, and Paolo Cristofanelli

Abstract

With a few exceptions, most of the studies on tropospheric ozone (O3) variability following the restriction measures related to the spread of COVID-19 focused on emissions-prone or urban environments. In this work, we investigated the impact of the pandemic restriction measures on surface O3 at several high-altitude remote and rural sites across North America and Western Europe. O3 monthly anomalies, computed with respect to the baseline period 2000–2019, were calculated for 2020 and 2021, to explore the impact of the economic downturn in 2020 and the economic recovery in 2021. A total of 41 high-altitude sites were analyzed: 5 remote stations in western Europe, 19 rural sites in the western US, 4 sites in the western US downwind of highly polluted areas, 4 sites in the eastern US, plus 9 remote sites across the globe to provide a "global" picture for comparison. In 2020, most of European high-altitude sites showed persistent negative anomalies for spring (March-May, i.e., MAM; average of -1.8 ppb) and summer (June-August, i.e., JJA; -2.7 ppb), except for April (1.8 ppb). The pattern was similar in 2021 (-1.8 ppb for both MAM and JJA), except for June (1.8 ppb). The sites in the western US showed similar behavior, with negative anomalies in 2020 (-2.1 ppb for MAM and -0.5 ppb for JJA), and 2021 (-0.5 ppb for MAM). However, the JJA seasonal average was influenced by strong positive anomalies in July, due to the large spread of wildfires in the western US. The polluted rural sites in the western US showed a negative O3 anomaly for MAM 2020, and a slight recovery in 2021, resulting in a positive anomaly for MAM and alternating positive and negative anomalies in JJA. The eastern US sites were characterized by below baseline numbers for both MAM and JJA 2020, while in 2021 the negative values generated a "dipole" structure, with the western sites influenced by the presence of wildfires. Concerning the rest of the world, a global picture could not be drawn, as the sites, spanning a range of different environments, did not show consistent anomalies across the globe. A few sites did not experience any relevant variations (e.g., the Antarctic sites), while others showed signals of influence from the surrounding environment, or behaviors like those observed for European and US remote sites. We also attempted anomaly attribution by analyzing the behavior of several O3 precursors (e.g., surface and columnar CO, NO, and NO2) from the CAMS reanalysis, and the patterns of emissions reductions for 2019, 2020, and 2021.

Published
2023

17. Diverging Ozone Trends Above Western North America: Boundary Layer Decreases Versus Free Tropospheric Increases

Author

Kai‐Lan Chang, Owen R. Cooper, Gavin Rodriguez, Laura T. Iraci, Emma L. Yates, Matthew S. Johnson, Audrey Gaudel, Daniel A. Jaffe, Noah Bernays, Hannah Clark, Peter Effertz, Thierry Leblanc, Irina Petropavlovskikh, Bastien Sauvage, and David W. Tarasick

Subjects
Atmospheric Science, Geophysics, Space and Planetary Science, Earth and Planetary Sciences (miscellaneous)
Published
2023

18. Fingerprints of the COVID-19 economic downturn and recovery on ozone anomalies at high-elevation sites in North America and Western Europe.

Author

Putero, Davide, Cristofanelli, Paolo, Kai-Lan Chang, Dufour, Gaëlle, Beachley, Gregory, Couret, Cédric, Effertz, Peter, Jaffe, Daniel A., Kubistin, Dagmar, Lynch, Jason, Petropavlovskikh, Irina, Puchalski, Melissa, Sharac, Timothy, Sive, Barkley C., Steinbacher, Martin, Torres, Carlos, and Cooper, Owen R.

Abstract

With a few exceptions, most studies on tropospheric ozone (O3) variability during and following the COVID-19 economic downturn focused on high-emission regions or urban environments. In this work, we investigated the impact of the societal restriction measures during the COVID-19 pandemic on surface O3 at several high-elevation sites across North America and Western Europe. Monthly O3 anomalies were calculated for 2020 and 2021, with respect to the baseline period 2000-2019, to explore the impact of the economic downturn initiated in 2020 and its recovery in 2021. In total, 41 highelevation sites were analyzed: 5 rural or mountaintop stations in Western Europe, 19 rural sites in the Western US, 4 sites in the Western US downwind of highly polluted source regions, 4 rural sites in the eastern US, plus 9 mountaintop or highelevation sites outside Europe and the United States to provide a "global" reference. In 2020, the European high-elevation sites showed persistent negative surface O3 anomalies during spring (March-May, i.e., MAM) and summer (June-August, i.e., JJA), except for April. The pattern was similar in 2021, except for June. The rural sites in the Western US showed similar behavior, with negative anomalies in MAM and JJA 2020 (except for August), and MAM 2021. The JJA 2021 seasonal average was influenced by strong positive anomalies in July, due to large and widespread wildfires across the Western US. The polluted sites in theWestern US showed negative O3 anomalies during MAM 2020, and a slight recovery in 2021, resulting in a positive average anomaly for MAM 2021 and a pronounced month-to-month variability in JJA 2021 anomalies. The Eastern US sites were also characterized by below average O3 for both MAM and JJA 2020, while in 2021 the negative values exhibited an opposite structure compared to the Western US sites, which were influenced by wildfires. Concerning the rest of the World, a global picture could not be drawn, as the sites, spanning a range of different environments, did not show consistent anomalies, with a few sites not experiencing any notable variation. Moreover, we also compared our surface anomalies to the variability of mid-tropospheric O3 detected by the IASI satellite instrument. Negative anomalies were observed by IASI, consistent with published satellite and modeling studies, suggesting that the anomalies can be largely attributed to the reduction of O3 precursor emissions in 2020. [ABSTRACT FROM AUTHOR]

Published
2023
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19. Understanding recent tropospheric ozone trends in the context of large internal variability: A new perspective from chemistry-climate model ensembles

Author

Arlene Fiore, Sarah Hancock, Jean-Francois Lamarque, Gustavo Correa, Kai-Lan Chang, Muye Ru, Owen Cooper, Audrey Gaudel, Lorenzo Polvani, Bastien Sauvage, and Jerald Ziemke

Abstract

Observational records of meteorological and chemical variables are imprinted by an unknown combination of anthropogenic activity, natural forcings, and internal variability. With a 15-member initial-condition ensemble generated from the CESM2-WACCM6 chemistry-climate model for 1950-2014, we extract signals of anthropogenic (‘forced’) change from the noise of internally arising climate variability on observed tropospheric ozone trends. Positive trends in free tropospheric ozone measured at long-term surface observatories, by commercial aircraft, and retrieved from satellite instruments generally fall within the ensemble range. CESM2-WACCM6 tropospheric ozone trends are also bracketed by those in a larger ensemble constructed from five additional chemistry-climate models. Comparison of the multi-model ensemble with observed tropospheric column ozone trends in the northern tropics implies an underestimate in regional precursor emission growth over recent decades. Positive tropospheric ozone trends clearly emerge from 1950 to 2014, exceeding 0.2 DU yr-1 at 20-40N in all CESM2-WACCM6 ensemble members. Tropospheric ozone observations are often only available for recent decades, and we show that even a two-decade record length is insufficient to eliminate the role of internal variability, which can produce regional tropospheric ozone trends oppositely signed from ensemble mean (forced) changes. By identifying regions and seasons with strong anthropogenic change signals relative to internal variability, initial-condition ensembles can guide future observing systems seeking to detect anthropogenic change. For example, analysis of the CESM2-WACCM6 ensemble reveals year-round upper tropospheric ozone increases from 1995-2014, largest at 30S-40N during boreal summer. Lower tropospheric ozone increases most strongly in the winter hemisphere, but internal variability leads to trends of opposite sign (ensemble overlaps zero) north of 40N during boreal summer. This decoupling of ozone trends in the upper and lower troposphere suggests a growing prominence for tropospheric ozone as a greenhouse gas despite regional efforts to abate warm season ground-level ozone.

Published
2022

20. Global trends in ozone concentration and attributable mortality for urban, peri-urban, and rural areas between 2000 and 2019: a modelling study

Author

Daniel A Malashock, Marissa N Delang, Jacob S Becker, Marc L Serre, J Jason West, Kai-Lan Chang, Owen R Cooper, and Susan C Anenberg

Subjects
Health (social science), Health Policy, Public Health, Environmental and Occupational Health, Medicine (miscellaneous)
Abstract

Data on long-term trends of ozone exposure and attributable mortality across urban-rural catchment areas worldwide are scarce, especially for low-income and middle-income countries. This study aims to estimate trends in ozone concentrations and attributable mortality for urban-rural catchment areas worldwide.In this modelling study, we used a health impact function to estimate ozone concentrations and ozone-attributable chronic respiratory disease mortality for urban areas worldwide, and their surrounding peri-urban, peri-rural, and rural areas. We estimated ozone-attributable respiratory health outcomes using a modified Global Burden of Diseases, Injuries, and Risk Factors 2019 Study approach. We evaluate long-term trends with linear regressions of annual ozone concentrations and ozone-attributable mortality against time in years, and examined the influence of each health impact function input parameter to temporal changes in ozone-attributable disease burden estimates for 12 946 cities worldwide by region, from 2000 to 2019.Ozone-attributable mortality worldwide increased by 46% from 2000 (290 400 deaths [95% CI 151 800-457 600]) to 2019 (423 100 deaths [95% CI 223 200-659 400]). The fraction of global ozone-attributable mortality occurring in peri-urban areas remained unchanged from 2000 to 2019 (56%), whereas urban areas gained in their share of global ozone-attributable burden (from 35% to 37%; 54 000 more deaths). Across all cities studied, average population-weighted mean ozone concentration increased by 11% (46 parts per billion [ppb] to 51 ppb). The number of cities with concentrations above the WHO peak season ozone standard (60 μg/mOzone exposure is increasing worldwide, contributing to disproportionate ozone mortality in peri-urban areas and increasing ozone exposure and attributable mortality in urban areas worldwide. Reducing ozone precursor emissions in areas affecting urban and peri-urban exposure can yield substantial public health benefits.NASA Health and Air Quality Applied Sciences Team, the National Institute for Occupational Safety and Health, and the NOAA Co-operative Agreement with the Cooperative Institute for Research in Environmental Sciences.

Published
2022

21. Global tropospheric ozone trends, attributions, and radiative impacts in 1995–2017: an integrated analysis using aircraft (IAGOS) observations, ozonesonde, and multi-decadal chemical model simulations

Author

Haolin Wang, Xiao Lu, Daniel J. Jacob, Owen R. Cooper, Kai-Lan Chang, Ke Li, Meng Gao, Yiming Liu, Bosi Sheng, Kai Wu, Tongwen Wu, Jie Zhang, Bastien Sauvage, Philippe Nédélec, Romain Blot, Shaojia Fan, Laboratoire d'aérologie (LAERO), Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Observatoire Midi-Pyrénées (OMP), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), and Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)

Subjects
Atmospheric Science, [SDU]Sciences of the Universe [physics]
Abstract

Quantification and attribution of long-term tropospheric ozone trends are critical for understanding the impact of human activity and climate change on atmospheric chemistry but are also challenged by the limited coverage of long-term ozone observations in the free troposphere where ozone has higher production efficiency and radiative potential compared to that at the surface. In this study, we examine observed tropospheric ozone trends, their attributions, and radiative impacts from 1995–2017 using aircraft observations from the In-service Aircraft for a Global Observing System database (IAGOS), ozonesondes, and a multi-decadal GEOS-Chem chemical model simulation. IAGOS observations above 11 regions in the Northern Hemisphere and 19 of 27 global ozonesonde sites have measured increases in tropospheric ozone (950–250 hPa) by 2.7 ± 1.7 and 1.9 ± 1.7 ppbv per decade on average, respectively, with particularly large increases in the lower troposphere (950–800 hPa) above East Asia, the Persian Gulf, India, northern South America, the Gulf of Guinea, and Malaysia/Indonesia by 2.8 to 10.6 ppbv per decade. The GEOS-Chem simulation driven by reanalysis meteorological fields and the most up-to-date year-specific anthropogenic emission inventory reproduces the overall pattern of observed tropospheric ozone trends, including the large ozone increases over the tropics of 2.1–2.9 ppbv per decade and above East Asia of 0.5–1.8 ppbv per decade and the weak tropospheric ozone trends above North America, Europe, and high latitudes in both hemispheres, but trends are underestimated compared to observations. GEOS-Chem estimates an increasing trend of 0.4 Tg yr−1 of the tropospheric ozone burden in 1995–2017. We suggest that uncertainties in the anthropogenic emission inventory in the early years of the simulation (e.g., 1995–1999) over developing regions may contribute to GEOS-Chem's underestimation of tropospheric ozone trends. GEOS-Chem sensitivity simulations show that changes in global anthropogenic emission patterns, including the equatorward redistribution of surface emissions and the rapid increases in aircraft emissions, are the dominant factors contributing to tropospheric ozone trends by 0.5 Tg yr−1. In particular, we highlight the disproportionately large, but previously underappreciated, contribution of aircraft emissions to tropospheric ozone trends by 0.3 Tg yr−1, mainly due to aircraft emitting NOx in the mid-troposphere and upper troposphere where ozone production efficiency is high. Decreases in lower-stratospheric ozone and the stratosphere–troposphere flux in 1995–2017 contribute to an ozone decrease at mid-latitudes and high latitudes. We estimate the change in tropospheric ozone radiative impacts from 1995–1999 to 2013–2017 is +18.5 mW m−2, with 43.5 mW m−2 contributed by anthropogenic emission changes (20.5 mW m−2 alone by aircraft emissions), highlighting that the equatorward redistribution of emissions to areas with strong convection and the increase in aircraft emissions are effective for increasing tropospheric ozone's greenhouse effect.

Published
2022

23. Statistical regularization for trend detection: an integrated approach for detecting long-term trends from sparse tropospheric ozone profiles

Author

Owen R. Cooper, Kai-Lan Chang, Valérie Thouret, Audrey Gaudel, and Irina Petropavlovskikh

Subjects
Atmospheric Science, 010504 meteorology & atmospheric sciences, Contrast (statistics), 010501 environmental sciences, 01 natural sciences, lcsh:QC1-999, lcsh:Chemistry, Troposphere, Trend analysis, chemistry.chemical_compound, Smoothing spline, Data point, Mean absolute percentage error, lcsh:QD1-999, chemistry, Sampling (signal processing), Statistics, Environmental science, Tropospheric ozone, lcsh:Physics, 0105 earth and related environmental sciences
Abstract

Detecting a tropospheric ozone trend from sparsely sampled ozonesonde profiles (typically once per week) is challenging due to the short-lived anomalies in the time series resulting from ozone's high temporal variability. To enhance trend detection, we have developed a sophisticated statistical approach that utilizes a geoadditive model to assess ozone variability across a time series of vertical profiles. Treating the profile time series as a set of individual time series on discrete pressure surfaces, a class of smoothing spline ANOVA (analysis of variance) models is used for the purpose of jointly modeling multiple correlated time series (on separate pressure surfaces) by their associated seasonal and interannual variabilities. This integrated fit method filters out the unstructured variation through a statistical regularization (i.e., a roughness penalty) by taking advantage of the additional correlated data points available on the pressure surfaces above and below the surface of interest. We have applied this technique to the trend analysis of the vertically correlated time series of tropospheric ozone observations from (1) IAGOS (In-service Aircraft for a Global Observing System) commercial aircraft profiles above Europe and China throughout 1994–2017 and (2) NOAA GML's (Global Monitoring Laboratory) ozonesonde records at Hilo, Hawaii, (1982–2018) and Trinidad Head, California (1998–2018). We illustrate the ability of this technique to detect a consistent trend estimate and its effectiveness in reducing the associated uncertainty in the profile data due to the low sampling frequency. We also conducted a sensitivity analysis of frequent IAGOS profiles above Europe (approximately 120 profiles per month) to determine how many profiles in a month are required for reliable long-term trend detection. When ignoring the vertical correlation, we found that a typical sampling strategy (i.e. four profiles per month) might result in 7 % of sampled trends falling outside the 2σ uncertainty interval derived from the full dataset with an associated 10 % of mean absolute percentage error. Based on a series of sensitivity studies, we determined optimal sampling frequencies for (1) basic trend detection and (2) accurate quantification of the trend. When applying the integrated fit method, we find that a typical sampling frequency of four profiles per month is adequate for basic trend detection; however, accurate quantification of the trend requires 14 profiles per month. Accurate trend quantification can be achieved with only 10 profiles per month if a regular sampling frequency is applied. In contrast, the standard separated fit method, which ignores the vertical correlation between pressure surfaces, requires 8 profiles per month for basic trend detection and 18 profiles per month for accurate trend quantification. While our method improves trend detection from sparse datasets, the key to substantially reducing the uncertainty is to increase the sampling frequency.

