Vertical information content of nadir measurements of tropospheric NO2 from satellite

Poster presented at the EGU General Assembly 2014 in Vienna/Austria

Tropospheric nitrogen dioxide from satellite measurements: SCIAMACHY limb/nadir matching and multi-instrument trend analysis

Tropospheric NO2, a key air pollutant particularly in cities, has been measured from space since the mid-1990s by the GOME, SCIAMACHY, OMI, and GOME-2 instruments. These data provide a unique global long-term dataset of tropospheric pollution. The focus of this thesis is twofold: When these satellite measurements are to be used for assessing tropospheric emissions and pollution, it is necessary to separate the stratospheric from the tropospheric signal. This thesis develops a new unique technique for this separation by using the measurements performed by SCIAMACHY in limb geometry. The stratospheric NO2 measurements from SCIAMACHY are shown to be in very good agreement with NO2 fields modeled by the Oslo CTM2. However, both stratospheric datasets need to be adjusted to the level of the nadir measurements, because a time- and latitude-dependent bias to the measured nadir columns can be observed over clean regions. By combining measurements of total and stratospheric air masses taken by the same instrument, the uncertainties commonly introduced by unjustified assumptions and spatial averaging and smoothing can be significantly reduced, leading to the best dataset of tropospheric NO2 slant columns currently available. The tropospheric columns can then be used to assess the level of air pollution on a regional and even local scale. However, the observations of the four instruments differ in spatial resolution, local time of measurement, viewing geometry, and other details, and all these factors can severely impact the retrieved NO2 columns. Therefore, the analysis of temporal changes in troposheric NO2 abundances using these measurements is challenging and not straightforward. In the second part of this thesis, several methods to account for these instrumental differences are developed and applied to the analysis of trends in tropospheric NO2 columns over megacities. The first method is based on spatial averaging of the measured SCIAMACHY earthshine spectra and extraction of a spatial pattern of the resolution effect. Furthermore, two empirical corrections, which summarize all instrumental differences by including instrument-dependent offsets in a fitted trend function, are developed. These methods are applied to data from GOME and SCIAMACHY separately, to the combined time series, and to an extended dataset comprising also OMI and GOME-2 measurements. All approaches show consistent trends of tropospheric NO2 for a selection of areas on both regional and city scales, for the first time allowing consistent trend analysis of the full time series at high spatial resolution. Measured tropospheric NO2 columns have been strongly increasing over China, the Middle East, and India, with values over east-central China tripling from 1996 to 2011. All parts of the developed world, including Western Europe, the United States, and Japan, show significantly decreasing NO2 amounts in the same time period. On a megacity level, individual trends can be as large as 27.2 ± 3.9 % / yr and 20.7 ± 1.9 % / yr in Dhaka and Baghdad, respectively, while Los Angeles shows a very strong decrease of −6.00 ± 0.72 % / yr. Most megacities in China, India, and the Middle East show NO2 columns increasing by 5 to 10 % / yr, which leads to a doubling to tripling within the study period

Gas-phase chemistry in the online multiscale NMMB/BSC Chemical Transport Model: Description and evaluation at global scale

