Suchergebnisse

The characteristics of ozone variations and the impacts of synoptic and local meteorological factors in northern China were quantitatively analyzed during the warm season from 2013 to 2017 based on multi-city in situ ozone and meteorological data as well as meteorological reanalysis. The domain-averaged maximum daily 8 h running average O-3 (MDA8 O-3) concentration was 122 +/- 11 mu g m(-3), with an increase rate of 7.88 lug mu g m(-3) yr(-1), and the three most polluted months were closely related to the variations in the synoptic circulation patterns, which occurred in June (149 mu m(-3)), May (138 mu m(-3)) and July (132 mu g m(-3)). A total of 26 weather types (merged into five weather categories) were objectively identified using the Lamb-Jenkinson method. The highly polluted weather categories included the S-W-N directions (geostrophic wind direction diverts from south to north), low-pressure-related weather types (LP) and cyclone type, which the study area controlled by a low-pressure center (C), and the corresponding domain-averaged MDA8 03 concentrations were 122, 126 and 128 mu g m(-3), respectively. Based on the frequency and intensity changes of the synoptic circulation patterns, 39.2 % of the interannual increase in the domain-averaged O-3 from 2013 to 2017 was attributed to synoptic changes, and the intensity of the synoptic circulation patterns was the dominant factor. Using synoptic classification and local meteorological factors, the segmented synoptic-regression approach was established to evaluate and forecast daily ozone variability on an urban scale. The results showed that this method is practical in most cities, and the dominant factors are the maximum temperature, southerly winds, relative humidity on the previous day and on the same day, and total cloud cover. Overall, 41 %-63 % of the day-today variability in the MDA8 O-3 concentrations was due to local meteorological variations in most cities over northern China, except for two cities: QHD (Qinhuangdao) at 34 % and ZZ (Zhengzhou) at 20 %. Our quantitative exploration of the influence of both synoptic and local meteorological factors on interannual and day-to-day ozone variability will provide a scientific basis for evaluating emission reduction measures that have been implemented by the national and local governments to mitigate air pollution in northern China. ; Peer reviewed

The characteristics of ozone variations and the impacts of synoptic and local meteorological factors in northern China were quantitatively analyzed during the warm season from 2013 to 2017 based on multi-city in situ ozone and meteorological data as well as meteorological reanalysis. The domain-averaged maximum daily 8 h running average O 3 (MDA8 O 3 ) concentration was 122±11 µ g m −3 , with an increase rate of 7.88 µ g m −3 yr −1 , and the three most polluted months were closely related to the variations in the synoptic circulation patterns, which occurred in June (149 µ g m −3 ), May (138 µ g m −3 ) and July (132 µ g m −3 ). A total of 26 weather types (merged into five weather categories) were objectively identified using the Lamb–Jenkinson method. The highly polluted weather categories included the S–W–N directions (geostrophic wind direction diverts from south to north), low-pressure-related weather types (LP) and cyclone type, which the study area controlled by a low-pressure center (C), and the corresponding domain-averaged MDA8 O 3 concentrations were 122, 126 and 128 µ g m −3 , respectively. Based on the frequency and intensity changes of the synoptic circulation patterns, 39.2 % of the interannual increase in the domain-averaged O 3 from 2013 to 2017 was attributed to synoptic changes, and the intensity of the synoptic circulation patterns was the dominant factor. Using synoptic classification and local meteorological factors, the segmented synoptic-regression approach was established to evaluate and forecast daily ozone variability on an urban scale. The results showed that this method is practical in most cities, and the dominant factors are the maximum temperature, southerly winds, relative humidity on the previous day and on the same day, and total cloud cover. Overall, 41 %–63 % of the day-to-day variability in the MDA8 O 3 concentrations was due to local meteorological variations in most cities over northern China, except for two cities: QHD (Qinhuangdao) at 34 % and ZZ (Zhengzhou) at 20 %. Our quantitative exploration of the influence of both synoptic and local meteorological factors on interannual and day-to-day ozone variability will provide a scientific basis for evaluating emission reduction measures that have been implemented by the national and local governments to mitigate air pollution in northern China.