Published
2020

26. Publisher Correction: The risk of fibromyalgia in patients with iron deficiency anemia: a nationwide population‑based cohort study

Author

Yu-Ting Tina Wang, Po-Ya Tung, Chien-Feng Kuo, Kam-Hang Leong, Shin-Yi Tsai, Hsuan-Ju Chen, Wei-Cheng Yao, Li-Chin Wu, Kai-Lan Chang, and Che-Chen Lin

Subjects
Adult, Male, Pediatrics, medicine.medical_specialty, Fibromyalgia, Adolescent, Science, Taiwan, MEDLINE, Population based cohort, Risk Factors, medicine, Humans, In patient, Proportional Hazards Models, Retrospective Studies, Multidisciplinary, Anemia, Iron-Deficiency, business.industry, Middle Aged, medicine.disease, Publisher Correction, Iron-deficiency anemia, Medicine, Female, business
Abstract

Since iron is essential for neurotransmitter synthesis, decreased iron stores might lead to reduced production of biogenic amines which phenomenon was shown in Fibromyalgia (FM) patients. The aims are to investigate the association of iron deficiency anemia (IDA) and FM and to find the effects of different interventions. We conducted a study using the Taiwan National Health Insurance Research Database. The IDA cohort consisted of 13,381 patients with newly diagnosed IDA between 2000 and 2008. Each patient with IDA was frequency-matched with one people without IDA, by sex, age and index year. The Cox proportional hazards regression analysis was conducted to estimate the association between IDA and FM risk. The event was the occurrence of FM. The overall incidence density rate of FM in the IDA cohort was higher than in the non-IDA cohort with a multivariable Cox proportional hazards model measured adjusted hazard ratio [HR], 1.19; 95% confidence interval [CI], 1.13-1.25). When using non-IDA group as reference, we compared with different therapies for IDA. The adjusted HRs of FM were 1.38 (95% CI = 1.30-1.47), 1.10 (95% CI = 1.03-1.16), 1.18 (95% CI = 0.98-1.43) and 0.73 (95% CI = 0.58-0.90) for IDA patient without therapy, iron supplement alone, blood transfusion alone and both iron supplement and blood transfusion respectively. Our results suggest IDA is associated with an increased risk of FM. All patients should have iron supplementation both to correct anemia and replenish body stores.

Published
2021

27. A new method (M3Fusion v1) for combining observations and multiple model output for an improved estimate of the global surface ozone distribution

Author

Junhua Liu, Makoto Deushi, Meiyun Lin, Kengo Sudo, Christoph A. Keller, Kai-Lan Chang, Owen R. Cooper, Virginie Marécal, Martin G. Schultz, Marc L. Serre, Béatrice Josse, and J. Jason West

Subjects
Fusion, Ozone, 010504 meteorology & atmospheric sciences, Meteorology, Mean squared error, Grid, 01 natural sciences, 010104 statistics & probability, chemistry.chemical_compound, Surface ozone, Distribution (mathematics), chemistry, 13. Climate action, Atmospheric chemistry, Environmental science, Tropospheric ozone, 0101 mathematics, 0105 earth and related environmental sciences
Abstract

We have developed a new statistical approach (M3Fusion) for combining surface ozone observations from thousands of monitoring sites around the world with the output from multiple atmospheric chemistry models to produce a global surface ozone distribution with greater accuracy than can be provided by any individual model. The ozone observations from 4766 monitoring sites were provided by the Tropospheric Ozone Assessment Report (TOAR) surface ozone database, which contains the world's largest collection of surface ozone metrics. Output from six models was provided by the participants of the Chemistry-Climate Model Initiative (CCMI) and NASA's Global Modeling and Assimilation Office (GMAO). We analyze the 6-month maximum of the maximum daily 8 h average ozone value (DMA8) for relevance to ozone health impacts. We interpolate the irregularly spaced observations onto a fine-resolution grid by using integrated nested Laplace approximations and compare the ozone field to each model in each world region. This method allows us to produce a global surface ozone field based on TOAR observations, which we then use to select the combination of global models with the greatest skill in each of eight world regions; models with greater skill in a particular region are given higher weight. This blended model product is bias corrected within 2∘ of observation locations to produce the final fused surface ozone product. We show that our fused product has an improved mean squared error compared to the simple multi-model ensemble mean, which is biased high in most regions of the world.

Published
2019

28. The risk of fibromyalgia in patients with iron deficiency anemia: a nationwide population-based cohort study

Author

Che-Chen Lin, Yu-Ting Tina Wang, Li-Chin Wu, Hsuan-Ju Chen, Chien-Feng Kuo, Kai-Lan Chang, Po-Ya Tung, Kam-Hang Leong, Shin-Yi Tsai, and Wei-Cheng Yao

Subjects
medicine.medical_specialty, Blood transfusion, Anemia, Science, medicine.medical_treatment, Iron supplement, 030204 cardiovascular system & hematology, Article, 03 medical and health sciences, Medical research, 0302 clinical medicine, hemic and lymphatic diseases, Internal medicine, medicine, 030212 general & internal medicine, Multidisciplinary, business.industry, Proportional hazards model, Incidence (epidemiology), Hazard ratio, Health care, nutritional and metabolic diseases, medicine.disease, Risk factors, Iron-deficiency anemia, Cohort, Medicine, business
Abstract

Since iron is essential for neurotransmitter synthesis, decreased iron stores might lead to reduced production of biogenic amines which phenomenon was shown in Fibromyalgia (FM) patients. The aims are to investigate the association of iron deficiency anemia (IDA) and FM and to find the effects of different interventions. We conducted a study using the Taiwan National Health Insurance Research Database. The IDA cohort consisted of 13,381 patients with newly diagnosed IDA between 2000 and 2008. Each patient with IDA was frequency-matched with one people without IDA, by sex, age and index year. The Cox proportional hazards regression analysis was conducted to estimate the association between IDA and FM risk. The event was the occurrence of FM. The overall incidence density rate of FM in the IDA cohort was higher than in the non-IDA cohort with a multivariable Cox proportional hazards model measured adjusted hazard ratio [HR], 1.19; 95% confidence interval [CI], 1.13–1.25). When using non-IDA group as reference, we compared with different therapies for IDA. The adjusted HRs of FM were 1.38 (95% CI = 1.30–1.47), 1.10 (95% CI = 1.03–1.16), 1.18 (95% CI = 0.98–1.43) and 0.73 (95% CI = 0.58–0.90) for IDA patient without therapy, iron supplement alone, blood transfusion alone and both iron supplement and blood transfusion respectively. Our results suggest IDA is associated with an increased risk of FM. All patients should have iron supplementation both to correct anemia and replenish body stores.

Published
2021

29. Mapping Yearly Fine Resolution Global Surface Ozone through the Bayesian Maximum Entropy Data Fusion of Observations and Model Output for 1990–2017

Author

Martin G. Schultz, Meiyun Lin, Li Zhang, Jean-Francois Lamarque, Stephanie E. Cleland, J. Jason West, M. DeLang, Simone Tilmes, Elyssa L Collins, Béatrice Josse, Michael Brauer, Sabine Schröder, Sarah A. Strode, Kengo Sudo, Xiao Lu, Lin Zhang, J. Becker, Virginie Marécal, Christoph A. Keller, Marc L. Serre, Kai-Lan Chang, Junhua Liu, Makoto Deushi, Owen R. Cooper, NOAA Chemical Sciences Laboratory (CSL), National Oceanic and Atmospheric Administration (NOAA), Institut für Energie- und Klimaforschung - Troposphäre (IEK-8), Forschungszentrum Jülich GmbH | Centre de recherche de Juliers, Helmholtz-Gemeinschaft = Helmholtz Association-Helmholtz-Gemeinschaft = Helmholtz Association, University of Freiburg [Freiburg], Meteorological Research Institute [Tsukuba] (MRI), Japan Meteorological Agency (JMA), Centre national de recherches météorologiques (CNRM), and Météo France-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Ozone, Asia, Entropy, 010501 environmental sciences, 01 natural sciences, Russia, chemistry.chemical_compound, Bayes' theorem, Surface ozone, Air Pollution, Environmental monitoring, Environmental Chemistry, Entropy (information theory), Humans, ComputingMilieux_MISCELLANEOUS, 0105 earth and related environmental sciences, Air Pollutants, Bayes Theorem, General Chemistry, Sensor fusion, United States, Weighting, chemistry, 13. Climate action, Climatology, Atmospheric chemistry, Africa, [SDE]Environmental Sciences, Environmental science, ddc:333.7, Environmental Monitoring
Abstract

Estimates of ground-level ozone concentrations are necessary to determine the human health burden of ozone. To support the Global Burden of Disease Study, we produce yearly fine resolution global surface ozone estimates from 1990 to 2017 through a data fusion of observations and models. As ozone observations are sparse in many populated regions, we use a novel combination of the M3Fusion and Bayesian Maximum Entropy (BME) methods. With M3Fusion, we create a multimodel composite by bias-correcting and weighting nine global atmospheric chemistry models based on their ability to predict observations (8834 sites globally) in each region and year. BME is then used to integrate observations, such that estimates match observations at each monitoring site with the observational influence decreasing smoothly across space and time until the output matches the multimodel composite. After estimating at 0.5° resolution using BME, we add fine spatial detail from an additional model, yielding estimates at 0.1° resolution. Observed ozone is predicted more accurately (R2 = 0.81 at the test point, 0.63 at 0.1°, and 0.62 at 0.5°) than the multimodel mean (R2 = 0.28 at 0.5°). Global ozone exposure is estimated to be increasing, driven by highly populated regions of Asia and Africa, despite decreases in the United States and Russia.

Published
2021

31. COVID‐19 Crisis Reduces Free Tropospheric Ozone Across the Northern Hemisphere

Author

Marc Allaart, Susan E. Strahan, Ryan M. Stauffer, Richard Querel, Anne M. Thompson, Nicholas B. Jones, Clare Paton-Walsh, Patrick Cullis, Tatsumi Nakano, Bryan J. Johnson, Gérard Ancellet, Thomas Blumenstock, Ankie Piters, Holger Deckelmann, Omaira García, Matthias Palm, Roeland Van Malderen, Kai-Lan Chang, Nis Jepsen, Antje Inness, M.B. Tully, Ralf Sussmann, Amelie N. Röhling, Gonzague Romanens, Dagmar Kubistin, Ana Diaz Rodriguez, Fernando Chouza, René Stübi, Owen R. Cooper, Emmanuel Mahieu, Kimberly Strong, Christian Plass-Dülmer, Jonathan Davies, Richard Engelen, Peter Oelsner, David W. Tarasick, Peter von der Gathen, Jose-Luis Hernandez, Michael Gill, Justus Notholt, Thierry Leblanc, Christian Servais, Irina Petropavlovskikh, Matthias Schneider, Norrie Lyall, Rigel Kivi, Carlos Torres, Shoma Yamanouchi, Sophie Godin-Beekmann, Bogumil Kois, James W. Hannigan, Wolfgang Steinbrecht, Andy Delcloo, Deutscher Wetterdienst [Offenbach] (DWD), Environment and Climate Change Canada, Alfred-Wegener-Institut, Helmholtz-Zentrum für Polar- und Meeresforschung (AWI), Danish Meteorological Institute (DMI), Finnish Meteorological Institute (FMI), Met Office Lerwick, Universität Bremen, Institute of Meteorology and Water Management - National Research Institute (IMGW - PIB), Royal Netherlands Meteorological Institute (KNMI), Irish Meteorological Service (MET ÉIREANN), Institut Royal Météorologique de Belgique [Bruxelles] (IRM), Institut für Meteorologie und Klimaforschung - Atmosphärische Umweltforschung (IMK-IFU), Karlsruher Institut für Technologie (KIT), Institut d'Astrophysique et de Géophysique [Liège], Université de Liège, Federal Office of Meteorology and Climatology MeteoSwiss, TROPO - LATMOS, Laboratoire Atmosphères, Milieux, Observations Spatiales (LATMOS), Sorbonne Université (SU)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS), STRATO - LATMOS, University of Toronto, ESRL Global Monitoring Laboratory [Boulder] (GML), NOAA Earth System Research Laboratory (ESRL), National Oceanic and Atmospheric Administration (NOAA)-National Oceanic and Atmospheric Administration (NOAA), Cooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado [Boulder]-National Oceanic and Atmospheric Administration (NOAA), National Center for Atmospheric Research [Boulder] (NCAR), Agencia Estatal de Meteorología (AEMet), Meteorological Research Institute [Tsukuba] (MRI), Japan Meteorological Agency (JMA), Jet Propulsion Laboratory (JPL), NASA-California Institute of Technology (CALTECH), Institut für Meteorologie und Klimaforschung - Atmosphärische Spurengase und Fernerkundung (IMK-ASF), Australian Bureau of Meteorology [Melbourne] (BoM), Australian Government, University of Wollongong [Australia], National Institute of Water and Atmospheric Research [Lauder] (NIWA), GSFC Earth Sciences Division, NASA Goddard Space Flight Center (GSFC), Earth Science System Interdisciplinary Center [College Park] (ESSIC), College of Computer, Mathematical, and Natural Sciences [College Park], University of Maryland [College Park], University of Maryland System-University of Maryland System-University of Maryland [College Park], University of Maryland System-University of Maryland System, European Centre for Medium-Range Weather Forecasts (ECMWF), NOAA Chemical Sciences Laboratory (CSL), National Oceanic and Atmospheric Administration (NOAA), University of Wollongong, GFSC Earth Sciences Division, Kubistin, Dagmar, 1 Deutscher Wetterdienst Hohenpeißenberg Germany, Plass‐Dülmer, Christian, Davies, Jonathan, 2 Environment and Climate Change Canada Toronto ONT Canada, Tarasick, David W., Gathen, Peter von der, 3 Alfred Wegener Institut Helmholtz‐Zentrum für Polar‐ und Meeresforschung Potsdam Germany, Deckelmann, Holger, Jepsen, Nis, 4 Danish Meteorological Institute Copenhagen Denmark, Kivi, Rigel, 5 Finnish Meteorological Institute Sodankylä Finland, Lyall, Norrie, 6 British Meteorological Service Lerwick UK, Palm, Matthias, 7 University of Bremen Bremen Germany, Notholt, Justus, Kois, Bogumil, 8 Institute of Meteorology and Water Management Legionowo Poland, Oelsner, Peter, 9 Deutscher Wetterdienst Lindenberg Germany, Allaart, Marc, 10 Royal Netherlands Meteorological Institute DeBilt The Netherlands, Piters, Ankie, Gill, Michael, 11 Met Éireann (Irish Met. Service) Valentia Ireland, Van Malderen, Roeland, 12 Royal Meteorological Institute of Belgium Uccle Belgium, Delcloo, Andy W., Sussmann, Ralf, 13 Karlsruhe Institute of Technology IMK‐IFU Garmisch‐Partenkirchen Germany, Mahieu, Emmanuel, 14 Institute of Astrophysics and Geophysics University of Liège Liège Belgium, Servais, Christian, Romanens, Gonzague, 15 Federal Office of Meteorology and Climatology MeteoSwiss Payerne Switzerland, Stübi, Rene, Ancellet, Gerard, 16 LATMOS Sorbonne Université‐UVSQ‐CNRS/INSU Paris France, Godin‐Beekmann, Sophie, Yamanouchi, Shoma, 17 University of Toronto Toronto ONT Canada, Strong, Kimberly, Johnson, Bryan, 18 NOAA ESRL Global Monitoring Laboratory Boulder CO USA, Cullis, Patrick, Petropavlovskikh, Irina, Hannigan, James W., 20 National Center for Atmospheric Research Boulder CO USA, Hernandez, Jose‐Luis, 21 State Meteorological Agency (AEMET) Madrid Spain, Diaz Rodriguez, Ana, Nakano, Tatsumi, 22 Meteorological Research Institute Tsukuba Japan, Chouza, Fernando, 23 Jet Propulsion Laboratory California Institute of Technology Table Mountain Facility Wrightwood CA USA, Leblanc, Thierry, Torres, Carlos, 24 Izaña Atmospheric Research Center AEMET Tenerife Spain, Garcia, Omaira, Röhling, Amelie N., 25 Karlsruhe Institute of Technology IMK‐ASF Karlsruhe Germany, Schneider, Matthias, Blumenstock, Thomas, Tully, Matt, 26 Bureau of Meteorology Melbourne Australia, Paton‐Walsh, Clare, 27 Centre for Atmospheric Chemistry University of Wollongong Wollongong Australia, Jones, Nicholas, Querel, Richard, 28 National Institute of Water and Atmospheric Research Lauder New Zealand, Strahan, Susan, 29 NASA Goddard Space Flight Center Earth Sciences Division Greenbelt MD USA, Stauffer, Ryan M., Thompson, Anne M., Inness, Antje, 32 European Centre for Medium‐Range Weather Forecasts Reading UK, Engelen, Richard, Chang, Kai‐Lan, 19 Cooperative Institute for Research in Environmental Sciences (CIRES) University of Colorado Boulder CO USA, and Cooper, Owen R.