This paper presents a comprehensive description and benchmark evaluation of the tropospheric gas-phase chemistry component of the NMMB/BSC Chemical Transport Model (NMMB/BSC-CTM), an online chemical weather prediction system conceived for both the regional and the global scale. We provide an extensive evaluation of a global annual cycle simulation using a variety of background surface stations (EMEP, WDCGG and CASTNET), ozonesondes (WOUDC, CMD and SHADOZ), aircraft data (MOZAIC and several campaigns), and satellite observations (SCIAMACHY and MOPITT). We also include an extensive discussion of our results in comparison to other state-of-the-art models. The model shows a realistic oxidative capacity across the globe. The seasonal cycle for CO is fairly well represented at different locations (correlations around 0.3–0.7 in surface concentrations), although concentrations are underestimated in spring and winter in the Northern Hemisphere, and are overestimated throughout the year at 800 and 500 hPa in the Southern Hemisphere. Nitrogen species are well represented in almost all locations, particularly NO2 in Europe (RMSE below 9 μg m−3). The modeled vertical distribution of NOx and HNO3 are in excellent agreement with the observed values and the spatial and seasonal trends of tropospheric NO2 columns correspond well to observations from SCIAMACHY, capturing the highly polluted areas and the biomass burning cycle throughout the year. Over Asia, the model underestimates NOx from March to August probably due to an underestimation of NOx emissions in the region. Overall, the comparison of the modelled CO and NO2 with MOPITT and SCIAMACHY observations emphasizes the need for more accurate emission rates from anthropogenic and biomass burning sources (i.e., specification of temporal variability). The resulting ozone (O3) burden (348 Tg) lies within the range of other state-of-the-art global atmospheric chemistry models. The model generally captures the spatial and seasonal trends of background surface O3 and its vertical distribution. However, the model tends to overestimate O3 throughout the troposphere in several stations. This is attributed to an overestimation of CO concentration over the southern hemisphere leading to an excessive production of O3. Overall, O3 correlations range between 0.6 to 0.8 for daily mean values. The overall performance of the NMMB/BSC-CTM is comparable to that of other state-of-the-art global chemical transport models.The authors wish to thank WOUDC, GAW, EMEP, WDCGG, CASTNET-EPA, NADP and EANET for the provision of measurement stations. Also, thanks go to the free use of the MOPITT CO data obtained from the NASA Langley Research Center Atmospheric Science Data Center. SCIAMACHY radiances have been provided by ESA. This work is funded by grants CGL2013-46736-R, Supercomputación and e-ciencia Project (CSD2007-0050) from the Consolider-Ingenio 2010 program of the Spanish Ministry of Economy and Competitiveness. Further support was provided by the SEV-2011-00067 grant of the Severo Ochoa Program, awarded by the Spanish Government. A.H. received funding from the Earth System Science Research School (ESSReS), an initiative of the Helmholtz Association of German research centres (HGF) at the AlfredWegener Institute for Polar and Marine Research. All the numerical simulations were performed with the MareNostrum Supercomputer hosted by the Barcelona Supercomputing Center. We also thank Beatriz Monge-Sanz for providing the COPCAT coefficients.Peer ReviewedPostprint (author's final draft

Description and evaluation of the Multiscale Online Nonhydrostatic AtmospheRe CHemistry model (NMMB-MONARCH) version 1.0: gas-phase chemistry at global scale

This paper presents a comprehensive description and benchmark evaluation of the tropospheric gas-phase chemistry component of the Multiscale Online Nonhydrostatic AtmospheRe CHemistry model (NMMB-MONARCH), formerly known as NMMB/BSC-CTM, that can be run on both regional and global domains. Here, we provide an extensive evaluation of a global annual cycle simulation using a variety of background surface stations (EMEP, WDCGG and CASTNET), ozonesondes (WOUDC, CMD and SHADOZ), aircraft data (MOZAIC and several campaigns), and satellite observations (SCIAMACHY and MOPITT). We also include an extensive discussion of our results in comparison to other state-of-the-art models. We note that in this study, we omitted aerosol processes and some natural emissions (lightning and volcano emissions). The model shows a realistic oxidative capacity across the globe. The seasonal cycle for CO is fairly well represented at different locations (correlations around 0.3–0.7 in surface concentrations), although concentrations are underestimated in spring and winter in the Northern Hemisphere, and are overestimated throughout the year at 800 and 500 hPa in the Southern Hemisphere. Nitrogen species are well represented in almost all locations, particularly NO2 in Europe (root mean square error – RMSE – below 5 ppb). The modeled vertical distributions of NOx and HNO3 are in excellent agreement with the observed values and the spatial and seasonal trends of tropospheric NO2 columns correspond well to observations from SCIAMACHY, capturing the highly polluted areas and the biomass burning cycle throughout the year. Over Asia, the model underestimates NOx from March to August, probably due to an underestimation of NOx emissions in the region. Overall, the comparison of the modeled CO and NO2 with MOPITT and SCIAMACHY observations emphasizes the need for more accurate emission rates from anthropogenic and biomass burning sources (i.e., specification of temporal variability). The resulting ozone (O3) burden (348 Tg) lies within the range of other state-of-the-art global atmospheric chemistry models. The model generally captures the spatial and seasonal trends of background surface O3 and its vertical distribution. However, the model tends to overestimate O3 throughout the troposphere in several stations. This may be attributed to an overestimation of CO concentration over the Southern Hemisphere leading to an excessive production of O3 or to the lack of specific chemistry (e.g., halogen chemistry, aerosol chemistry). Overall, O3 correlations range between 0.6 and 0.8 for daily mean values. The overall performance of the NMMB-MONARCH is comparable to that of other state-of-the-art global chemistry models