The characteristics of ozone variations and the impacts of synoptic and local meteorological factors in northern China were quantitatively analyzed during the warm season from 2013 to 2017 based on multi-city in situ ozone and meteorological data as well as meteorological reanalysis. The domain-averaged maximum daily 8 h running average O3 (MDA8 O3) concentration was 122±11 µg m−3, with an increase rate of 7.88 µg m−3 yr−1, and the three most polluted months were closely related to the variations in the synoptic circulation patterns, which occurred in June (149 µg m−3), May (138 µg m−3) and July (132 µg m−3). A total of 26 weather types (merged into five weather categories) were objectively identified using the Lamb–Jenkinson method. The highly polluted weather categories included the S–W–N directions (geostrophic wind direction diverts from south to north), low-pressure-related weather types (LP) and cyclone type, which the study area controlled by a low-pressure center (C), and the corresponding domain-averaged MDA8 O3 concentrations were 122, 126 and 128 µg m−3, respectively. Based on the frequency and intensity changes of the synoptic circulation patterns, 39.2 % of the interannual increase in the domain-averaged O3 from 2013 to 2017 was attributed to synoptic changes, and the intensity of the synoptic circulation patterns was the dominant factor. Using synoptic classification and local meteorological factors, the segmented synoptic-regression approach was established to evaluate and forecast daily ozone variability on an urban scale. The results showed that this method is practical in most cities, and the dominant factors are the maximum temperature, southerly winds, relative humidity on the previous day and on the same day, and total cloud cover. Overall, 41 %–63 % of the day-to-day variability in the MDA8 O3 concentrations was due to local meteorological variations in most cities over northern China, except for two cities: QHD (Qinhuangdao) at 34 % and ZZ (Zhengzhou) at 20 %. Our quantitative exploration of the influence of both synoptic and local meteorological factors on interannual and day-to-day ozone variability will provide a scientific basis for evaluating emission reduction measures that have been implemented by the national and local governments to mitigate air pollution in northern China.

In the last decade, North China (NC) has been one of the most populated and polluted regions in the world. The regional air pollution has had a serious impact on people's health; thus, all levels of government have implemented various pollution prevention measures since 2013. Based on multi-city in situ environmental and meteorological data, as well as the meteorological reanalysis dataset from 2013 to 2017, regional pollution characteristics and meteorological formation mechanisms were analyzed to provide a more comprehensive understanding of the evolution of PM2.5 in NC. The domain-averaged PM2.5 was 79 +/- 17 mu g m(-3) from 2013 to 2017, with a decreasing rate of 10 mu g m(-3) yr(-1). Two automatic computer algorithms were established to identify 6 daily regional pollution types (DRPTs) and 48 persistent regional pollution events (PRPEs) over NC during 2014-2017. The average PM2.5 concentration for the Large-Region-Pollution type (including the Large-Moderate-Region-Pollution and Large-Severe-Region-Pollution types) was 113 +/- 40 mu g m(-3), and more than half of Large-Region-Pollution days and PRPEs occurred in winter. The PRPEs in NC mainly developed from the area south of Hebei. The number of Large-Region-Pollution days decreased notably from 2014 to 2017, the annual number of days varying between 194 and 97 days, whereas a slight decline was observed in winter. In addition, the averaged PM2.5 concentrations and the numbers and durations of the PRPEs decreased. Lamb-Jenkinson weather typing was used to reveal the impact of synoptic circulations on PM2.5 across NC. Generally, the contributions of the variations in circulation to the reduction in PM2.5 levels over NC between 2013 and 2017 were 64% and 45% in summer and winter, respectively. The three most highly polluted weather types were types C, S and E, with an average PM2.5 concentration of 137 +/- 40 mu g m(-3) in winter. Furthermore, three typical circulation dynamics were categorized in the peak stage of the PRPEs, namely, the southerly airflow pattern, the northerly airflow pattern and anticyclone pattern; the averaged relative humidity, recirculation index, wind speed and boundary layer height were 63%, 0.33, 2.0 m s(-1) and 493 m, respectively. Our results imply that additional emission reduction measures should be implemented under unfavorable meteorological situations to attain ambient air quality standards in the future. ; Peer reviewed

Although much attention has been paid to investigating and controlling air pollution in China, the trends of air-pollutant concentrations on a national scale have remained unclear. Here, we quantitatively investigated the variation of air pollutants in China using long-term comprehensive data sets from 2013 to 2017, during which Chinese government made major efforts to reduce anthropogenic emission in polluted regions. Our results show a significant decreasing trend in the PM2.5 concentration in heavily polluted regions of eastern China, with an annual decrease of similar to 7% compared with measurements in 2013. The measured decreased concentrations of SO2, NO2 and CO (a proxy for anthropogenic volatile organic compounds) could explain a large fraction of the decreased PM2.5 concentrations in different regions. As a consequence, the heavily polluted days decreased significantly in corresponding regions. Concentrations of organic aerosol, nitrate, sulfate, ammonium and chloride measured in urban Beijing revealed a remarkable reduction from 2013 to 2017, connecting the decreases in aerosol precursors with corresponding chemical components closely. However, surface-ozone concentrations showed increasing trends in most urban stations from 2013 to 2017, which indicates stronger photochemical pollution. The boundary-layer height in capital cities of eastern China showed no significant trends over the Beijing-Tianjin-Hebei, Yangtze River Delta and Pearl River Delta regions from 2013 to 2017, which confirmed the reduction in anthropogenic emissions. Our results demonstrated that the Chinese government was successful in the reduction of particulate matter in urban areas from 2013 to 2017, although the ozone concentration has increased significantly, suggesting a more complex mechanism of improving Chinese air quality in the future. ; Peer reviewed