Subjects
Atmospheric Science, 010504 meteorology & atmospheric sciences, Pollution: Urban, Regional and Global, Atmospheric Composition and Structure, Biogeosciences, 010502 geochemistry & geophysics, Atmospheric sciences, [SDV.MHEP.PSR]Life Sciences [q-bio]/Human health and pathology/Pulmonology and respiratory tract, 01 natural sciences, Biogeochemical Kinetics and Reaction Modeling, LIDAR, Troposphere, Oceanography: Biological and Chemical, chemistry.chemical_compound, [SDV.MHEP.MI]Life Sciences [q-bio]/Human health and pathology/Infectious diseases, Emission reductions, ddc:550, [SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere, VERTICAL-DISTRIBUTION, Marine Pollution, RECORD, NOX, 551.51, Biogeochemistry, Ozone depletion, Oceanography: General, Pollution: Urban and Regional, Geophysics, Free troposphere, Emissions, Troposphere: Composition and Chemistry, The COVID‐19 pandemic: linking health, society and environment, Cryosphere, Biogeochemical Cycles, Processes, and Modeling, Ozone, Megacities and Urban Environment, URBAN, Atmosphere, Paleoceanography, Altitude, COVID‐19, Research Letter, Global Change, Tropospheric ozone, Stratosphere, Urban Systems, 0105 earth and related environmental sciences, Aerosols, [PHYS.PHYS.PHYS-AO-PH]Physics [physics]/Physics [physics]/Atmospheric and Oceanic Physics [physics.ao-ph], emissions, Northern Hemisphere, COVID-19, PROFILES, Aerosols and Particles, TRENDS, Earth sciences, ozone, Physics and Astronomy, troposphere, chemistry, 13. Climate action, General Earth and Planetary Sciences, Environmental science, Natural Hazards
Abstract

Throughout spring and summer 2020, ozone stations in the northern extratropics recorded unusually low ozone in the free troposphere. From April to August, and from 1 to 8 kilometers altitude, ozone was on average 7% (≈4 nmol/mol) below the 2000–2020 climatological mean. Such low ozone, over several months, and at so many stations, has not been observed in any previous year since at least 2000. Atmospheric composition analyses from the Copernicus Atmosphere Monitoring Service and simulations from the NASA GMI model indicate that the large 2020 springtime ozone depletion in the Arctic stratosphere contributed less than one‐quarter of the observed tropospheric anomaly. The observed anomaly is consistent with recent chemistry‐climate model simulations, which assume emissions reductions similar to those caused by the COVID‐19 crisis. COVID‐19 related emissions reductions appear to be the major cause for the observed reduced free tropospheric ozone in 2020., Plain Language Summary: Worldwide actions to contain the COVID‐19 virus have closed factories, grounded airplanes, and have generally reduced travel and transportation. Less fuel was burnt, and less exhaust was emitted into the atmosphere. Due to these measures, the concentration of nitrogen oxides and volatile organic compounds (VOCs) decreased in the atmosphere. These substances are important for photochemical production and destruction of ozone in the atmosphere. In clean or mildly polluted air, reducing nitrogen oxides and/or VOCs will reduce the photochemical production of ozone and result in less ozone. In heavily polluted air, in contrast, reducing nitrogen oxides can increase ozone concentrations, because less nitrogen oxide is available to destroy ozone. In this study, we use data from three types of ozone instruments, but mostly from ozonesondes on weather balloons. The sondes fly from the ground up to 30 kilometers altitude. In the first 8 km, we find significantly reduced ozone concentrations in the northern extratropics during spring and summer of 2020, less than in any other year since at least 2000. We suggest that reduced emissions due to the COVID‐19 crisis have lowered photochemical ozone production and have caused the observed ozone reductions in the troposphere., Key Points: In spring and summer 2020, stations in the northern extratropics report on average 7% (4 nmol/mol) less tropospheric ozone than normal Such low tropospheric ozone, over several months, and at so many sites, has not been observed in any previous year since at least 2000 Most of the reduction in tropospheric ozone in 2020 is likely due to emissions reductions related to the COVID‐19 pandemic, NASA | Earth Sciences Division (NASA Earth Science Division) http://dx.doi.org/10.13039/100014573, Gouvernement du Canada | Natural Sciences and Engineering Research Council of Canada (NSERC) http://dx.doi.org/10.13039/501100000038, Australian Research Council, Fonds De La Recherche Scientifique ‐ FNRS (FNRS) http://dx.doi.org/10.13039/501100002661, Deutsche Forschungsgemeinschaft (DFG) http://dx.doi.org/10.13039/501100001659, Bundesministerium für Wirtschaft und Energie (BMWi) http://dx.doi.org/10.13039/501100006360

Published
2021

32. Contributions of World Regions to the Global Tropospheric Ozone Burden Change From 1980 to 2010

Author

Johannes Flemming, Takashi Sekiya, Kai-Lan Chang, Marianne Tronstad Lund, Philippe Nédélec, J. Jason West, Louisa K. Emmons, Valérie Thouret, Kengo Sudo, Jan Eiof Jonson, Yuqiang Zhang, and Audrey Gaudel

Subjects
chemistry.chemical_compound, Geophysics, 010504 meteorology & atmospheric sciences, chemistry, General Earth and Planetary Sciences, Environmental science, Tropospheric ozone, 010502 geochemistry & geophysics, Atmospheric sciences, 01 natural sciences, Methane, 0105 earth and related environmental sciences
Abstract

We investigate the contributions of emission changes from 10 world regions, as well as the global methane concentration change, on the global tropospheric ozone burden change from 1980 to 2010. The...

Published
2021

33. Did the COVID-19 Crisis Reduce Free Tropospheric Ozone across the Northern Hemisphere?

Author

Sophie Godin-Beekmann, Roeland Van Malderen, James W. Hannigan, Nicholas B. Jones, Kimberly Strong, Matthias Schneider, Bogumil Kois, Norrie Lyall, Owen R. Cooper, Peter von der Gathen, Bryan J. Johnson, Ryan M. Stauffer, Christian Plass-Dülmer, Rigel Kivi, Ralf Sussmann, Thierry Leblanc, Jonathan Davies, David W. Tarasick, Justus Notholt, Richard Engelen, Peter Oelsner, Patrick Cullis, René Stübi, Susan E. Strahan, Thomas Blumenstock, Ankie Piters, Richard Querel, Omaira García, Gonzague Romanens, Michael Gill, Dagmar Kubistin, Wolfgang Steinbrecht, Gérard Ancellet, Andy Delcloo, Carlos Torres, Ana Diaz Rodriguez, Holger Deckelmann, Kai-Lan Chang, Fernando Chouza, Emmanuel Mahieu, Anne M. Thompson, Shoma Yamanouchi, Tatsumi Nakano, Jose-Luis Hernandez, Mathias Palm, Irina Petropavlovskikh, Antje Inness, M.B. Tully, Clare Paton-Walsh, Nis Jepsen, Marc Allaart, Amelie Röhling, and Christian Servais

Subjects
Atmosphere, Troposphere, chemistry.chemical_compound, Ozone, Altitude, chemistry, 13. Climate action, Northern Hemisphere, Environmental science, Tropospheric ozone, Atmospheric sciences, Stratosphere, Ozone depletion
Abstract

Throughout spring and summer 2020, ozone stations in the northern extratropics recorded unusually low ozone in the free troposphere. From April to August, and from 1 to 8 kilometers altitude, ozone was on average 7% (≈4 nmol/mol) below the 2000 to 2020 climatological mean. Such low ozone, over several months, and at so many stations, has not been observed in any previous year since at least 2000. Atmospheric composition analyses from the Copernicus Atmosphere Monitoring Service and simulations from the NASA GMI model indicate that the large 2020 springtime ozone depletion in the Arctic stratosphere contributed less than one quarter of the observed tropospheric anomaly. The observed anomaly is consistent with recent chemistry-climate model simulations, which assume emissions reductions similar to those caused by the COVID-19 crisis. COVID-19 related emissions reductions appear to be the major cause for the observed reduced free tropospheric ozone in 2020.

Published
2020

34. Estimates of Ozone-Attributable Burden of Disease in Urban Areas Worldwide

Author

J. Jason West, Owen R. Cooper, D. Malashock, J. Becker, Kai-Lan Chang, M. DeLang, Susan C. Anenberg, and Marc L. Serre

Subjects
Burden of disease, chemistry.chemical_compound, Ozone, chemistry, business.industry, Environmental health, General Earth and Planetary Sciences, Medicine, business, General Environmental Science
Published
2020