Chemical and dynamical identification of emission outflows during the HALO campaign EMeRGe in Europe and Asia

The number of large urban agglomerations is steadily increasing worldwide. At a local scale, their emissions lead to air pollution, directly affecting people\u27s health. On a global scale, their emissions lead to an increase of greenhouse gases, affecting climate. In this context, in 2017 and 2018, the airborne campaign EMeRGe (Effect of Megacities on the transport and transformation of pollutants on the Regional to Global scales) investigated emissions of European and Asian major population centres (MPCs) to improve the understanding and predictability of pollution outflows. Here, we present two methods to identify and characterise pollution outflows probed during EMeRGe. First, we use a set of volatile organic compounds (VOCs) as chemical tracers to characterise air masses by specific source signals, i.e. benzene from anthropogenic pollution of targeted regions, acetonitrile from biomass burning (BB, primarily during EMeRGe-Asia), and isoprene from fresh biogenic signals (primarily during EMeRGe-Europe. Second, we attribute probed air masses to source regions and estimate their individual contribution by constructing and applying a simple emission uptake scheme for the boundary layer which combines FLEXTRA back trajectories and EDGAR carbon monoxide (CO) emission rates (acronyms are provided in the Appendix). During EMeRGe-Europe, we identified anthropogenic pollution outflows from northern Italy, southern Great Britain, the Belgium–Netherlands–Ruhr (BNR) area and the Iberian Peninsula. Additionally, our uptake scheme indicates significant long-range transport of pollution from the USA and Canada. During EMeRGe-Asia, the pollution outflow is dominated by sources in China and Taiwan, but BB signals from Southeast Asia and India contribute as well. Outflows of pre-selected MPC targets are identified in less than 20 % of the sampling time, due to restrictions in flight planning and constraints of the measurement platform itself. Still, EMeRGe combines in a unique way near- and far-field measurements, which show signatures of local and distant sources, transport and conversion fingerprints, and complex air mass compositions. Our approach provides a valuable classification and characterisation of the EMeRGe dataset, e.g. for BB and anthropogenic influence of potential source regions and paves the way for a more comprehensive analysis and various model studies

Overview: On the transport and transformation of pollutants in the outflow of major population centres – observational data from the EMeRGe European intensive operational period in summer 2017