35. Global burden of 87 risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019

Author

Soufiane Boufous, Yousef Veisani, Mehran Asadi-Aliabadi, Sharath Burugina Nagaraja, Maziar Moradi-Lakeh, Getachew Mullu Kassa, Edward J Mills, Dimas Ria Angga Pribadi, William James Dangel, Mohamad-Hani Temsah, Catherine O. Johnson, Gregory A. Roth, Giuseppe Gorini, Fariborz Mansour-Ghanaei, Alberto Ortiz, Samad Azari, Assefa Ayalew Ayalew Ayalew Gebreslassie, Salime Goharinezhad, Stephanie R. M. Zimsen, Peng Zheng, Michael Assmus, Elisabetta Pupillo, Bach Xuan Tran, Lal B. Rawal, Narayanaswamy Venketasubramanian, Noushin Mohammadifard, Stephen S Lim, Ata Rafiee, Maria Inês Schmidt, Vincent C. Iannucci, Suzanne Lyn Barker-Collo, Leah E. Cahill, Tauseef Ahmad, Platon D. Lopukhov, Kazumasa Yamagishi, Abdullah Al Mamun, Iqbal R. F. Elyazar, Giovanni Damiani, Mohammad Hossein Bakhshaei, Mehdi Fazlzadeh, Virginia Núñez-Samudio, Alyssa Pennini, Dietrich Plass, Atkilt Esaiyas Etisso, Gebre Teklemariam Demoz, Alexandrea Watson, Arvin Haj-Mirzaian, Paul S Briant, Frank B. Osei, Blair R. Bumgarner, Maciej Banach, Ravensara S. Travillian, Kai-Lan Chang, Shirin Djalalinia, Hasan Yusefzadeh, Silvano Gallus, Seyyed Meysam Mousavi, Bernhard T. Baune, Aaron van Donkelaar, Azeem Majeed, Hans Kromhout, Robert Ancuceanu, Blessing J. Akombi, Pushpendra Singh, Nayu Ikeda, William M. Gardner, Zahid A Butt, Mohammad Abdollahi, Temesgen Yihunie Akalu, Rahman Shiri, Benn Sartorius, Ai-Min Wu, Bing Fang Hwang, Flavia M. Cicuttini, Hiroyasu Iso, Luis Camera, Amin Soheili, Félix Carvalho, Yun Jin Kim, Caleb Mackay Salpeter Irvine, Mehdi Mirzaei-Alavijeh, Iman Halvaei, Saqib Ali, Giulio Castelpietra, Catalina Liliana Andrei, Ali Kazemi Karyani, Parkes J Kendrick, Hamidreza Haririan, Lucas Guimarães Abreu, Mukhammad David Naimzada, Jeff T. Zhao, Samiah Alam, Sorin Hostiuc, Shaun Wen Huey Lee, João Mauricio Castaldelli-Maia, Behzad Karami Matin, Cyrus Alinia, Takahiro Tabuchi, Manu Raj Mathur, Søren Thorgaard Skou, Thomas Khaled Dwayne Classen, Reza Heidari-Soureshjani, Massimo Cirillo, Nikita Otstavnov, Mehdi Bohluli, Ruth W. Kimokoti, Animut Tagele Tamiru, Masoumeh Sadeghi, Mohammad Ali Jahani, Itamar S. Santos, Mekdes Tigistu Yilma, Lars Johansson, Arielle Wilder Eagan, Nevine El Nahas, Silvia Schiavolin, Kevin D. Shield, Dinh-Toi Chu, Shiva Borzouei, Paul S. F. Yip, Beatrix Haddock, Gianfranco Alicandro, Vasily Vlassov, Deanna Anderlini, Giuseppe Remuzzi, Khalid A Altirkawi, Farahnaz Joukar, Aso Mohammad Darwesh, Ratilal Lalloo, Panniyammakal Jeemon, Rohollah Kalhor, Daniel Youngwhan Cho, Weijia Fu, João Pedro Silva, Rodrigo Sarmiento-Suarez, Seth Christopher Yaw Appiah, Mehdi Ahmadi, Jacob Olusegun Olusanya, José Neves, Gaorui Guo, Tomas Y. Yeheyis, John S. Ji, Charles D. H. Parry, Maryam Ghadimi, Seyed-Mohammad Fereshtehnejad, Serge Resnikoff, Anna E. Torre, Vinod C Nayak, Jamileh Shadid, Susanne Breitner, Mohammad Khammarnia, Mathilde Touvier, Ensiyeh Jenabi, Hosna Janjani, Floriane Ausloos, Irmina Maria Michalek, Alexandra S. Boon-Dooley, Jessica A. Cruz, Syed Mohamed Aljunid, Abiodun M. Adeoye, André Faro, Bartosz Miazgowski, Jobert Richie Nansseu, Erin C Mullany, Giannina Ferrara, Martin McKee, Emmanuel Peprah, Oommen John, Reza Saeedi, Yasser Vasseghian, Dragos Virgil Davitoiu, Sarah Wulf Hanson, Yingxi Zhao, Omid Shafaat, Ali Rajabpour-Sanati, Farid Najafi, Ana Maria Mantilla Herrera, Fatemeh Rajati, Tarun Gupta, Łukasz Szumowski, Mohammed Ibrahim Mohialdeen Gubari, Peter Njenga Keiyoro, Dharmesh Kumar Lal, Zhi Jiang Zhang, Osayomwanbo Osarenotor, Tanvir M. Huda, Perminder S. Sachdev, Farhad Ghamari, Era Upadhyay, Vivek Kumar, Guoqing Hu, Vinay Nangia, Vladimir Andreevich Korshunov, Saeed Shahabi, Golnaz Heidari, Ashraf Nabhan, Robert C. Reiner, Aziz Rezapour, Justin J. Lang, Rakhi Dandona, Josephine W. Ngunjiri, Anna-Karin Danielsson, André Karch, Filippo Ariani, Ahmed Abdelalim, Masih Tajdini, Stefanos Tyrovolas, Mohamed Hsairi, Jae Il Shin, Jasvinder A. Singh, Meghan D. Mooney, Fiona B. Bennitt, Hesam Alizade, Segun Emmanuel Ibitoye, Pradhum Ram, Soraya Siabani, Evanson Z. Sambala, Reza Malekzadeh, Falk Schwendicke, Lalit Dandona, Masoud Moradi, Molly R Nixon, Roya Mirzaei, Rachel Feldman, Hosni Salem, Alberto L. García-Basteiro, Lorainne Tudor Car, Sharareh Eskandarieh, Ramesh Holla, Ritesh G. Menezes, Taraneh Yousefinezhadi, Hai Quang Pham, Hamideh Salimzadeh, Luisa Sorio Flor, Priya Rathi, Ali Bijani, Harvey Whiteford, Shanshan Li, Aleksandr Y. Aravkin, Joshua A. Salomon, Franz Castro, Lisa M. Force, Abdilahi Yousuf Yousuf, Mina Anjomshoa, Ken Takahashi, Maigeng Zhou, Telma Zahirian Moghadam, Maseer Khan, Irina Filip, Santi Martini, Randah R. Hamadeh, Somayeh Bohlouli, Joana Morgado-da-Costa, Tim Driscoll, Jingkai Wei, Hermann Brenner, Reza Rawassizadeh, Radoslaw Sierpinski, Saman Esmaeilnejad, Emmanuel Wandera Okunga, Marisa Freitas, Srikanta Banerjee, Feleke Mekonnen Demeke, Shahin Soltani, Naser Mohammad Gholi Mezerji, Reed J D Sorensen, Elena V. Gnedovskaya, Johan Ärnlöv, Ivo Rakovac, Reza Mohammadpourhodki, Hussain Jafari, Atte Meretoja, Yasir Waheed, Fahad Alanezi, Arya Haj-Mirzaian, Sezer Kisa, Jaifred Christian F. Lopez, Khalid F. Alhabib, Basema Saddik, Michael R. Phillips, Vishnu Renjith, Yafeng Wang, Kevan R. Polkinghorne, Liliana G Ciobanu, Jacek Jerzy Jozwiak, Gelin Xu, Richard C. Franklin, Junjie Wu, Thomas R. Hird, Mohammad Zamani, Diana Silva, Fotis Topouzis, Irena Ilic, Jeffrey D. Stanaway, Hadi Pourjafar, Huong Lan Thi Nguyen, Khaled Khatab, Ammas Siraj Mohammed, Ziyad Al-Aly, A. A. Fomenkov, Charlie Ashbaugh, Mowafa Househ, Paul I. Dargan, Endalkachew Worku Mengesha, Shaimaa I. El-Jaafary, Ernoiz Antriyandarti, Iffat Elbarazi, Dara K. Mohammad, Naznin Hossain, Reza Shirkoohi, João M. Furtado, Arash Ziapour, Morteza Jafarinia, M. Mofizul Islam, Anamika Pandey, Ahmed I. Hasaballah, Tanuj Kanchan, Lee Ling Lim, Muktar Omer Omer, Charles D.A. Wolfe, Ulrich O Mueller, Helen Bitew, V. Prakash, Fereshteh Mehri, Randall V. Martin, Claudiu Herteliu, Constance D. Pond, Min Jeong Shin, Morteza Oladnabi, Antonio Maria Borzì, Yousef Mohammad, Yuichiro Yano, Luke D. Knibbs, Kevin S Ikuta, Maryam Mirzaei, Andreea Mirica, Sofia Boston Redford, Siamak Sabour, Mariya Vladimirovna Titova, Davood Anvari, Ghobad Moradi, Jordi Alonso, Shiwei Liu, Theo Vos, Tuomo J. Meretoja, Ireneous N. Soyiri, Owen R. Cooper, Seyed Sina Naghibi Irvani, Kejia Hu, Hannah J. Henrikson, Sanni Yaya, Robert P. Dellavalle, Chidozie Declan Iwu, Sergey Soshnikov, Jee-Young Jasmine Choi, Satinath Mukhopadhyay, Zabihollah Yousefi, Iman El Sayed, Biresaw Wassihun Alemu, Neeti Kapoor, Ahmad Daryani, Cathleen Keller, Ibrahim Abdollahpour, Nuno Taveira, Masoud Foroutan, Fatemeh Amiri, Mariam Molokhia, Songhomitra Panda-Jonas, Amy E. Peden, Rufus Akinyemi, Ejaz Ahmad Khan, Mostafa Amini-Rarani, J. Becker, Hamidreza Pazoki Toroudi, Ben Lacey, Kidanemaryam Berhe, Simon Yadgir, Spencer L. James, Cory N. Spencer, Aletta E. Schutte, Mu'awiyyah Babale Sufiyan, Arash Tehrani-Banihashemi, Govinda Prasad Dhungana, Fereshteh Ansari, Rixing Xu, Jonathan F. Mosser, Josep Maria Haro, Enrico Rubagotti, Anurag Agrawal, Zahiruddin Quazi Syed, Hoa Thi Do, Ettore Beghi, Seyed Mohammad Kazem Aghamir, Abraham Geremew, Alireza Esteghamati, Paramjit Gill, Farzad Jalilian, J. Jason West, Mikhail Sergeevich Zastrozhin, Albertino Damasceno, Bruce Bartholow Duncan, Nazir Fattahi, Daniel Cury Ribeiro, Michael Brauer, Mark A. Stokes, Veincent Christian Filipino Pepito, Lidia Morawska, Fiona J Charlson, Babak Moazen, Dejana Braithwaite, Jonathan M. Kocarnik, Sergio I. Prada, Andrea Farioli, Zahra Atafar, Heather Orpana, Daniela Balzi, Marco Vacante, Rahmatollah Moradzadeh, Jacek A. Kopec, Iván Landires, Robert G. Weintraub, Leonardo Roever, Yannick Béjot, Markus P. Schlaich, Hamed Zandian, Subramanian Senthilkumaran, Klara Dokova, Vahid Alipour, Sowmya J. Rao, Rakesh Ghosh, Omar Mukhtar Salman, Faris Lami, Marcos Roberto Tovani-Palone, Hamid Reza Tohidinik, Seyed Mohammad Riahi, Adnan Kisa, Ghasem Azarian, Caroline Stein, Zulfiqar A. Bhutta, Sinead Langan, Kunihiro Matsushita, Lauren E. Schaeffer, Kathryn H. Jacobsen, Mehdi Naderi, Mehdi Hosseinzadeh, Nizal Sarrafzadegan, Bahram Armoon, Dan J. Stein, Jessica Fanzo, Morenike Oluwatoyin Folayan, Luca Ronfani, Phetole Walter Mahasha, Nicholas J K Breitborde, Adrian Oţoiu, Arash Sarveazad, Marc L. Serre, Mostafa Hosseini, Nicolas Cherbuin, George D. Thurston, Jalal Arabloo, Mokhtar Mahdavi, Andre Rodrigues Duraes, Ana Laura Manda, Ahmed Abualhasan, Nima Rezaei, Yuan-Pang Wang, Theodore Patrick Younker, Alireza Ansari-Moghaddam, Borhan Mansouri, Adam E. Berman, Khezar Hayat, Rosario Cárdenas, Bolajoko O. Olusanya, Tommi Vasankari, Simon I. Hay, Fatemeh Ghaffarifar, Leo Stockfelt, Ai Koyanagi, Giorgia Giussani, Yuming Guo, Sonia Saxena, Deborah Carvalho Malta, Mehedi Hasan, Negar Rezaei, Maja Pasovic, Jukka Takala, Bogdan Oancea, Sare Safi, A. Werdecker, Florian Fischer, Beatriz Paulina Ayala Quintanilla, Muhammad Shahdaat Bin Sayeed, Takeshi Fukumoto, Maha Atout, Brijesh Sathian, Cristina Bosetti, Mitchell T. Wallin, Agegnehu Bante, Helen Ippolito, Anthony Masaka, Chantal Huynh, Justin Lo, Jordan Weiss, Joemer C. Maravilla, Alberto Raggi, Peter W. Gething, Cristiana Abbafati, Daniela Ribeiro, Mohammad Farahmand, Yetunde O. John-Akinola, Isabela M. Benseñor, Emilie R Maddison, John J. McGrath, Salman Khazaei, Scott B. Patten, Jean Jacques Noubiap, Emmanuela Gakidou, Celine M. Barthelemy, Ashish Badiye, George C. Patton, Obinna Onwujekwe, Peter Allebeck, Victor Aboyans, Olayinka Stephen Ilesanmi, Mousa Yaminfirooz, Neeraj Bedi, Nicholas L S Roberts, Joht Singh Chandan, Hans W. Hoek, Usman Iqbal, Nima Hafezi-Nejad, Haley Lescinsky, Naohiro Yonemoto, Ahmad Ghashghaee, Anders Larsson, David C. Schwebel, Milena Ilic, Richard T. Burnett, Yang Liu, Carlo La Vecchia, Felix Akpojene Ogbo, Morteza Naserbakht, Sangram Kishor Patel, Melanie S. Hammer, Prasanna Mithra, Mohammad Amin Bahrami, Kanyin L. Ong, Mona Pathak, Afshin Maleki, Saeid Safiri, Masood Ali Shaikh, Kate Causey, Michael R.M. Abrigo, Jost B. Jonas, Dian Kusuma, Jagadish Rao Padubidri, Mihajlo Jakovljevic, Ali S. Shalash, Abdiwahab Hashi, Ionut Negoi, Nataliya A. Foigt, Andrey Nikolaevich Briko, François Alla, Giuseppe Grosso, Houman Goudarzi, Di H. Cross, Vera Marisa Costa, Eduarda Fernandes, Chandrashekhar T Sreeramareddy, Odgerel Chimed-Ochir, Sonia Rodríguez-Ramírez, Bo Norrving, Kerem Shuval, Jacob L. Stubbs, Muhammad Ali, Shuhei Nomura, Man Mohan Mehndiratta, Chisom Joyqueenet Akunna, Abdollah Mohammadian-Hafshejani, Navid Rabiee, Dana Bryazka, Hussen Mohammed, Asadollah Gholamian, Ashley Marks, Rizwan Kalani, Molly E. Herbert, Islam Y. Elgendy, Moritz U. G. Kraemer, Chuanhua Yu, Suzanne Polinder, Pascual R. Valdez, Jennifer Rickard, Kylie Ball, Turki Alanzi, Mohsen Bayati, Hamed Mirzaei, Christopher M Odell, Amira Shaheen, Ziad A. Memish, Thirunavukkarasu Sathish, Michael A. Piradov, Hamed Kalani, Lorenzo Monasta, Christopher S Yilgwan, Desalegn Getnet Demsie, Riaz Uddin, Rizwan Suliankatchi Abdulkader, Vinay Srinivasan, Hamid Ahmadieh, Claudio Alberto Dávila-Cervantes, Raffaele Palladino, Chukwudi A Nnaji, Mika Shigematsu, Stein Emil Vollset, Abbas Sheikhtaheri, Paulo A. Lotufo, Nasir Salam, Binyam Minuye Birihane, Mohammad Ali Mansournia, Tomislav Mestrovic, Samer Hamidi, Rajesh Sagar, Mayowa O. Owolabi, Kara Estep, Ester Cerin, Michael T. Chung, Simon Øverland, Amir Taherkhani, Sheng Chia Chung, Martin Amogre Ayanore, Nikolaos Dervenis, Joan B. Soriano, Tahereh Javaheri, Victor Adekanmbi, Seid Tiku Mereta, Gbenga A. Kayode, Christopher R. Cederroth, Razique Anwer, Rajan Nikbakhsh, Kaja Abbas, Fatemeh Heydarpour, Louisa Degenhardt, Tahiya Alam, Mohammad Miri, Alibek Mereke, David Laith Rawaf, Ippazio Cosimo Antonazzo, Erkin M. Mirrakhimov, Seyed Hossein Yahyazadeh Jabbari, Desta Debalkie Atnafu, Davide Sattin, Moslem Soofi, Edris Hasanpoor, Krittika Bhattacharyya, Mika Kivimäki, Nikolay Ivanovich Briko, Joanna L Whisnant, Christopher J L Murray, Simin Mouodi, Alize J. Ferrari, Damian Santomauro, Katrin Burkart, Tudorel Andrei, Alberto Baldasseroni, Hafiz Ansar Rasul Suleria, Valery L. Feigin, Nauman Khalid, Ewerton Cousin, S. Mohammad Sajadi, Francisco Rogerlândio Martins-Melo, Shankar M Bakkannavar, Themba G.G. Ginindza, Sadaf G. Sepanlou, Sheikh Mohammed Shariful Islam, Sanjay Basu, Getinet Ayano, Paula Moraga, Soheil Hassanipour, Jason A. Anderson, Catherine Bisignano, Iyad Sultan, Deepak Kumar Pasupula, Keivan Ahmadi, Fariba Dorostkar, Rajeev Gupta, David M. Pereira, Mustafa Z. Younis, Adel Spotin, Rasmus Havmoeller, Yeshambel T. Nigatu, Barthelemy Kuate Defo, Mithila Faruque, Alan D. Lopez, Shailesh Advani, Behshad Naghshtabrizi, Shane D. Morrison, Inga Dora Sigfusdottir, Konrad Pesudovs, Anna Gershberg Hayoon, Raaj Kishore Biswas, Reshmi Bhageerathy, H. Dean Hosgood, Giulia Carreras, Sarika Chaturvedi, James L. Fisher, In-Hwan Oh, G Anil Kumar, Christoph Nowak, Vijay Kumar Chattu, Puja C Rao, Marcel Ausloos, Ali Kabir, Rannveig Sigurvinsdottir, Leeberk Raja Inbaraj, Edgar Denova-Gutiérrez, Rafael Tabarés-Seisdedos, Minh Nguyen, Ashkan Afshin, Payman Salamati, Colin Angus, Mona M. Khater, Ehsan Sadeghi, Mahalaqua Nazli Khatib, Antonio Biondi, Valentin Yurievich Skryabin, Mohsen Abbasi-Kangevari, Andrew T Olagunju, Amir Radfar, Laith J. Abu-Raddad, Marcello Tonelli, Hesam Ghiasvand, Hanne Christensen, Juan Jesus Carrero, Maryam Adabi, Saravanan Muthupandian, Kurt Straif, Hossein Samadi Kafil, Lope H Barrero, Harish Chander Gugnani, Mohammad Fareed, Morteza Shamsizadeh, Jemal Abdu Mohammed, Juan A Rivera, Shai Linn, Saad M.A. Dahlawi, Janni Leung, Shokofeh Maleki, Mohd Anisul Karim, Kamarul Imran Musa, Farshad Pourmalek, Dietrich Rothenbacher, Kiomars Sharafi, Alessandra C. Goulart, Leila Doshmangir, Gabriele Nagel, Helena Manguerra, Olatunji O. Adetokunboh, Srinivasa Vittal Katikireddi, Nermin Ghith, Maha El Tantawi, Awoke Misganaw, Yunquan Zhang, Carl Abelardo T. Antonio, Vahid Rashedi, Mehran Shams-Beyranvand, Bhaskaran Unnikrishnan, Holly E. Erskine, Ann Kristin Knudsen, Marissa B Reitsma, Getayeneh Antehunegn Tesema, Javad Javidnia, Ismael R. Campos-Nonato, Biniyam Sahiledengle Geberemariyam, Godfrey Mutashambara Rwegerera, Alaa Badawi, James Leigh, Morteza Arab-Zozani, Kyle E. Simpson, Muluken Bekele Sorrie, Roghiyeh Faridnia, Vivekanand Jha, Tomasz Miazgowski, Aaron J Cohen, Chukwuma David Umeokonkwo, Alessandra Lugo, Adhanom Gebreegziabher Baraki, Akshaya Srikanth Bhagavathula, Caitlyn Steiner, Gholamreza Roshandel, Cuong Tat Nguyen, Tania G Sánchez-Pimienta, Ahamarshan Jayaraman Nagarajan, Laura Kemmer, Ihoghosa Osamuyi Iyamu, Seyedeh Zahra Masoumi, Vivian Chia-Rong Hsieh, Kris J. Krohn, Phoebe Anne Rhinehart, Sarah Wozniak, Sahar Saeedi Moghaddam, Kate E. LeGrand, Christian Kieling, Vahid Yazdi-Feyzabadi, Robin Room, Zelalem Nigussie Azene, Kelly Cercy, Paul H. Lee, Stanislav S. Otstavnov, Dinesh Bhandari, Rafael Alves Guimarães, Zemenu Tadesse Tessema, Aziz Sheikh, Michellr L. Bell, Marwa Rashad Salem, Kirsten E. Wiens, Emma U.R. Smith, Hassan Abolhassani, Cristiano Piccinelli, Kedir Hussein Abegaz, G.K. Mini, Christian Razo, Manuela L. Ferreira, Diego De Leo, Francesco Saverio Violante, Aristidis Tsatsakis, Zahra Sadat Dibaji Forooshani, Tea Lallukka, Dickson A. Amugsi, Anna Poznańska, Graeme J. Hankey, Kewal Krishan, Maryam Zamanian, Eirini Skiadaresi, Jai K Das, Felix Greaves, Tessa M. Pilz, Sameer Vali Gopalani, Mansour Ghafourifard, M. DeLang, Morteza Mahmoudi, Alton Lu, Brian J. Hall, Ravi Prakash Jha, David Edvardsson, Xiu Ju George Zhao, Farshad Farzadfar, Hadi Hassankhani, Samuel M. Ostroff, Gerhard Sulo, Keyghobad Ghadiri, Neeraj Bhala, Stefan Lorkowski, Mohammad Rabiee, Sivan Yegnanarayana Iyer Saraswathy, Amirhossein Sahebkar, Rashid Abdi Guled, Abdallah M. Samy, Roman Topor-Madry, Michal Grivna, Afsaneh Arzani, Ayesha Humayun, Simin Liu, Maryam Khayamzadeh, Davoud Adham, Ahad Bakhtiari, Shafiu Mohammed, Paolo Lauriola, Abbas Mosapour, Sophia Emmons-Bell, Khurshid Alam, Rajat Das Gupta, Matilde Leonardi, Muktar Beshir Ahmed, Jeffrey V. Lazarus, Mohamed M. Gad, Kelly Compton, Leila R Kalankesh, Abdelrahman Ibrahim Abushouk, Mikk Jürisson, Catherine M. Antony, Ali A. Asadi-Pooya, Daniel Diaz, Salman Rawaf, Gina Agarwal, Ted R. Miller, Rebecca Ivers, João Vasco Santos, Savita Lasrado, Abdul Moiz Hafiz, Amir Almasi-Hashiani, Praveen Hoogar, Fares Alahdab, Om P Kurmi, Anbissa Muleta Senbeta, Tomi Akinyemiju, Boris Bikbov, Muhammad Aziz Rahman, Amin Mousavi Khaneghah, Yahya Safari, Guilherme Borges, Carlos A Castañeda-Orjuela, Kenji Shibuya, Bahman Yousefi, Berrin Serdar, Karen M. Tabb, Sonali Kochhar, Till Bärnighausen, Kala M. Mehta, Mostafa Dianatinasab, Arash Etemadi, Melissa Y. Wei, Kiana Ramezanzadeh, Lingkan Barua, Zubair Kabir, Rade Vukovic, Hesham M. Al-Mekhlafi, Shoshana H. Ballew, Miriam Levi, Zainab Samad, Florentino Luciano Caetano dos Santos, Juan Sanabria, Ramu Rawat, Chinwe Juliana Iwu, Gabrielle B. Britton, Colm McAlinden, Mohsen Naghavi, Maarten J. Postma, Chhabi Lal Ranabhat, Jalil Jaafari, Walter Mendoza, Mahesh P A, Pallab K. Maulik, Ali H. Mokdad, Andre M. N. Renzaho, Milena Santric-Milicevic, Parvaiz A Koul, Foad Abd-Allah, Mihaela Hostiuc, Richard G. Cowden, Ronny Westerman, Meghdad Pirsaheb, Department of Earth Observation Science, UT-I-ITC-ACQUAL, Faculty of Geo-Information Science and Earth Observation, Real World Studies in PharmacoEpidemiology, -Genetics, -Economics and -Therapy (PEGET), Value, Affordability and Sustainability (VALUE), Microbes in Health and Disease (MHD), Tampere University, Tays Research Services, Health Sciences, Murray, C. J. L., Aravkin, A. Y., Zheng, P., Abbafati, C., Abbas, K. M., Abbasi-Kangevari, M., Abd-Allah, F., Abdelalim, A., Abdollahi, M., Abdollahpour, I., Abegaz, K. H., Abolhassani, H., Aboyans, V., Abreu, L. G., Abrigo, M. R. M., Abualhasan, A., Abu-Raddad, L. J., Abushouk, A. I., Adabi, M., Adekanmbi, V., Adeoye, A. M., Adetokunboh, O. O., Adham, D., Advani, S. M., Agarwal, G., Aghamir, S. M. K., Agrawal, A., Ahmad, T., Ahmadi, K., Ahmadi, M., Ahmadieh, H., Ahmed, M. B., Akalu, T. Y., Akinyemi, R. O., Akinyemiju, T., Akombi, B., Akunna, C. J., Alahdab, F., Al-Aly, Z., Alam, K., Alam, S., Alam, T., Alanezi, F. M., Alanzi, T. M., Alemu, B. W., Alhabib, K. F., Ali, M., Ali, S., Alicandro, G., Alinia, C., Alipour, V., Alizade, H., Aljunid, S. M., Alla, F., Allebeck, P., Almasi-Hashiani, A., Al-Mekhlafi, H. M., Alonso, J., Altirkawi, K. A., Amini-Rarani, M., Amiri, F., Amugsi, D. A., Ancuceanu, R., Anderlini, D., Anderson, J. A., Andrei, C. L., Andrei, T., Angus, C., Anjomshoa, M., Ansari, F., Ansari-Moghaddam, A., Antonazzo, I. C., Antonio, C. A. T., Antony, C. M., Antriyandarti, E., Anvari, D., Anwer, R., Appiah, S. C. Y., Arabloo, J., Arab-Zozani, M., Ariani, F., Armoon, B., Arnlov, J., Arzani, A., Asadi-Aliabadi, M., Asadi-Pooya, A. A., Ashbaugh, C., Assmus, M., Atafar, Z., Atnafu, D. D., Atout, M. M. W., Ausloos, F., Ausloos, M., Ayala Quintanilla, B. P., Ayano, G., Ayanore, M. A., Azari, S., Azarian, G., Azene, Z. N., Badawi, A., Badiye, A. D., Bahrami, M. A., Bakhshaei, M. H., Bakhtiari, A., Bakkannavar, S. M., Baldasseroni, A., Ball, K., Ballew, S. H., Balzi, D., Banach, M., Banerjee, S. K., Bante, A. B., Baraki, A. G., Barker-Collo, S. L., Barnighausen, T. W., Barrero, L. H., Barthelemy, C. M., Barua, L., Basu, S., Baune, B. T., Bayati, M., Becker, J. S., Bedi, N., Beghi, E., Bejot, Y., Bell, M. L., Bennitt, F. B., Bensenor, I. M., Berhe, K., Berman, A. E., Bhagavathula, A. S., Bhageerathy, R., Bhala, N., Bhandari, D., Bhattacharyya, K., Bhutta, Z. A., Bijani, A., Bikbov, B., Bin Sayeed, M. S., Biondi, A., Birihane, B. M., Bisignano, C., Biswas, R. K., Bitew, H., Bohlouli, S., Bohluli, M., Boon-Dooley, A. S., Borges, G., Borzi, A. M., Borzouei, S., Bosetti, C., Boufous, S., Braithwaite, D., Breitborde, N. J. K., Breitner, S., Brenner, H., Briant, P. S., Briko, A. N., Briko, N. I., Britton, G. B., Bryazka, D., Bumgarner, B. R., Burkart, K., Burnett, R. T., Burugina Nagaraja, S., Butt, Z. A., Caetano dos Santos, F. L., Cahill, L. E., Camera, L. L. A., Campos-Nonato, I. R., Cardenas, R., Carreras, G., Carrero, J. J., Carvalho, F., Castaldelli-Maia, J. M., Castaneda-Orjuela, C. A., Castelpietra, G., Castro, F., Causey, K., Cederroth, C. R., Cercy, K. M., Cerin, E., Chandan, J. S., Chang, K. -L., Charlson, F. J., Chattu, V. K., Chaturvedi, S., Cherbuin, N., Chimed-Ochir, O., Cho, D. Y., Choi, J. -Y. J., Christensen, H., Chu, D. -T., Chung, M. T., Chung, S. -C., Cicuttini, F. M., Ciobanu, L. G., Cirillo, M., Classen, T. K. D., Cohen, A. J., Compton, K., Cooper, O. R., Costa, V. M., Cousin, E., Cowden, R. G., Cross, D. H., Cruz, J. A., Dahlawi, S. M. A., Damasceno, A. A. M., Damiani, G., Dandona, L., Dandona, R., Dangel, W. J., Danielsson, A. -K., Dargan, P. I., Darwesh, A. M., Daryani, A., Das, J. K., Das Gupta, R., das Neves, J., Davila-Cervantes, C. A., Davitoiu, D. V., De Leo, D., Degenhardt, L., Delang, M., Dellavalle, R. P., Demeke, F. M., Demoz, G. T., Demsie, D. G., Denova-Gutierrez, E., Dervenis, N., Dhungana, G. P., Dianatinasab, M., Dias da Silva, D., Diaz, D., Dibaji Forooshani, Z. S., Djalalinia, S., Do, H. T., Dokova, K., Dorostkar, F., Doshmangir, L., Driscoll, T. R., Duncan, B. B., Duraes, A. R., Eagan, A. W., Edvardsson, D., El Nahas, N., El Sayed, I., El