Megacities and other major population centres (MPCs) worldwide are major sources of air pollution, both locally as well as downwind. The overall assessment and prediction of the impact of MPC pollution on tropospheric chemistry are challenging. The present work provides an overview of the highlights of a major new contribution to the understanding of this issue based on the data and analysis of the EMeRGe (Effect of Megacities on the transport and transformation of pollutants on the Regional to Global scales) international project. EMeRGe focuses on atmospheric chemistry, dynamics, and transport of local and regional pollution originating in MPCs. Airborne measurements, taking advantage of the long range capabilities of the High Altitude and LOng Range Research Aircraft (HALO, https://www.halo-spp.de, last access: 22 March 2022), are a central part of the project. The synergistic use and consistent interpretation of observational data sets of different spatial and temporal resolution (e.g. from ground-based networks, airborne campaigns, and satellite measurements) supported by modelling within EMeRGe provide unique insight to test the current understanding of MPC pollution outflows. In order to obtain an adequate set of measurements at different spatial scales, two field experiments were positioned in time and space to contrast situations when the photochemical transformation of plumes emerging from MPCs is large. These experiments were conducted in summer 2017 over Europe and in the inter-monsoon period over Asia in spring 2018. The intensive observational periods (IOPs) involved HALO airborne measurements of ozone and its precursors, volatile organic compounds, aerosol particles, and related species as well as coordinated ground-based ancillary observations at different sites. Perfluorocarbon (PFC) tracer releases and model forecasts supported the flight planning, the identification of pollution plumes, and the analysis of chemical transformations during transport. This paper describes the experimental deployment and scientific questions of the IOP in Europe. The MPC targets – London (United Kingdom; UK), the Benelux/Ruhr area (Belgium, the Netherlands, Luxembourg and Germany), Paris (France), Rome and the Po Valley (Italy), and Madrid and Barcelona (Spain) – were investigated during seven HALO research flights with an aircraft base in Germany for a total of 53 flight hours. An in-flight comparison of HALO with the collaborating UK-airborne platform Facility for Airborne Atmospheric Measurements (FAAM) took place to assure accuracy and comparability of the instrumentation on board. Overall, EMeRGe unites measurements of near- and far-field emissions and hence deals with complex air masses of local and distant sources. Regional transport of several European MPC outflows was successfully identified and measured. Chemical processing of the MPC emissions was inferred from airborne observations of primary and secondary pollutants and the ratios between species having different chemical lifetimes. Photochemical processing of aerosol and secondary formation or organic acids was evident during the transport of MPC plumes. Urban plumes mix efficiently with natural sources as mineral dust and with biomass burning emissions from vegetation and forest fires. This confirms the importance of wildland fire emissions in Europe and indicates an important but discontinuous contribution to the European emission budget that might be of relevance in the design of efficient mitigation strategies. The present work provides an overview of the most salient results in the European context, with these being addressed in more detail within additional dedicated EMeRGe studies. The deployment and results obtained in Asia will be the subject of separate publications.The HALO deployment during EMeRGe was funded by a consortium comprising the German Research Foundation (DFG) Priority Program HALO-SPP 1294, the Institute of Atmospheric Physics of DLR, the Max Planck Society (MPG), and the Helmholtz Association. Flora Kluge, Benjamin Schreiner, and Klaus Pfeilsticker acknowledge the support given by the DFG through the project nos. PF 384-16, PF 384-17, and PG 385-19. Ralf Koppmann and Marc Krebsbach acknowledge DFG funding through project no. KR3861_1-1. Katja Bigge acknowledges additional funding from the Heidelberg Graduate School for Physics. Johannes Schneider, Katharina Kaiser, and Stephan Borrmann acknowledge funding through the DFG (project no. 316589531). Lisa Eirenschmalz and Hans Schlager acknowledge support by DFG through project MEPOLL (SCHL1857/4-1). Anna B. Kalisz Hedegaard would like to thank DAAD and DLR for a Research Fellowship. Hans Schlager acknowledge financial support by the DLR TraK (Transport and Climate) project. Michael Sicard acknowledges support from the EU (GA nos. 654109, 778349, 871115, and 101008004) and the Spanish Government (ref. nos. CGL2017-90884-REDT, PID2019-103886RB-I00, RTI2018-096548-B-I00, and MDM-2016-0600). Midhun George, Yangzhuoran Liu, M. Dolores Andrés Hernández, and John Phillip Burrows acknowledge financial support from the University of Bremen. FLEXPART simulations were performed on the HPC cluster Aether at the University of Bremen, financed by DFG within the scope of the Excellence Initiative. Anne-Marlene Blechschmidt was partly funded through the CAMS-84 project. Jennifer Wolf acknowledges support from the German Federal Ministry for Economic Affairs and Energy – BMWi (project Digitally optimized Engineering for Services – DoEfS; contract no. 20X1701B). Theresa Harlass thanks DLR VOR for funding the young investigator research group “Greenhouse Gases”. Mariano Mertens, Patrick Jöckel, and Markus Kilian acknowledge resources of the Deutsches Klimarechenzentrum (DKRZ) granted by the WLA project ID bd0617 for the MECO(n) simulations and the financial support from the DLR projects TraK (Transport und Klima) and the Initiative and Networking Fund of the Helmholtz Association through the project “Advanced Earth System Modelling Capacity” (ESM). Bruna A. Holanda acknowledges the funding from Brazilian CNPq (process 200723/2015-4).Peer ReviewedArticle signat per 53 autors/es: M. Dolores Andrés Hernández (1), Andreas Hilboll (2), Helmut Ziereis (3), Eric Förster (4), Ovid O. Krüger (5), Katharina Kaiser (6,7), Johannes Schneider (7), Francesca Barnaba (8), Mihalis Vrekoussis (2,18), Jörg Schmidt (9), Heidi Huntrieser (3), Anne-Marlene Blechschmidt (1), Midhun George (1), Vladyslav Nenakhov (1,a), Theresa Harlass (3), Bruna A. Holanda (5), Jennifer Wolf (3), Lisa Eirenschmalz (3), Marc Krebsbach (10), Mira L. Pöhlker (5,b), Anna B. Kalisz Hedegaard (3,2), Linlu Mei (1), Klaus Pfeilsticker (11), Yangzhuoran Liu (1), Ralf Koppmann (10), Hans Schlager (3), Birger Bohn (12), Ulrich Schumann (3), Andreas Richter (1), Benjamin Schreiner (11), Daniel Sauer (3), Robert Baumann (3), Mariano Mertens (3), Patrick Jöckel (3), Markus Kilian (3), Greta Stratmann (3,c,) Christopher Pöhlker (5), Monica Campanelli (8), Marco Pandolfi (13), Michael Sicard (14,15), José L. Gómez-Amo (16), Manuel Pujadas (17), Katja Bigge (11), Flora Kluge (11), Anja Schwarz (9), Nikos Daskalakis (2), David Walter (5), Andreas Zahn (4), Ulrich Pöschl (5), Harald Bönisch (4), Stephan Borrmann (6,7), Ulrich Platt (11), and John P. Burrows (1) // (1) Institute of Environmental Physics, University of Bremen, Bremen, Germany; (2) Laboratory for Modeling and Observation of the Earth System, Institute of Environmental Physics, Bremen, Germany; (3) Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany; (4) Atmospheric Trace Gases and Remote Sensing, Karlsruhe Institute of Technology, Institute of Meteorology and Climate Research, Karlsruhe, Germany; (5) Multiphase Chemistry Department, Max Planck Institute for Chemistry, Mainz, Germany; (6) Institute for Atmospheric Physics, Johannes Gutenberg University, Mainz, Germany, (7) Particle Chemistry Department, Max Planck Institute for Chemistry, Mainz, Germany; (8) National Research Council of Italy, Institute of Atmospheric Sciences and Climate (CNR-ISAC), Rome, Italy; (9) Leipzig Institute for Meteorology, Leipzig University, Leipzig, Germany; (10) Institute for Atmospheric and Environmental Research, University of Wuppertal, Wuppertal, Germany; (11) Institute for Environmental Physics, University of Heidelberg, Heidelberg, Germany, (12) Institute of Energy and Climate Research IEK-8, Forschungszentrum Jülich, Jülich, Germany; (13) Consejo Superior de Investigaciones Científicas, Institute of Environmental Assessment and Water Research, Barcelona, Spain; (14) CommSensLab, Department of Signal Theory and Communications, Universitat Politècnica de Catalunya, Barcelona, Spain; (15) Ciències i Tecnologies de l’Espai-Centre de Recerca de l’Aeronàutica i de l’Espai/Institut d’Estudis Espacials de Catalunya), Universitat Politècnica de Catalunya, Barcelona, Spain; (16) Department of Earth Physics and Thermodynamics, University of Valencia, Burjassot, Spain; (17) Atmospheric Pollution Unit, Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (Ciemat), Madrid, Spain; (18) Climate and Atmosphere Research Center (CARE-C), The Cyprus Institute, Nicosia, Cyprus anow at: Flight Experiments, Deutsches Zentrum für Luft- und Raumfahrt (DLR), Oberpfaffenhofen, GermanyPostprint (published version

Impact of forest fires, biogenic emissions and high temperatures on the elevated Eastern Meditteranean ozone levels during the hot summer of 2007

The hot summer of 2007 in southeast Europe has been studied using two regional atmospheric chemistry models; WRF-Chem and EMEP MSC-W. The region was struck by three heat waves and a number of forest fire episodes, greatly affecting air pollution levels. We have focused on ozone and its precursors using state-of-the-art inventories for anthropogenic, biogenic and forest fire emissions. The models have been evaluated against measurement data, and processes leading to ozone formation have been quantified. Heat wave episodes are projected to occur more frequently in a future climate, and therefore this study also makes a contribution to climate change impact research. The plume from the Greek forest fires in August 2007 is clearly seen in satellite observations of CO and NO2 columns, showing extreme levels of CO in and downwind of the fires. Model simulations reflect the location and influence of the fires relatively well, but the modelled magnitude of CO in the plume core is too low. Most likely, this is caused by underestimation of CO in the emission inventories, suggesting that the CO/NOx ratios of fire emissions should be re-assessed. Moreover, higher maximum values are seen in WRF-Chem than in EMEP MSC-W, presumably due to differences in plume rise altitudes as the first model emits a larger fraction of the fire emissions in the lowermost model layer. The model results are also in fairly good agreement with surface ozone measurements. Biogenic VOC emissions reacting with anthropogenic NOx emissions are calculated to contribute significantly to the levels of ozone in the region, but the magnitude and geographical distribution depend strongly on the model and biogenic emission module used. During the July and August heat waves, ozone levels increased substantially due to a combination of forest fire emissions and the effect of high temperatures. We found that the largest temperature impact on ozone was through the temperature dependence of the biogenic emissions, closely followed by the effect of reduced dry deposition caused by closing of the plants’ stomata at very high temperatures. The impact of high temperatures on the ozone chemistry was much lower. The results suggest that forest fire emissions, and the temperature effect on biogenic emissions and dry deposition, will potentially lead to substantial ozone increases in a warmer climate