Tantawi, M., Elbarazi, I., Elgendy, I. Y., El-Jaafary, S. I., Elyazar, I. R., Emmons-Bell, S., Erskine, H. E., Eskandarieh, S., Esmaeilnejad, S., Esteghamati, A., Estep, K., Etemadi, A., Etisso, A. E., Fanzo, J., Farahmand, M., Fareed, M., Faridnia, R., Farioli, A., Faro, A., Faruque, M., Farzadfar, F., Fattahi, N., Fazlzadeh, M., Feigin, V. L., Feldman, R., Fereshtehnejad, S. -M., Fernandes, E., Ferrara, G., Ferrari, A. J., Ferreira, M. L., Filip, I., Fischer, F., Fisher, J. L., Flor, L. S., Foigt, N. A., Folayan, M. O., Fomenkov, A. A., Force, L. M., Foroutan, M., Franklin, R. C., Freitas, M., Fu, W., Fukumoto, T., Furtado, J. M., Gad, M. M., Gakidou, E., Gallus, S., Garcia-Basteiro, A. L., Gardner, W. M., Geberemariyam, B. S., Gebreslassie, A. A. A. A., Geremew, A., Gershberg Hayoon, A., Gething, P. W., Ghadimi, M., Ghadiri, K., Ghaffarifar, F., Ghafourifard, M., Ghamari, F., Ghashghaee, A., Ghiasvand, H., Ghith, N., Gholamian, A., Ghosh, R., Gill, P. S., Ginindza, T. G. G., Giussani, G., Gnedovskaya, E. V., Goharinezhad, S., Gopalani, S. V., Gorini, G., Goudarzi, H., Goulart, A. C., Greaves, F., Grivna, M., Grosso, G., Gubari, M. I. M., Gugnani, H. C., Guimaraes, R. A., Guled, R. A., Guo, G., Guo, Y., Gupta, R., Gupta, T., Haddock, B., Hafezi-Nejad, N., Hafiz, A., Haj-Mirzaian, A., Hall, B. J., Halvaei, I., Hamadeh, R. R., Hamidi, S., Hammer, M. S., Hankey, G. J., Haririan, H., Haro, J. M., Hasaballah, A. I., Hasan, M. M., Hasanpoor, E., Hashi, A., Hassanipour, S., Hassankhani, H., Havmoeller, R. J., Hay, S. I., Hayat, K., Heidari, G., Heidari-Soureshjani, R., Henrikson, H. J., Herbert, M. E., Herteliu, C., Heydarpour, F., Hird, T. R., Hoek, H. W., Holla, R., Hoogar, P., Hosgood, H. D., Hossain, N., Hosseini, M., Hosseinzadeh, M., Hostiuc, M., Hostiuc, S., Househ, M., Hsairi, M., Hsieh, V. C. -R., Hu, G., Hu, K., Huda, T. M., Humayun, A., Huynh, C. K., Hwang, B. -F., Iannucci, V. C., Ibitoye, S. E., Ikeda, N., Ikuta, K. S., Ilesanmi, O. S., Ilic, I. M., Ilic, M. D., Inbaraj, L. R., Ippolito, H., Iqbal, U., Irvani, S. S. N., Irvine, C. M. S., Islam, M. M., Islam, S. M. S., Iso, H., Ivers, R. Q., Iwu, C. C. D., Iwu, C. J., Iyamu, I. O., Jaafari, J., Jacobsen, K. H., Jafari, H., Jafarinia, M., Jahani, M. A., Jakovljevic, M., Jalilian, F., James, S. L., Janjani, H., Javaheri, T., Javidnia, J., Jeemon, P., Jenabi, E., Jha, R. P., Jha, V., Ji, J. S., Johansson, L., John, O., John-Akinola, Y. O., Johnson, C. O., Jonas, J. B., Joukar, F., Jozwiak, J. J., Jurisson, M., Kabir, A., Kabir, Z., Kalani, H., Kalani, R., Kalankesh, L. R., Kalhor, R., Kanchan, T., Kapoor, N., Karami Matin, B., Karch, A., Karim, M. A., Kassa, G. M., Katikireddi, S. V., Kayode, G. A., Kazemi Karyani, A., Keiyoro, P. N., Keller, C., Kemmer, L., Kendrick, P. J., Khalid, N., Khammarnia, M., Khan, E. A., Khan, M., Khatab, K., Khater, M. M., Khatib, M. N., Khayamzadeh, M., Khazaei, S., Kieling, C., Kim, Y. J., Kimokoti, R. W., Kisa, A., Kisa, S., Kivimaki, M., Knibbs, L. D., Knudsen, A. K. S., Kocarnik, J. M., Kochhar, S., Kopec, J. A., Korshunov, V. A., Koul, P. A., Koyanagi, A., Kraemer, M. U. G., Krishan, K., Krohn, K. J., Kromhout, H., Kuate Defo, B., Kumar, G. A., Kumar, V., Kurmi, O. P., Kusuma, D., La Vecchia, C., Lacey, B., Lal, D. K., Lalloo, R., Lallukka, T., Lami, F. H., Landires, I., Lang, J. J., Langan, S. M., Larsson, A. O., Lasrado, S., Lauriola, P., Lazarus, J. V., Lee, P. H., Lee, S. W. H., Legrand, K. E., Leigh, J., Leonardi, M., Lescinsky, H., Leung, J., Levi, M., Li, S., Lim, L. -L., Linn, S., Liu, S., Liu, Y., Lo, J., Lopez, A. D., Lopez, J. C. F., Lopukhov, P. D., Lorkowski, S., Lotufo, P. A., Lu, A., Lugo, A., Maddison, E. R., Mahasha, P. W., Mahdavi, M. M., Mahmoudi, M., Majeed, A., Maleki, A., Maleki, S., Malekzadeh, R., Malta, D. C., Mamun, A. A., Manda, A. L., Manguerra, H., Mansour-Ghanaei, F., Mansouri, B., Mansournia, M. A., Mantilla Herrera, A. M., Maravilla, J. C., Marks, A., Martin, R. V., Martini, S., Martins-Melo, F. R., Masaka, A., Masoumi, S. Z., Mathur, M. R., Matsushita, K., Maulik, P. K., Mcalinden, C., Mcgrath, J. J., Mckee, M., Mehndiratta, M. M., Mehri, F., Mehta, K. M., Memish, Z. A., Mendoza, W., Menezes, R. G., Mengesha, E. W., Mereke, A., Mereta, S. T., Meretoja, A., Meretoja, T. J., Mestrovic, T., Miazgowski, B., Miazgowski, T., Michalek, I. M., Miller, T. R., Mills, E. J., Mini, G. K., Miri, M., Mirica, A., Mirrakhimov, E. M., Mirzaei, H., Mirzaei, M., Mirzaei, R., Mirzaei-Alavijeh, M., Misganaw, A. T., Mithra, P., Moazen, B., Mohammad, D. K., Mohammad, Y., Mohammad Gholi Mezerji, N., Mohammadian-Hafshejani, A., Mohammadifard, N., Mohammadpourhodki, R., Mohammed, A. S., Mohammed, H., Mohammed, J. A., Mohammed, S., Mokdad, A. H., Molokhia, M., Monasta, L., Mooney, M. D., Moradi, G., Moradi, M., Moradi-Lakeh, M., Moradzadeh, R., Moraga, P., Morawska, L., Morgado-da-Costa, J., Morrison, S. D., Mosapour, A., Mosser, J. F., Mouodi, S., Mousavi, S. M., Mousavi Khaneghah, A., Mueller, U. O., Mukhopadhyay, S., Mullany, E. C., Musa, K. I., Muthupandian, S., Nabhan, A. F., Naderi, M., Nagarajan, A. J., Nagel, G., Naghavi, M., Naghshtabrizi, B., Naimzada, M. D., Najafi, F., Nangia, V., Nansseu, J. R., Naserbakht, M., Nayak, V. C., Negoi, I., Ngunjiri, J. W., Nguyen, C. T., Nguyen, H. L. T., Nguyen, M., Nigatu, Y. T., Nikbakhsh, R., Nixon, M. R., Nnaji, C. A., Nomura, S., Norrving, B., Noubiap, J. J., Nowak, C., Nunez-Samudio, V., Otoiu, A., Oancea, B., Odell, C. M., Ogbo, F. A., Oh, I. -H., Okunga, E. W., Oladnabi, M., Olagunju, A. T., Olusanya, B. O., Olusanya, J. O., Omer, M. O., Ong, K. L., Onwujekwe, O. E., Orpana, H. M., Ortiz, A., Osarenotor, O., Osei, F. B., Ostroff, S. M., Otstavnov, N., Otstavnov, S. S., Overland, S., Owolabi, M. O., P A, M., Padubidri, J. R., Palladino, R., Panda-Jonas, S., Pandey, A., Parry, C. D. H., Pasovic, M., Pasupula, D. K., Patel, S. K., Pathak, M., Patten, S. B., Patton, G. C., Pazoki Toroudi, H., Peden, A. E., Pennini, A., Pepito, V. C. F., Peprah, E. K., Pereira, D. M., Pesudovs, K., Pham, H. Q., Phillips, M. R., Piccinelli, C., Pilz, T. M., Piradov, M. A., Pirsaheb, M., Plass, D., Polinder, S., Polkinghorne, K. R., Pond, C. D., Postma, M. J., Pourjafar, H., Pourmalek, F., Poznanska, A., Prada, S. I., Prakash, V., Pribadi, D. R. A., Pupillo, E., Quazi Syed, Z., Rabiee, M., Rabiee, N., Radfar, A., Rafiee, A., Raggi, A., Rahman, M. A., Rajabpour-Sanati, A., Rajati, F., Rakovac, I., Ram, P., Ramezanzadeh, K., Ranabhat, C. L., Rao, P. C., Rao, S. J., Rashedi, V., Rathi, P., Rawaf, D. L., Rawaf, S., Rawal, L., Rawassizadeh, R., Rawat, R., Razo, C., Redford, S. B., Reiner, R. C., Reitsma, M. B., Remuzzi, G., Renjith, V., Renzaho, A. M. N., Resnikoff, S., Rezaei, N., Rezapour, A., Rhinehart, P. -A., Riahi, S. M., Ribeiro, D. C., Ribeiro, D., Rickard, J., Rivera, J. A., Roberts, N. L. S., Rodriguez-Ramirez, S., Roever, L., Ronfani, L., Room, R., Roshandel, G., Roth, G. A., Rothenbacher, D., Rubagotti, E., Rwegerera, G. M., Sabour, S., Sachdev, P. S., Saddik, B., Sadeghi, E., Sadeghi, M., Saeedi, R., Saeedi Moghaddam, S., Safari, Y., Safi, S., Safiri, S., Sagar, R., Sahebkar, A., Sajadi, S. M., Salam, N., Salamati, P., Salem, H., Salem, M. R. R., Salimzadeh, H., Salman, O. M., Salomon, J. A., Samad, Z., Samadi Kafil, H., Sambala, E. Z., Samy, A. M., Sanabria, J., Sanchez-Pimienta, T. G., Santomauro, D. F., Santos, I. S., Santos, J. V., Santric-Milicevic, M. M., Saraswathy, S. Y. I., Sarmiento-Suarez, R., Sarrafzadegan, N., Sartorius, B., Sarveazad, A., Sathian, B., Sathish, T., Sattin, D., Saxena, S., Schaeffer, L. E., Schiavolin, S., Schlaich, M. P., Schmidt, M. I., Schutte, A. E., Schwebel, D. C., Schwendicke, F., Senbeta, A. M., Senthilkumaran, S., Sepanlou, S. G., Serdar, B., Serre, M. L., Shadid, J., Shafaat, O., Shahabi, S., Shaheen, A. A., Shaikh, M. A., Shalash, A. S., Shams-Beyranvand, M., Shamsizadeh, M., Sharafi, K., Sheikh, A., Sheikhtaheri, A., Shibuya, K., Shield, K. D., Shigematsu, M., Shin, J. I., Shin, M. -J., Shiri, R., Shirkoohi, R., Shuval, K., Siabani, S., Sierpinski, R., Sigfusdottir, I. D., Sigurvinsdottir, R., Silva, J. P., Simpson, K. E., Singh, J. A., Singh, P., Skiadaresi, E., Skou, S. T. S., Skryabin, V. Y., Smith, E. U. R., Soheili, A., Soltani, S., Soofi, M., Sorensen, R. J. D., Soriano, J. B., Sorrie, M. B., Soshnikov, S., Soyiri, I. N., Spencer, C. N., Spotin, A., Sreeramareddy, C. T., Srinivasan, V., Stanaway, J. D., Stein, C., Stein, D. J., Steiner, C., Stockfelt, L., Stokes, M. A., Straif, K., Stubbs, J. L., Sufiyan, M. B., Suleria, H. A. R., Suliankatchi Abdulkader, R., Sulo, G., Sultan, I., Szumowski, Tabares-Seisdedos, R., Tabb, K. M., Tabuchi, T., Taherkhani, A., Tajdini, M., Takahashi, K., Takala, J. S., Tamiru, A. T., Taveira, N., Tehrani-Banihashemi, A., Temsah, M. -H., Tesema, G. A., Tessema, Z. T., Thurston, G. D., Titova, M. V., Tohidinik, H. R., Tonelli, M., Topor-Madry, R., Topouzis, F., Torre, A. E., Touvier, M., Tovani-Palone, M. R. R., Tran, B. X., Travillian, R., Tsatsakis, A., Tudor Car, L., Tyrovolas, S., Uddin, R., Umeokonkwo, C. D., Unnikrishnan, B., Upadhyay, E., Vacante, M., Valdez, P. R., van Donkelaar, A., Vasankari, T. J., Vasseghian, Y., Veisani, Y., Venketasubramanian, N., Violante, F. S., Vlassov, V., Vollset, S. E., Vos, T., Vukovic, R., Waheed, Y., Wallin, M. T., Wang, Y., Wang, Y. -P., Watson, A., Wei, J., Wei, M. Y. W., Weintraub, R. G., Weiss, J., Werdecker, A., West, J. J., Westerman, R., Whisnant, J. L., Whiteford, H. A., Wiens, K. E., Wolfe, C. D. A., Wozniak, S. S., Wu, A. -M., Wu, J., Wulf Hanson, S., Xu, G., Xu, R., Yadgir, S., Yahyazadeh Jabbari, S. H., Yamagishi, K., Yaminfirooz, M., Yano, Y., Yaya, S., Yazdi-Feyzabadi, V., Yeheyis, T. Y., Yilgwan, C. S., Yilma, M. T., Yip, P., Yonemoto, N., Younis, M. Z., Younker, T. P., Yousefi, B., Yousefi, Z., Yousefinezhadi, T., Yousuf, A. Y., Yu, C., Yusefzadeh, H., Zahirian Moghadam, T., Zamani, M., Zamanian, M., Zandian, H., Zastrozhin, M. S., Zhang, Y., Zhang, Z. -J., Zhao, J. T., Zhao, X. -J. G., Zhao, Y., Zhou, M., Ziapour, A., Zimsen, S. R. M., Brauer, M., Afshin, A., Lim, S. S., Lacey, B, Sartorius, B, Abbafati, C, Abbas, K, Abbasi-Kangevari, M, Abd-Allah, F, Abdelalim, A, Abdollahi, M, Abdollahpour, I, Abegaz, K, Abolhassani, H, Aboyans, V, Abreu, L, Abrigo, M, Abualhasan, A, Abu-Raddad, L, Abushouk, A, Adabi, M, Adekanmbi, V, Adeoye, A, Adetokunboh, O, Adham, D, Advani, S, Afshin, A, Agarwal, G, Aghamir, S, Agrawal, A, Ahmad, T, Ahmadi, K, Ahmadi, M, Ahmadieh, H, Ahmed, M, Akalu, T, Akinyemi, R, Akinyemiju, T, Akombi, B, Akunna, C, Alahdab, F, Al-Aly, Z, Alam, K, Alam, S, Alam, T, Alanezi, F, Alanzi, T, Alemu, B, Alhabib, K, Ali, M, Ali, S, Alicandro, G, Alinia, C, Alipour, V, Alizade, H, Aljunid, S, Alla, F, Allebeck, P, Almasi-Hashiani, A, Al-Mekhlafi, H, Alonso, J, Altirkawi, K, Amini-Rarani, M, Amiri, F, Amugsi, D, Ancuceanu, R, Anderlini, D, Anderson, J, Andrei, C, Andrei, T, Angus, C, Anjomshoa, M, Ansari, F, Ansari-Moghaddam, A, Antonazzo, I, Antonio, C, Antony, C, Antriyandarti, E, Anvari, D, Anwer, R, Appiah, S, Arabloo, J, Arab-Zozani, M, Aravkin, A, Ariani, F, Armoon, B, Arnlov, J, Arzani, A, Asadi-Aliabadi, M, Asadi-Pooya, A, Ashbaugh, C, Assmus, M, Atafar, Z, Atnafu, D, Atout, M, Ausloos, F, Ausloos, M, Ayala Quintanilla, B, Ayano, G, Ayanore, M, Azari, S, Azarian, G, Azene, Z, Badawi, A, Badiye, A, Bahrami, M, Bakhshaei, M, Bakhtiari, A, Bakkannavar, S, Baldasseroni, A, Ball, K, Ballew, S, Balzi, D, Banach, M, Banerjee, S, Bante, A, Baraki, A, Barker-Collo, S, Barnighausen, T, Barrero, L, Barthelemy, C, Barua, L, Basu, S, Baune, B, Bayati, M, Becker, J, Bedi, N, Beghi, E, Bejot, Y, Bell, M, Bennitt, F, Bensenor, I, Berhe, K, Berman, A, Bhagavathula, A, Bhageerathy, R, Bhala, N, Bhandari, D, Bhattacharyya, K, Bhutta, Z, Bijani, A, Bikbov, B, Bin Sayeed, M, Biondi, A, Birihane, B, Bisignano, C, Biswas, R, Bitew, H, Bohlouli, S, Bohluli, M, Boon-Dooley, A, Borges, G, Borzi, A, Borzouei, S, Bosetti, C, Boufous, S, Braithwaite, D, Brauer, M, Breitborde, N, Breitner, S, Brenner, H, Briant, P, Briko, A, Briko, N, Britton, G, Bryazka, D, Bumgarner, B, Burkart, K, Burnett, R, Burugina Nagaraja, S, Butt, Z, Caetano Dos Santos, F, Cahill, L, Camera, L, Campos-Nonato, I, Cardenas, R, Carreras, G, Carrero, J, Carvalho, F, Castaldelli-Maia, J, Castaneda-Orjuela, C, Castelpietra, G, Castro, F, Causey, K, Cederroth, C, Cercy, K, Cerin, E, Chandan, J, Chang, K, Charlson, F, Chattu, V, Chaturvedi, S, Cherbuin, N, Chimed-Ochir, O, Cho, D, Choi, J, Christensen, H, Chu, D, Chung, M, Chung, S, Cicuttini, F, Ciobanu, L, Cirillo, M, Classen, T, Cohen, A, Compton, K, Cooper, O, Costa, V, Cousin, E, Cowden, R, Cross, D, Cruz, J, Dahlawi, S, Damasceno, A, Damiani, G, Dandona, L, Dandona, R, Dangel, W, Danielsson, A, Dargan, P, Darwesh, A, Daryani, A, Das, J, Das Gupta, R, das Neves, J, Davila-Cervantes, C, Davitoiu, D, De Leo, D, Degenhardt, L, Delang, M, Dellavalle, R, Demeke, F, Demoz, G, Demsie, D, Denova-Gutierrez, E, Dervenis, N, Dhungana, G, Dianatinasab, M, Dias da Silva, D, Diaz, D, Dibaji Forooshani, Z, Djalalinia, S, Do, H, Dokova, K, Dorostkar, F, Doshmangir, L, Driscoll, T, Duncan, B, Duraes, A, Eagan, A, Edvardsson, D, El Nahas, N, El Sayed, I, El Tantawi, M, Elbarazi, I, Elgendy, I, El-Jaafary, S, Elyazar, I, Emmons-Bell, S, Erskine, H, Eskandarieh, S, Esmaeilnejad, S, Esteghamati, A, Estep, K, Etemadi, A, Etisso, A, Fanzo, J, Farahmand, M, Fareed, M, Faridnia, R, Farioli, A, Faro, A, Faruque, M, Farzadfar, F, Fattahi, N, Fazlzadeh, M, Feigin, V, Feldman, R, Fereshtehnejad, S, Fernandes, E, Ferrara, G, Ferrari, A, Ferreira, M, Filip, I, Fischer, F, Fisher, J, Flor, L, Foigt, N, Folayan, M, Fomenkov, A, Force, L, Foroutan, M, Franklin, R, Freitas, M, Fu, W, Fukumoto, T, Furtado, J, Gad, M, Gakidou, E, Gallus, S, Garcia-Basteiro, A, Gardner, W, Geberemariyam, B, Ayalew Gebreslassie, A, Geremew, A, Gershberg Hayoon, A, Gething, P, Ghadimi, M, Ghadiri, K, Ghaffarifar, F, Ghafourifard, M, Ghamari, F, Ghashghaee, A, Ghiasvand, H, Ghith, N, Gholamian, A, Ghosh, R, Gill, P, Ginindza, T, Giussani, G, Gnedovskaya, E, Goharinezhad, S, Gopalani, S, Gorini, G, Goudarzi, H, Goulart, A, Greaves, F, Grivna, M, Grosso, G, Gubari, M, Gugnani, H, Guimaraes, R, Guled, R, Guo, G, Guo, Y, Gupta, R, Gupta, T, Haddock, B, Hafezi-Nejad, N, Hafiz, A, Haj-Mirzaian, A, Hall, B, Halvaei, I, Hamadeh, R, Hamidi, S, Hammer, M, Hankey, G, Haririan, H, Haro, J, Hasaballah, A, Hasan, M, Hasanpoor, E, Hashi, A, Hassanipour, S, Hassankhani, H, Havmoeller, R, Hay, S, Hayat, K, Heidari, G, Heidari-Soureshjani, R, Henrikson, H, Herbert, M, Herteliu, C, Heydarpour, F, Hird, T, Hoek, H, Holla, R, Hoogar, P, Hosgood, H, Hossain, N, Hosseini, M, Hosseinzadeh, M, Hostiuc, M, Hostiuc, S, Househ, M, Hsairi, M, Hsieh, V, Hu, G, Hu, K, Huda, T, Humayun, A, Huynh, C, Hwang, B, Iannucci, V, Ibitoye, S, Ikeda, N, Ikuta, K, Ilesanmi, O, Ilic, I, Ilic, M, Inbaraj, L, Ippolito, H, Iqbal, U, Irvani, S, Irvine, C, Islam, M, Islam, S, Iso, H, Ivers, R, Iwu, C, Iyamu, I, Jaafari, J, Jacobsen, K, Jafari, H, Jafarinia, M, Jahani, M, Jakovljevic, M, Jalilian, F, James, S, Janjani, H, Javaheri, T, Javidnia, J, Jeemon, P, Jenabi, E, Jha, R, Jha, V, Ji, J, Johansson, L, John, O, John-Akinola, Y, Johnson, C, Jonas, J, Joukar, F, Jozwiak, J, Jurisson, M, Kabir, A, Kabir, Z, Kalani, H, Kalani, R, Kalankesh, L, Kalhor, R, Kanchan, T, Kapoor, N, Matin, B, Karch, A, Karim, M, Kassa, G, Katikireddi, S, Kayode, G, Kazemi Karyani, A, Keiyoro, P, Keller, C, Kemmer, L, Kendrick, P, Khalid, N, Khammarnia, M, Khan, E, Khan, M, Khatab, K, Khater, M, Khatib, M, Khayamzadeh, M, Khazaei, S, Kieling, C, Kim, Y, Kimokoti, R, Kisa, A, Kisa, S, Kivimaki, M, Knibbs, L, Knudsen, A, Kocarnik, J, Kochhar, S, Kopec, J, Korshunov, V, Koul, P, Koyanagi, A, Kraemer, M, Krishan, K, Krohn, K, Kromhout, H, Kuate Defo, B, Kumar, G, Kumar, V, Kurmi, O, Kusuma, D, La Vecchia, C, Lal, D, Lalloo, R, Lallukka, T, Lami, F, Landires, I, Lang, J, Langan, S, Larsson, A, Lasrado, S, Lauriola, P, Lazarus, J, Lee, P, Lee, S, Legrand, K, Leigh, J, Leonardi, M, Lescinsky, H, Leung, J, Levi, M, Li, S, Lim, L, Linn, S, Liu, S, Liu, Y, Lo, J, Lopez, A, Lopez, J, Lopukhov, P, Lorkowski, S, Lotufo, P, Lu, A, Lugo, A, Maddison, E, Mahasha, P, Mahdavi, M, Mahmoudi, M, Majeed, A, Maleki, A, Maleki, S, Malekzadeh, R, Malta, D, Mamun, A, Manda, A, Manguerra, H, Mansour-Ghanaei, F, Mansouri, B, Mansournia, M, Mantilla Herrera, A, Maravilla, J, Marks, A, Martin, R, Martini, S, Martins-Melo, F, Masaka, A, Masoumi, S, Mathur, M, Matsushita, K, Maulik, P, Mcalinden, C, Mcgrath, J, Mckee, M, Mehndiratta, M, Mehri, F, Mehta, K, Memish, Z, Mendoza, W, Menezes, R, Mengesha, E, Mereke, A, Mereta, S, Meretoja, A, Meretoja, T, Mestrovic, T, Miazgowski, B, Miazgowski, T, Michalek, I, Miller, T, Mills, E, Mini, G, Miri, M, Mirica, A, Mirrakhimov, E, Mirzaei, H, Mirzaei, M, Mirzaei, R, Mirzaei-Alavijeh, M, Misganaw, A, Mithra, P, Moazen, B, Mohammad, D, Mohammad, Y, Mohammad Gholi Mezerji, N, Mohammadian-Hafshejani, A, Mohammadifard, N, Mohammadpourhodki, R, Mohammed, A, Mohammed, H, Mohammed, J, Mohammed, S, Mokdad, A, Molokhia, M, Monasta, L, Mooney, M, Moradi, G, Moradi, M, Moradi-Lakeh, M, Moradzadeh, R, Moraga, P, Morawska, L, Morgado-Da-Costa, J, Morrison, S, Mosapour, A, Mosser, J, Mouodi, S, Mousavi, S, Khaneghah, A, Mueller, U, Mukhopadhyay, S, Mullany, E, Musa, K, Muthupandian, S, Nabhan, A, Naderi, M, Nagarajan, A, Nagel, G, Naghavi, M, Naghshtabrizi, B, Naimzada, M, Najafi, F, Nangia, V, Nansseu, J, Naserbakht, M, Nayak, V, Negoi, I, Ngunjiri, J, Nguyen, C, Nguyen, H, Nguyen, M, Nigatu, Y, Nikbakhsh, R, Nixon, M, Nnaji, C, Nomura, S, Norrving, B, Noubiap, J, Nowak, C, Nunez-Samudio, V, Oancea, B, Odell, C, Ogbo, F, Oh, I, Okunga, E, Oladnabi, M, Olagunju, A, Olusanya, B, Olusanya, J, Omer, M, Ong, K, Onwujekwe, O, Orpana, H, Ortiz, A, Osarenotor, O, Osei, F, Ostroff, S, Otoiu, A, Otstavnov, N, Otstavnov, S, Overland, S, Owolabi, M, Mahesh, P, Padubidri, J, Palladino, R, Panda-Jonas, S, Pandey, A, Parry, C, Pasovic, M, Pasupula, D, Patel, S, Pathak, M, Patten, S, Patton, G, Toroudi, H, Peden, A, Pennini, A, Pepito, V, Peprah, E, Pereira, D, Pesudovs, K, Pham, H, Phillips, M, Piccinelli, C, Pilz, T, Piradov, M, Pirsaheb, M, Plass, D, Polinder, S, Polkinghorne, K, Pond, C, Postma, M, Pourjafar, H, Pourmalek, F, Poznanska, A, Prada, S, Prakash, V, Pribadi, D, Pupillo, E, Syed, Z, Rabiee, M, Rabiee, N, Radfar, A, Rafiee, A, Raggi, A, Rahman, M, Rajabpour-Sanati, A, Rajati, F, Rakovac, I, Ram, P, Ramezanzadeh, K, Ranabhat, C, Rao, P, Rao, S, Rashedi, V, Rathi, P, Rawaf, D, Rawaf, S, Rawal, L, Rawassizadeh, R, Rawat, R, Razo, C, Redford, S, Reiner, R, Reitsma, M, Remuzzi, G, Renjith, V, Renzaho, A, Resnikoff, S, Rezaei, N, Rezapour, A, Rhinehart, P, Riahi, S, Ribeiro, D, Rickard, J, Rivera, J, Roberts, N, Rodriguez-Ramirez, S, Roever, L, Ronfani, L, Room, R, Roshandel, G, Roth, G, Rothenbacher, D, Rubagotti, E, Rwegerera, G, Sabour, S, Sachdev, P, Saddik, B, Sadeghi, E, Sadeghi, M, Saeedi, R, Saeedi Moghaddam, S, Safari, Y, Safi, S, Safiri, S, Sagar, R, Sahebkar, A, Sajadi, S, Salam, N, Salamati, P, Salem, H, Salem, M, Salimzadeh, H, Salman, O, Salomon, J, Samad, Z, Samadi Kafil, H, Sambala, E, Samy, A, Sanabria, J, Sanchez-Pimienta, T, Santomauro, D, Santos, I, Santos, J, Santric-Milicevic, M, Saraswathy, S, Sarmiento-Suarez, R, Sarrafzadegan, N, Sarveazad, A, Sathian, B, Sathish, T, Sattin, D, Saxena, S, Schaeffer, L, Schiavolin, S, Schlaich, M, Schmidt, M, Schutte, A, Schwebel, D, Schwendicke, F, Senbeta, A, Senthilkumaran, S, Sepanlou, S, Serdar, B, Serre, M, Shadid, J, Shafaat, O, Shahabi, S, Shaheen, A, Shaikh, M, Shalash, A, Shams-Beyranvand, M, Shamsizadeh, M, Sharafi, K, Sheikh, A, Sheikhtaheri, A, Shibuya, K, Shield, K, Shigematsu, M, Shin, J, Shin, M, Shiri, R, Shirkoohi, R, Shuval, K, Siabani, S, Sierpinski, R, Sigfusdottir, I, Sigurvinsdottir, R, Silva, J, Simpson, K, Singh, J, Singh, P, Skiadaresi, E, Skou, S, Skryabin, V, Smith, E, Soheili, A, Soltani, S, Soofi, M, Sorensen, R, Soriano, J, Sorrie, M, Soshnikov, S, Soyiri, I, Spencer, C, Spotin, A, Sreeramareddy, C, Srinivasan, V, Stanaway, J, Stein, C, Stein, D, Steiner, C, Stockfelt, L, Stokes, M, Straif, K, Stubbs, J, Sufiyan, M, Suleria, H, Suliankatchi Abdulkader, R, Sulo, G, Sultan, I, Tabares-Seisdedos, R, Tabb, K, Tabuchi, T, Taherkhani, A, Tajdini, M, Takahashi, K, Takala, J, Tamiru, A, Taveira, N, Tehrani-Banihashemi, A, Temsah, M, Tesema, G, Tessema, Z, Thurston, G, Titova, M, Tohidinik, H, Tonelli, M, Topor-Madry, R, Topouzis, F, Torre, A, Touvier, M, Tovani-Palone, M, Tran, B, Travillian, R, Tsatsakis, A, Tudor Car, L, Tyrovolas, S, Uddin, R, Umeokonkwo, C, Unnikrishnan, B, Upadhyay, E, Vacante, M, Valdez, P, van Donkelaar, A, Vasankari, T, Vasseghian, Y, Veisani, Y, Venketasubramanian, N, Violante, F, Vlassov, V, Vollset, S, Vos, T, Vukovic, R, Waheed, Y, Wallin, M, Wang, Y, Watson, A, Wei, J, Wei, M, Weintraub, R, Weiss, J, Werdecker, A, West, J, Westerman, R, Whisnant, J, Whiteford, H, Wiens, K, Wolfe, C, Wozniak, S, Wu, A, Wu, J, Wulf Hanson, S, Xu, G, Xu, R, Yadgir, S, Yahyazadeh Jabbari, S, Yamagishi, K, Yaminfirooz, M, Yano, Y, Yaya, S, Yazdi-Feyzabadi, V, Yeheyis, T, Yilgwan, C, Yilma, M, Yip, P, Yonemoto, N, Younis, M, Younker, T, Yousefi, B, Yousefi, Z, Yousefinezhadi, T, Yousuf, A, Yu, C, Yusefzadeh, H, Moghadam, T, Zamani, M, Zamanian, M, Zandian, H, Zastrozhin, M, Zhang, Y, Zhang, Z, Zhao, J, Zhao, X, Zhao, Y, Zheng, P, Zhou, M, Ziapour, A, Zimsen, S, Lim, S, Murray, C, GBD 2019 Risk Factors Collaborator, Violante FS, Biosciences, Department of Public Health, Clinicum, Department of Neurosciences, HUS Comprehensive Cancer Center, Environmental Sciences, Sub Foundations&PhilosophyofNaturSc begr, IRAS OH Epidemiology Chemical Agents, dIRAS RA-2, Public Health, Bin Sayeed, M. S. B., Caetano Dos Santos, F. L., Camera, L. A., Elyazar, I. R. F., Ayalew Gebreslassie, A. A. A., Ginindza, T. G., Matin, B. K., Morgado-Da-Costa, J., Khaneghah, A. M., Mahesh, P. A., Toroudi, H. P., Syed, Z. Q., Salem, M. R., Skou, S. T., Tovani-Palone, M. R., Tudor Car, L. T., and Moghadam, T. Z.

Subjects
Male, Nutritional Sciences, Specific risk, Contaminación del Aire Interior, 030204 cardiovascular system & hematology, Socioeconomic Factor, systematic analysis, Global Health, Body Mass Index, Global Burden of Disease, Health Risk Behavior, Health Risk Behaviors, Disease studies, 0302 clinical medicine, Risk Factors, METABOLIC RISKS, 030212 general & internal medicine, 11 Medical and Health Sciences, Factores de Riesgo, 2. Zero hunger, education.field_of_study, Public health, Injuries, Public Health, Global Health, Social Medicine and Epidemiology, General Medicine, GBD, risck factors, attributable burden of disease, 3142 Public health care science, environmental and occupational health, 3. Good health, Relative risk, Environmental health, Health, Hypertension, Global Burden of Diseases, Injuries, Risk Factors, A990 Medicine and Dentistry not elsewhere classified, Female, Leading risk factors, Global Health Metrics, Cohort study, Human, medicine.medical_specialty, Substance-Related Disorders, Population, UNITED-STATES, Risk Assessment, DIET, ITC-HYBRID, 03 medical and health sciences, Life Expectancy, MORTALITY, DISABILITY, POLLUTION, CLUSTERS, SDG 3 - Good Health and Well-being, General & Internal Medicine, medicine, Humans, Global Burden of Disease Study, Risk factor, education, Global burden, business.industry, Risk Factor, Malnutrition, Klinisk medicin, Global Burden of Diseases, Environmental Exposure, medicine.disease, Enfermedades, purl.org/pe-repo/ocde/ford#3.02.00 [https], Folkhälsovetenskap, global hälsa, socialmedicin och epidemiologi, Years of potential life lost, Socioeconomic Factors, Risk factors, Disease study, Hyperglycemia, ITC-ISI-JOURNAL-ARTICLE, NA, Clinical Medicine, business, RA
Abstract