Chemical and dynamical identification of emission outflows during the HALO campaign EMeRGe in Europe and Asia

The airborne megacity campaign EMeRGe provided an unprecedented amount of trace gas measurements. We combine measured volatile organic compounds (VOCs) with trajectory-modelled emission uptakes to identify potential source regions of pollution. We also characterise the chemical fingerprints (e.g. biomass burning and anthropogenic signatures) of the probed air masses to corroborate the contributing source regions. Our approach is the first large-scale study of VOCs originating from megacities

MAX-DOAS measurements of HONO slant column densities during the MAD-CAT campaign: inter-comparison, sensitivity studies on spectral analysis settings, and error budget

In order to promote the development of the passive DOAS technique the Multi Axis DOAS – Comparison campaign for Aerosols and Trace gases (MAD-CAT) was held at the Max Planck Institute for Chemistry in Mainz, Germany, from June to October 2013. Here, we systematically compare the differential slant column densities (dSCDs) of nitrous acid (HONO) derived from measurements of seven different instruments. We also compare the tropospheric difference of SCDs (delta SCD) of HONO, namely the difference of the SCDs for the non-zenith observations and the zenith observation of the same elevation sequence. Different research groups analysed the spectra from their own instruments using their individual fit software. All the fit errors of HONO dSCDs from the instruments with cooled large-size detectors are mostly in the range of 0.1 to 0.3  ×  1015 molecules cm−2 for an integration time of 1 min. The fit error for the mini MAX-DOAS is around 0.7  ×  1015 molecules cm−2. Although the HONO delta SCDs are normally smaller than 6  ×  1015 molecules cm−2, consistent time series of HONO delta SCDs are retrieved from the measurements of different instruments. Both fits with a sequential Fraunhofer reference spectrum (FRS) and a daily noon FRS lead to similar consistency. Apart from the mini-MAX-DOAS, the systematic absolute differences of HONO delta SCDs between the instruments are smaller than 0.63  ×  1015 molecules cm−2. The correlation coefficients are higher than 0.7 and the slopes of linear regressions deviate from unity by less than 16 % for the elevation angle of 1°. The correlations decrease with an increase in elevation angle. All the participants also analysed synthetic spectra using the same baseline DOAS settings to evaluate the systematic errors of HONO results from their respective fit programs. In general the errors are smaller than 0.3  ×  1015 molecules cm−2, which is about half of the systematic difference between the real measurements.The differences of HONO delta SCDs retrieved in the selected three spectral ranges 335–361, 335–373 and 335–390 nm are considerable (up to 0.57  ×  1015 molecules cm−2) for both real measurements and synthetic spectra. We performed sensitivity studies to quantify the dominant systematic error sources and to find a recommended DOAS setting in the three spectral ranges. The results show that water vapour absorption, temperature and wavelength dependence of O4 absorption, temperature dependence of Ring spectrum, and polynomial and intensity offset correction all together dominate the systematic errors. We recommend a fit range of 335–373 nm for HONO retrievals. In such fit range the overall systematic uncertainty is about 0.87  ×  1015 molecules cm−2, much smaller than those in the other two ranges. The typical random uncertainty is estimated to be about 0.16  ×  1015 molecules cm−2, which is only 25 % of the total systematic uncertainty for most of the instruments in the MAD-CAT campaign. In summary for most of the MAX-DOAS instruments for elevation angle below 5°, half daytime measurements (usually in the morning) of HONO delta SCD can be over the detection limit of 0.2  ×  1015 molecules cm−2 with an uncertainty of  ∼  0.9  ×  1015 molecules cm−2