Background Rigorous analysis of levels and trends in exposure to leading risk factors and quantification of their effect on human health are important to identify where public health is making progress and in which cases current efforts are inadequate. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 provides a standardised and comprehensive assessment of the magnitude of risk factor exposure, relative risk, and attributable burden of disease. Methods GBD 2019 estimated attributable mortality, years of life lost (YLLs), years of life lived with disability (YLDs), and disability-adjusted life-years (DALYs) for 87 risk factors and combinations of risk factors, at the global level, regionally, and for 204 countries and territories. GBD uses a hierarchical list of risk factors so that specific risk factors (eg, sodium intake), and related aggregates (eg, diet quality), are both evaluated. This method has six analytical steps. (1) We included 560 risk-outcome pairs that met criteria for convincing or probable evidence on the basis of research studies. 12 risk-outcome pairs included in GBD 2017 no longer met inclusion criteria and 47 risk-outcome pairs for risks already included in GBD 2017 were added based on new evidence. (2) Relative risks were estimated as a function of exposure based on published systematic reviews, 81 systematic reviews done for GBD 2019, and meta-regression. (3) Levels of exposure in each age-sex-location-year included in the study were estimated based on all available data sources using spatiotemporal Gaussian process regression, DisMod-MR 2.1, a Bayesian meta-regression method, or alternative methods. (4) We determined, from published trials or cohort studies, the level of exposure associated with minimum risk, called the theoretical minimum risk exposure level. (5) Attributable deaths, YLLs, YLDs, and DALYs were computed by multiplying population attributable fractions (PAFs) by the relevant outcome quantity for each age-sex-location-year. (6) PAFs and attributable burden for combinations of risk factors were estimated taking into account mediation of different risk factors through other risk factors. Across all six analytical steps, 30 652 distinct data sources were used in the analysis. Uncertainty in each step of the analysis was propagated into the final estimates of attributable burden. Exposure levels for dichotomous, polytomous, and continuous risk factors were summarised with use of the summary exposure value to facilitate comparisons over time, across location, and across risks. Because the entire time series from 1990 to 2019 has been re-estimated with use of consistent data and methods, these results supersede previously published GBD estimates of attributable burden. Findings The largest declines in risk exposure from 2010 to 2019 were among a set of risks that are strongly linked to social and economic development, including household air pollution; unsafe water, sanitation, and handwashing; and child growth failure. Global declines also occurred for tobacco smoking and lead exposure. The largest increases in risk exposure were for ambient particulate matter pollution, drug use, high fasting plasma glucose, and high body-mass index. In 2019, the leading Level 2 risk factor globally for attributable deaths was high systolic blood pressure, which accounted for 10.8 million (95% uncertainty interval [UI] 9.51-12.1) deaths (19.2% [16.9-21.3] of all deaths in 2019), followed by tobacco (smoked, second-hand, and chewing), which accounted for 8.71 million (8.12-9.31) deaths (15.4% [14.6-16.2] of all deaths in 2019). The leading Level 2 risk factor for attributable DALYs globally in 2019 was child and maternal malnutrition, which largely affects health in the youngest age groups and accounted for 295 million (253-350) DALYs (11.6% [10.3-13.1] of all global DALYs that year). The risk factor burden varied considerably in 2019 between age groups and locations. Among children aged 0-9 years, the three leading detailed risk factors for attributable DALYs were all related to malnutrition. Iron deficiency was the leading risk factor for those aged 10-24 years, alcohol use for those aged 25-49 years, and high systolic blood pressure for those aged 50-74 years and 75 years and older. Interpretation Overall, the record for reducing exposure to harmful risks over the past three decades is poor. Success with reducing smoking and lead exposure through regulatory policy might point the way for a stronger role for public policy on other risks in addition to continued efforts to provide information on risk factor harm to the general public. Copyright (C) 2020 The Author(s). Published by Elsevier Ltd.

Published
2020

36. Aircraft observations since the 1990s reveal increases of tropospheric ozone at multiple locations across the Northern Hemisphere

Author

Valérie Thouret, Sarah A. Strode, Owen R. Cooper, Kai-Lan Chang, Luke D. Oman, Ilann Bourgeois, Jerry Ziemke, Philippe Nédélec, Pasquale Sellitto, Romain Blot, Claire Granier, Audrey Gaudel, Cooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado [Boulder]-National Oceanic and Atmospheric Administration (NOAA), Laboratoire d'Ecologie Alpine (LECA ), Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), NASA Goddard Space Flight Center (GSFC), Universities Space Research Association (USRA), Laboratoire Interuniversitaire des Systèmes Atmosphériques (LISA (UMR_7583)), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris Diderot - Paris 7 (UPD7)-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)-Centre National de la Recherche Scientifique (CNRS), Institut Pierre-Simon-Laplace (IPSL (FR_636)), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Laboratoire d'aérologie (LAERO), Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Observatoire Midi-Pyrénées (OMP), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées, Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Observatoire Midi-Pyrénées (OMP), and Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)

Subjects
Climatology, Multidisciplinary, Ozone, 010504 meteorology & atmospheric sciences, Northern Hemisphere, SciAdv r-articles, 010501 environmental sciences, Radiative forcing, Atmospheric sciences, 01 natural sciences, Troposphere, chemistry.chemical_compound, chemistry, 13. Climate action, [SDU]Sciences of the Universe [physics], Atmospheric chemistry, Greenhouse gas, Middle latitudes, Environmental science, Tropospheric ozone, Research Articles, Research Article, 0105 earth and related environmental sciences
Abstract

Recent aircraft observations show that tropospheric ozone increased above many Northern Hemisphere regions since the mid-1990s., Tropospheric ozone is an important greenhouse gas, is detrimental to human health and crop and ecosystem productivity, and controls the oxidizing capacity of the troposphere. Because of its high spatial and temporal variability and limited observations, quantifying net tropospheric ozone changes across the Northern Hemisphere on time scales of two decades had not been possible. Here, we show, using newly available observations from an extensive commercial aircraft monitoring network, that tropospheric ozone has increased above 11 regions of the Northern Hemisphere since the mid-1990s, consistent with the OMI/MLS satellite product. The net result of shifting anthropogenic ozone precursor emissions has led to an increase of ozone and its radiative forcing above all 11 study regions of the Northern Hemisphere, despite NOx emission reductions at midlatitudes.

Published
2020

38. Statistical regularization for trend detection: An integrated approach for detecting long-term trends from sparse tropospheric ozone profiles

Author

Kai-Lan Chang, Owen R Cooper, Audrey Gaudel, Irina Petropavlovskikh, and Valerie Thouret

Abstract

Detecting a tropospheric ozone trend from sparsely sampled ozonesonde profiles (typically once per week) is challenging due to the noise in the time series resulting from ozone's high temporal variability. To enhance trend detection we have developed a sophisticated statistical approach that utilizes a geoadditive model to assess ozone variability across a time series of vertical profiles. Treating the profile time series as a set of individual time series on discrete pressure surfaces, a class of smoothing spline ANOVA (analysis of variance) models is used for the purpose of jointly modeling multiple correlated time series (on separate pressure surfaces) by their associated seasonal and interannual variabilities. This integrated fit method filters out the unstructured noise through a statistical regularization (i.e. a roughness penalty), by taking advantage of the additional correlated data points available on the pressure surfaces above and below the surface of interest. We have applied this technique to the trend analysis of the vertically correlated time series of tropospheric ozone observations from 1) IAGOS (In-service Aircraft for a Global Observing System) commercial aircraft profiles above Europe and China, and 2) NOAA GMD's (Global Monitoring Division) ozonesonde records at Hilo, Hawaii and Trinidad Head, California. We illustrate the ability of this technique to detect a consistent trend estimate, and its effectiveness for reducing the associated uncertainty in the noisy profile data due to low sampling frequency. We also conducted a sensitivity analysis of frequent IAGOS profiles above Europe (approximately 120 profiles per month) to determine how many profiles in a month are required for reliable long-term trend detection. When ignoring the vertical correlation we found that a typical sampling strategy of 4 profiles-per-month results in 7 % of sampled trends falling outside the 2-sigma uncertainty interval derived from the full data set, with associated 10 % of mean absolute percentage error. We determined that an optimal sampling frequency is 14 profiles per month when using the integrated fit method for calculating trends; when the integrated fit method is not applied, the sampling frequency had to be increased to 18 profiles per month to achieve the same result. While our method improves trend detection from sparse data sets, the key to substantially reducing the uncertainty is to increase the sampling frequency.

Published
2020

40. Multi-decadal surface ozone trends at globally distributed remote locations

Author

Xiaobin Xu, Karin Sjöberg, Xiao Lu, Emilio Cuevas, David W. Tarasick, Valérie Thouret, Martin G. Schultz, Marina Fröhlich, Irina Petropavlovskikh, Ian E. Galbally, Owen R. Cooper, Audrey Gaudel, Jason O'Brien, Samuel J. Oltmans, Audra McClure-Begley, Kai-Lan Chang, Sabine Schroeder, Gerardo Carbajal Benítez, Irina Senik, Dagmar Kubistin, Martin Steinbacher, Gerard Spain, Ludwig Ries, Suzie B. Molloy, Wolfgang Spangl, Philippe Nédélec, and Sverre Solberg

Subjects
Atmospheric Science, Environmental Engineering, Ozone, Tropospheric Ozone, Trends, Global change, Trace gas, 010504 meteorology & atmospheric sciences, Climate change, 010501 environmental sciences, Oceanography, Atmospheric sciences, 01 natural sciences, Latitude, chemistry.chemical_compound, ddc:550, Tropospheric ozone, Southern Hemisphere, lcsh:Environmental sciences, 0105 earth and related environmental sciences, lcsh:GE1-350, Ecology, Northern Hemisphere, Geology, Geotechnical Engineering and Engineering Geology, chemistry, Environmental science
Abstract

Extracting globally representative trend information from lower tropospheric ozone observations is extremely difficult due to the highly variable distribution and interannual variability of ozone, and the ongoing shift of ozone precursor emissions from high latitudes to low latitudes. Here we report surface ozone trends at 27 globally distributed remote locations (20 in the Northern Hemisphere, 7 in the Southern Hemisphere), focusing on continuous time series that extend from the present back to at least 1995. While these sites are only representative of less than 25% of the global surface area, this analysis provides a range of regional long-term ozone trends for the evaluation of global chemistry-climate models. Trends are based on monthly mean ozone anomalies, and all sites have at least 20 years of data, which improves the likelihood that a robust trend value is due to changes in ozone precursor emissions and/or forced climate change rather than naturally occurring climate variability. Since 1995, the Northern Hemisphere sites are nearly evenly split between positive and negative ozone trends, while 5 of 7 Southern Hemisphere sites have positive trends. Positive trends are in the range of 0.5–2 ppbv decade–1, with ozone increasing at Mauna Loa by roughly 50% since the late 1950s. Two high elevation Alpine sites, discussed by previous assessments, exhibit decreasing ozone trends in contrast to the positive trend observed by IAGOS commercial aircraft in the European lower free-troposphere. The Alpine sites frequently sample polluted European boundary layer air, especially in summer, and can only be representative of lower free tropospheric ozone if the data are carefully filtered to avoid boundary layer air. The highly variable ozone trends at these 27 surface sites are not necessarily indicative of free tropospheric trends, which have been overwhelmingly positive since the mid-1990s, as shown by recent studies of ozonesonde and aircraft observations. The IAGOS program acknowledges the European Commission for its support of the MOZAIC project (1994–2003) the preparatory phase of IAGOS (2005–2013) and IGAS (2013–2016).

Published
2020

41. A Statistical Model for Automated Quality Assessment of the TOAR-II

Author

Martin G. Schultz, Sabine Schröder, Najmeh Kaffashzadeh, and Kai-Lan Chang

Subjects
Quality assessment, Computer science, Statistical model, Data mining, computer.software_genre, computer
Abstract

The Tropospheric Ozone Assessment Report, phase 2, (TOAR-II) database is a collection of global ground-level ozone in-situ measurements from various locations. It also holds data of selected ozone precursors and meteorological variables. TOAR-II assembles air quality data from many different sources and thus requires a common data quality assessment (QA) to ensure the data meet the quality required for globally consistent analyses. The large volume of this database (more than 100,000 data series) enforces the use of automated, data-driven QA procedures.Accordingly, we have developed a statistical model for automated QA. This model consists of several statistical tests that are classified into several sub-groups. In this model, a QA-score (an indicator ranging from 0 to 1) was assigned to each individual data point to estimates the value‘s plausibility. The foundation of this concept is statistical hypothesis testing and the probability theory. This model was implemented in a Python package and is called AutoQA4Env.One application of AutoQA4Env is the data ingestion workflow of TOAR-II. The tool generates a data quality report which is then sent back to the data provider for inspection. Since AutoQA4Env is easily configurable, it allows the users to set quality thresholds and thus filter data according to their use case. While we primarily develop AutoQA4Env for air quality data, the same concept and model might be applicable to other databases and the software framework is flexible enough to allow for other use cases.

Published
2020
Full Text
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42. Spatial Coverage of Monitoring Networks: A Climate Observing System Simulation Experiment

Author

Alessandro Fasso, Greg Bodeker, Soner Yorgun, Birgit Hassler, Jeffrey K. Lazo, Kai-Lan Chang, Christopher T. M. Clack, Jason M. English, and Dale F. Hurst

Subjects
Optimization, Atmospheric Science, 010504 meteorology & atmospheric sciences, Meteorology, Global climate, 0208 environmental biotechnology, 02 engineering and technology, 01 natural sciences, Representativeness heuristic, Statistical techniques, Sampling, 0105 earth and related environmental sciences, Remote sensing, Pixel, Trend detection, Sampling (statistics), In situ atmospheric observations, Experimental design, Planning, 020801 environmental engineering, Depth sounding, 13. Climate action, Environmental science, Satellite, Settore SECS-S/02 - Statistica per La Ricerca Sperimentale e Tecnologica
Abstract

Observing systems consisting of a finite number of in situ monitoring stations can provide high-quality measurements with the ability to quality assure both the instruments and the data but offer limited information over larger geographic areas. This paper quantifies the spatial coverage represented by a finite set of monitoring stations by using global data—data that are possibly of lower resolution and quality. For illustration purposes, merged satellite temperature data from Microwave Sounding Units are used to estimate the representativeness of the Global Climate Observing System Reference Upper-Air Network (GRUAN). While many metrics exist for evaluating the representativeness of a site, the ability to have highly accurate monthly averaged data is essential for both trend detection and climatology evaluation. The calculated correlations of the monthly averaged upper-troposphere satellite-derived temperatures over the GRUAN stations with all other pixels around the globe show that the current 9 certified GRUAN stations have moderate correlations (r ≥ 0.7) for approximately 10% of the earth, but an expanded network incorporating another 15 stations would result in moderate correlations for just over 60% of the earth. This analysis indicates that the value of additional stations can be quantified by using historical, satellite, or model data and can be used to reveal critical gaps in current monitoring capabilities. Evaluating the value of potential additional stations and prioritizing their initiation can optimize networks. The expansion of networks can be evaluated in a manner that allows for optimal benefit on the basis of optimization theory and economic analyses.

Published
2017

43. A pause in Southern Hemisphere circulation trends due to the Montreal Protocol

Author

John C. Fyfe, Lorenzo M. Polvani, Darryn W. Waugh, Kai-Lan Chang, and Antara Banerjee

Subjects
Salinity, 010504 meteorology & atmospheric sciences, Atmospheric circulation, International Cooperation, Oceans and Seas, Rain, Antarctic Regions, Wind, 010502 geochemistry & geophysics, 01 natural sciences, Troposphere, Ozone, Ozone layer, Montreal Protocol, Water Movements, Human Activities, Stratosphere, Southern Hemisphere, 0105 earth and related environmental sciences, Multidisciplinary, Atmosphere, Ocean current, Ozone depletion, Environmental Policy, Climatology, Environmental science
Abstract

Observations show robust near-surface trends in Southern Hemisphere tropospheric circulation towards the end of the twentieth century, including a poleward shift in the mid-latitude jet1,2, a positive trend in the Southern Annular Mode1,3–6 and an expansion of the Hadley cell7,8. It has been established that these trends were driven by ozone depletion in the Antarctic stratosphere due to emissions of ozone-depleting substances9–11. Here we show that these widely reported circulation trends paused, or slightly reversed, around the year 2000. Using a pattern-based detection and attribution analysis of atmospheric zonal wind, we show that the pause in circulation trends is forced by human activities, and has not occurred owing only to internal or natural variability of the climate system. Furthermore, we demonstrate that stratospheric ozone recovery, resulting from the Montreal Protocol, is the key driver of the pause. Because pre-2000 circulation trends have affected precipitation12–14, and potentially ocean circulation and salinity15–17, we anticipate that a pause in these trends will have wider impacts on the Earth system. Signatures of the effects of the Montreal Protocol and the associated stratospheric ozone recovery might therefore manifest, or have already manifested, in other aspects of the Earth system. The recovery of stratospheric ozone in the Southern Hemisphere in the wake of the Montreal Protocol is driving a pause in atmospheric circulation trends that warrants closer scrutiny across the Earth system.

Published
2019

45. Tropospheric Ozone Assessment Report: Tropospheric ozone from 1877 to 2016, observed levels, trends and uncertainties

Author

Jessica L. Neu, Kai-Lan Chang, Samuel J. Oltmans, Thomas Trickl, David W. Tarasick, Corinne Vigouroux, Juan Cuesta, James W. Hannigan, Mohammed K. Osman, Meiyun Lin, Helen M. Worden, Timothy J. Wallington, Jane Liu, Ian E. Galbally, Anne M. Thompson, Martin Steinbacher, Prodromos Zanis, Owen R. Cooper, Martin G. Schultz, Birgit Hassler, Maria J. Granados-Munoz, Gaëlle Dufour, Audrey Gaudel, Gilles Foret, Gérard Ancellet, Xiong Liu, Valérie Thouret, Thierry Leblanc, Irina Petropavlovskikh, Omaira García, Jerry Ziemke, Johannes Staehelin, Environment and Climate Change Canada, CSIRO Climate Science Centre, Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), Centre for Atmospheric Chemistry [Wollongong] (CAC), University of Wollongong [Australia], Cooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado [Boulder]-National Oceanic and Atmospheric Administration (NOAA), NOAA Earth System Research Laboratory (ESRL), National Oceanic and Atmospheric Administration (NOAA), Jülich Supercomputing Centre (JSC), Forschungszentrum Jülich GmbH | Centre de recherche de Juliers, Helmholtz-Gemeinschaft = Helmholtz Association-Helmholtz-Gemeinschaft = Helmholtz Association, TROPO - LATMOS, Laboratoire Atmosphères, Milieux, Observations Spatiales (LATMOS), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Jet Propulsion Laboratory (JPL), NASA-California Institute of Technology (CALTECH), Ford Motor Company, NASA Goddard Space Flight Center (GSFC), Harvard-Smithsonian Center for Astrophysics (CfA), Harvard University-Smithsonian Institution, Swiss Federal Laboratories for Materials Science and Technology [Dübendorf] (EMPA), Department of Environmental Systems Science [ETH Zürich] (D-USYS), Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] (ETH Zürich), Belgian Institute for Space Aeronomy / Institut d'Aéronomie Spatiale de Belgique (BIRA-IASB), National Center for Atmospheric Research [Boulder] (NCAR), Izaña Atmospheric Research Center (IARC), Agencia Estatal de Meteorología (AEMet), Laboratoire Interuniversitaire des Systèmes Atmosphériques (LISA (UMR_7583)), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris Diderot - Paris 7 (UPD7)-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)-Centre National de la Recherche Scientifique (CNRS), Department of Meteorology and Climatology [Thessaloniki], Aristotle University of Thessaloniki, Cooperative Institute for Mesoscale Meteorological Studies (CIMMS), National Severe Storms Laboratory (NSSL), Enable Midstream Partners, Department of Geography and Planning [University of Toronto], University of Toronto, School of Atmospheric Sciences [Nanjing], Nanjing University (NJU), NOAA Geophysical Fluid Dynamics Laboratory (GFDL), Atmospheric and Oceanic Sciences Program [Princeton] (AOS Program), National Oceanic and Atmospheric Administration (NOAA)-National Oceanic and Atmospheric Administration (NOAA)-Princeton University, Remote Sensing Laboratory [Barcelona] (RSLab), Universitat Politècnica de Catalunya [Barcelona] (UPC), ESRL Global Monitoring Laboratory [Boulder] (GML), National Oceanic and Atmospheric Administration (NOAA)-National Oceanic and Atmospheric Administration (NOAA), Laboratoire d'aérologie (LAERO), Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Observatoire Midi-Pyrénées (OMP), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS), DLR Institut für Physik der Atmosphäre (IPA), Deutsches Zentrum für Luft- und Raumfahrt [Oberpfaffenhofen-Wessling] (DLR), Institut für Meteorologie und Klimaforschung - Atmosphärische Umweltforschung (IMK-IFU), Karlsruher Institut für Technologie (KIT), Sorbonne Université (SU)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS), Smithsonian Institution-Harvard University [Cambridge], Centre National de la Recherche Scientifique (CNRS)-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées, Center for Atmospheric Chemistry [Wollongong] (CAC), University of Wollongong, Forschungszentrum Jülich GmbH, Harvard University [Cambridge]-Smithsonian Institution, Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology in Zürich [Zürich] (ETH Zürich), Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), ESRL Global Monitoring Division [Boulder] (GMD), Laboratoire d'aérologie (LA), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Observatoire Midi-Pyrénées (OMP), and Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)

Subjects
Atmospheric Science, Environmental Engineering, Ozone, 010504 meteorology & atmospheric sciences, Climate, 010501 environmental sciences, Oceanography, Atmospheric sciences, 01 natural sciences, Standard deviation, science development, Atmosphere, Troposphere, chemistry.chemical_compound, ddc:550, Tropospheric ozone, Erdsystemmodell -Evaluation und -Analyse, lcsh:Environmental sciences, 0105 earth and related environmental sciences, Measurements, Trends, Historical, lcsh:GE1-350, [PHYS.PHYS.PHYS-AO-PH]Physics [physics]/Physics [physics]/Atmospheric and Oceanic Physics [physics.ao-ph], [SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere, Institut für Physik der Atmosphäre, Ecology, Northern Hemisphere, Geology, Geotechnical Engineering and Engineering Geology, Earth sciences, Lidar, chemistry, 13. Climate action, Environmental science, Satellite
Abstract

From the earliest observations of ozone in the lower atmosphere in the 19th century, both measurement methods and the portion of the globe observed have evolved and changed. These methods have different uncertainties and biases, and the data records differ with respect to coverage (space and time), information content, and representativeness. In this study, various ozone measurement methods and ozone datasets are reviewed and selected for inclusion in the historical record of background ozone levels, based on relationship of the measurement technique to the modern UV absorption standard, absence of interfering pollutants, representativeness of the well-mixed boundary layer and expert judgement of their credibility. There are significant uncertainties with the 19th and early 20th-century measurements related to interference of other gases. Spectroscopic methods applied before 1960 have likely underestimated ozone by as much as 11% at the surface and by about 24% in the free troposphere, due to the use of differing ozone absorption coefficients. There is no unambiguous evidence in the measurement record back to 1896 that typical mid-latitude background surface ozone values were below about 20 nmol mol–1, but there is robust evidence for increases in the temperate and polar regions of the northern hemisphere of 30–70%, with large uncertainty, between the period of historic observations, 1896–1975, and the modern period (1990–2014). Independent historical observations from balloons and aircraft indicate similar changes in the free troposphere. Changes in the southern hemisphere are much less. Regional representativeness of the available observations remains a potential source of large errors, which are difficult to quantify. The great majority of validation and intercomparison studies of free tropospheric ozone measurement methods use ECC ozonesondes as reference. Compared to UV-absorption measurements they show a modest (~1–5% ±5%) high bias in the troposphere, but no evidence of a change with time. Umkehr, lidar, and FTIR methods all show modest low biases relative to ECCs, and so, using ECC sondes as a transfer standard, all appear to agree to within one standard deviation with the modern UV-absorption standard. Other sonde types show an increase of 5–20% in sensitivity to tropospheric ozone from 1970–1995. Biases and standard deviations of satellite retrieval comparisons are often 2–3 times larger than those of other free tropospheric measurements. The lack of information on temporal changes of bias for satellite measurements of tropospheric ozone is an area of concern for long-term trend studies.

Published
2019

46. A new method (M3Fusion-v1) for combining observations and multiple model output for an improved estimate of the global surface ozone distribution

Author

Kai-Lan Chang, Owen R. Cooper, J. Jason West, Marc L. Serre, Martin G. Schultz, Meiyun Lin, Virginie Marécal, Béatrice Josse, Makoto Deushi, Kengo Sudo, Junhua Liu, and Christoph A. Keller

Subjects
13. Climate action
Abstract

We have developed a new statistical approach (M3Fusion) for combining surface ozone observations from thousands of monitoring sites around the world with the output from multiple atmospheric chemistry models to produce a global surface ozone distribution with greater accuracy than can be provided by any individual model. The ozone observations from 4766 monitoring sites were provided by the Tropospheric Ozone Assessment Report (TOAR) surface ozone database which contains the world's largest collection of surface ozone metrics. Output from six models was provided by the participants of the Chemistry-Climate Model Initiative (CCMI) and NASA's Global Modeling and Assimilation Office (GMAO). We analyze the 6-month maximum of the maximum daily 8-hour average ozone value (DMA8) for relevance to ozone health impacts. We interpolate the irregularly-spaced observations onto a fine resolution grid by using integrated nested Laplace approximations, and compare the ozone field to each model in each world region. This method allows us to produce a global surface ozone field based on TOAR observations, which we then use to select the combination of global models with the greatest skill in each of 8 world regions; models with greater skill in a particular region are given higher weight. This blended model product is bias-corrected within two degrees of observation locations to produce the final fused surface ozone product. We show that our fused product has an improved mean squared error compared to the simple multi-model ensemble mean.

Published
2018

48. Spatial mapping of ground-based observations of total ozone

Author

Vitali Fioletov, Serge Guillas, and Kai-Lan Chang

Subjects
Atmospheric Science, Meteorology, Calibration (statistics), Chemistry, lcsh:TA715-787, lcsh:Earthwork. Foundations, Covariance, Spatial distribution, Multivariate interpolation, lcsh:Environmental engineering, Stochastic partial differential equation, Robustness (computer science), Kriging, Satellite, lcsh:TA170-171
Abstract

Total column ozone variations estimated using ground-based stations provide important independent source of information in addition to satellite-based estimates. This estimation has been vigorously challenged by data inhomogeneity in time and by the irregularity of the spatial distribution of stations, as well as by interruptions in observation records. Furthermore, some stations have calibration issues and thus observations may drift. In this paper we compare the spatial interpolation of ozone levels using the novel stochastic partial differential equation (SPDE) approach with the covariance-based kriging. We show how these new spatial predictions are more accurate, less uncertain and more robust. We construct long-term zonal means to investigate the robustness against the absence of measurements at some stations as well as instruments drifts. We conclude that time series analyzes can benefit from the SPDE approach compared to the covariance-based kriging when stations are missing, but the positive impact of the technique is less pronounced in the case of drifts.

Published
2018

49. Regional trend analysis of surface ozone observations from monitoring networks in eastern North America, Europe and East Asia

Author

Tao Wang, Martin G. Schultz, Kai-Lan Chang, Irina Petropavlovskikh, and Owen R. Cooper

Subjects
Atmospheric Science, Environmental Engineering, Ozone, 010504 meteorology & atmospheric sciences, tropospheric ozone, regional variation, irregular monitoring network, space-time model, 010501 environmental sciences, Oceanography, 01 natural sciences, chemistry.chemical_compound, ddc:550, East Asia, Ecosystem, Tropospheric ozone, lcsh:Environmental sciences, 0105 earth and related environmental sciences, lcsh:GE1-350, Ecology, Geology, Geotechnical Engineering and Engineering Geology, Trend analysis, Geography, chemistry, Productivity (ecology), Regional variation, Climatology, Greenhouse gas
Abstract

Surface ozone is a greenhouse gas and pollutant detrimental to human health and crop and ecosystem productivity. The Tropospheric Ozone Assessment Report (TOAR) is designed to provide the research community with an up-to-date observation-based overview of tropospheric ozone’s global distribution and trends. The TOAR Surface Ozone Database contains ozone metrics at thousands of monitoring sites around the world, densely clustered across mid-latitude North America, western Europe and East Asia. Calculating regional ozone trends across these locations is challenging due to the uneven spacing of the monitoring sites across urban and rural areas. To meet this challenge we conducted a spatial and temporal trend analysis of several TOAR ozone metrics across these three regions for summertime (April–September) 2000–2014, using the generalized additive mixed model (GAMM). Our analysis indicates that East Asia has the greatest human and plant exposure to ozone pollution among investigating regions, with increasing ozone levels through 2014. The results also show that ozone mixing ratios continue to decline significantly over eastern North America and Europe, however, there is less evidence for decreases of daytime average ozone at urban sites. The present-day spatial coverage of ozone monitors in East Asia (South Korea and Japan) and eastern North America is adequate for estimating regional trends by simply taking the average of the individual trends at each site. However the European network is more sparsely populated across its northern and eastern regions and therefore a simple average of the individual trends at each site does not yield an accurate regional trend. This analysis demonstrates that the GAMM technique can be used to assess the regional representativeness of existing monitoring networks, indicating those networks for which a regional trend can be obtained by simply averaging the trends of all individual sites and those networks that require a more sophisticated statistical approach.

Published
2017

50. Multi-decadal surface ozone trends at globally distributed remote locations.

Author

Cooper, Owen R., Schultz, Martin G., Schroder, Sabine, Kai-Lan Chang, Gaudel, Audrey, Carbajal Benitez, Gerardo, Cuevas, Emilio, Fröhlich, Marina, Galbally, Ian E., Molloy, Suzie, Kubistin, Dagmar, Xiao Lun, McClure-Begley, Audra, Nédélec, Philippe, O'Brien, Jason, Oltmans, Samuel J., Petropavlovskikh, Irina, Ries, Ludwig, Senik, Irina, and Sjöberg, Karin

Published
2020
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