From the Ethnic History of Asia – the Dōnghú, Wūhuán and Xiānbēi Proto-Mongolian Tribes

Cilj je ovog članka prikazati povijest protomongolskih plemena Dōnghú, Wūhuán i Xiānbēi od 4. stoljeća pr. Kr. do kraja 3. stoljeća po. Kr. Povijest drevnih nomadskih naroda koji su živjeli sjeverno od Kine zapisana je u kineskim dinastijskim kronikama. Protomongolska plemena 1. tisućljeća pr. Kr. u kineskim se izvorima nazivaju Dōnghú. Najstarije vijesti o njima potječu iz razdoblja Zaraćenih država (4. – 3. st. pr. Kr.), a govore o sukobu sa sjevernim kineskim državama. Druga vrsta izvora za povijest protomongolskih plemena arheološki su nalazi, koji mongolsku etnogenezu povezuju s kulturama pločastih grobova, i Mlađi Xiàjiādiàn. Lingvisti građu za istraživanje mongolske etnogeneze pronalaze u altajskoj jezičnoj porodici, kojoj pripada i mongolski jezik. U radu se na temelju navedenih izvora opisuje promjena političke situacije u stepi krajem 3. stoljeća pr. Kr., kada narod Xiōngnú stvara moćnu državu i pokorava Dōnghúe. Ostatke razbijenih Dōnghúa, koji su najvećim dijelom migrirali na sjever, kineske kronike bilježe pod novim topoetnonimima – Xiānbēi i Wūhuán. Slabljenje i pad države Xiōngnúa omogućili su protomongolskim plemenima ponovni izlazak na povijesnu scenu. Kinesko carstvo Hàn uspostavilo je krajem 1. stoljeća pr. Kr. najprije odnose s plemenima Wūhuán, a sredinom 1. stoljeća po. Kr. i s plemenima Xiānbēi. Oba plemenska saveza u početku su priznavala vrhovnu vlast Kine i obavljala graničarsku službu. Pod vodstvom plemenskih starješina u 2. stoljeću po. Kr. počela su voditi samostalnu politiku i napadati pogranična kineska područja. U zaključnom dijelu rada govori se o vremenu kada su plemena Wūhuán i Xiānbēi bila na vrhuncu moći. No već početkom 3. stoljeća Wūhuáni su potpali pod vlast Kineza i Xiānbēija; plemenski savez Xiānbēi raspao se u drugoj polovini 3. stoljeća.The aim of this paper is to present the history of the Dōnghú, Wūhuán and Xiānbēi Proto-Mongolian tribes in the period from the 4th century B.C. to the end of the 3rd century A.D. The history of the ancient nomadic peoples who lived north of China is written in Chinese dynasty chronicles. Proto-Mongolian tribes from the 1st century B.C. are called Dōnghú in Chinese sources. The earliest news on them originates from the Warring States Period (4th – 3rd century B.C.), and tells of a conflict with the northern Chinese states. Other types of sources on the history of the Proto-Mongolian tribes are archaeological findings, which associate Mongolian ethnogenesis with slab grave cultures and the Lower Xiàjiādiàn. Linguists find the materials for the research on Mongolian ethnogenesis in the Altaic linguistic family, which the Mongolian language belongs to as well. Based on the mentioned sources, the change in the political situation in the steppes at the end of the 3rd century B.C., when the people of Xiōngnú created a powerful state and conquered the Dōnghúes, is described in the paper. The remains of the shattered Dōnghúes, who had mostly migrated to the north, have been recorded in Chinese chronicles under new topoethnonyms: Xiānbēi and Wūhuán. The weakening and fall of the Xiōngnúes’ state enabled the Proto-Mongolian tribes to re-enter the historical scene. At the end of the 1st century B.C. the Chinese Hàn Empire firstly established relations with the Wūhuán tribes and in the middle of the 1st century A.D. with the Xiānbēi tribes, too. In the beginning both tribal alliances acknowledged the supreme authority of China and carried out frontier service. Under the guidance of tribal chiefs the tribes started to run an independent policy and attack China’s border areas during the 2nd century A.D. In the conclusion, the author describes the period when the Wūhuán and Xiānbēi tribes were at the peak of their power. However, already at the beginning of the 3rd century, the Wūhuáns fell under the authorities of China and Xiānbēi, but the Xiānbēi tribal alliance fell apart in the second half of the 3rd century