We investigated the influence of the Chinese New Year (CNY) celebrations on local air quality in Beijing from 2013 through 2019, bringing together comprehensive observations at the newly-constructed Aerosol and Haze Laboratory at Beijing University of Chemical Technology – West Campus (BUCT-AHL) and data from Chinese government air quality measurement stations. In this study, these datasets are used together to provide a detailed analysis of air quality during the CNY over multiple years. Before CNY in 2018, the city of Beijing prohibited the use of fireworks and firecrackers in an effort to reduce air pollution. In 2018 air pollutant concentrations still showed a significant peak during the CNY night, even though not as strong as in previous years, but in 2019, the pollution levels were notably lower. During the studied 7-year study period, it appears that there has been a long-term decrease in CNY related emissions since 2016. Based on our analysis, the pollutants with the most notable spike during CNY were sulfur dioxide and particulate matter, including black carbon. Sulfuric acid concentration followed the sulfur dioxide concentration and showed elevated overnight concentration in CNY 2018, but not notably in 2019. Additionally, spectrometer data and analysis of aerosol particle number size distribution shows direct emissions of particles with diameters around 20 nm during CNY in 2018 and 2019. Meteorological conditions were comparable between the latest two years, indicating that air quality associated with the CNY may be improving, perhaps a positive effect of the restrictions. The longer observations in the future will provide confirmation for these trends.

The vertical profile of new particle formation (NPF) events was studied by comparing the aerosol size number distributions measured aloft and at surface level in a suburban environment in Madrid, Spain, using airborne instruments. The horizontal distribution and regional impact of the NPF events was investigated with data from three urban, urban background, and suburban stations in the Madrid metropolitan area. Intensive regional NPF episodes followed by particle growth were simultaneously recorded at three stations in and around Madrid during a field campaign in July 2016. The urban stations presented larger formation rates compared to the suburban station. Condensation and coagulation sinks followed a similar evolution at all stations, with higher values at urban stations. However, the total number concentration of particles larger than 2.5 nm was lower at the urban station and peaked around noon, when black carbon (BC) levels are at a minimum. The vertical soundings demonstrated that ultrafine particles (UFPs) are formed exclusively inside the mixed layer. As convection becomes more effective and the mixed layer grows, UFPs are detected at higher levels. The morning soundings revealed the presence of a residual layer in the upper levels in which aged particles (nucleated and grown on previous days) prevail. The particles in this layer also grow in size, with growth rates significantly smaller than those inside the mixed layer. Under conditions with strong enough convection, the soundings revealed homogeneous number size distributions and growth rates at all altitudes, which follow the same evolution at the other stations considered in this study. This indicates that UFPs are detected quasi-homogenously in an area spanning at least 17 km horizontally. The NPF events extend over the full vertical extension of the mixed layer, which can reach as high as 3000 m in the area, according to previous studies. On some days a marked decline in particle size (shrinkage) was observed in the afternoon, associated with a change in air masses. Additionally, a few nocturnal nucleation-mode bursts were observed at the urban stations, for which further research is needed to elucidate their origin. © Author(s) 2018. ; This work was supported by the Spanish Ministry of Agriculture, Fishing, Food and Environment; the Ministry of Economy, Industry and Competitiveness; the Madrid City Council and Regional Government; FEDER funds under the project HOUSE (CGL2016-78594-R); the CUD of Zaragoza (project CUD 2016-05); the Government of Catalonia (AGAUR 2017 SGR44); and the Korean Ministry of Environment through "The Eco-Innovation project". The funding received by ERA-PLANET (http://www.era-planet.eu, last access: 16 November 2018), the trans-national project SMURBS (http://www.smurbs.eu, last access: 16 November 2018) (Grant agreement No. 689443), and the support of the Academy of Finland via the Center of Excellence in Atmospheric Sciences are acknowledged. These results are part of a project (ATM-GTP/ERC) that has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (Grant agreement No. 742206). The authors also acknowledge the Doctoral program of Atmospheric Sciences at the University of Helsinki (ATM-DP). Markku Kulmala acknowledges the support of the Academy of Finland via his Academy Professorship (no. 302958). We also thank the City Council of Majadahonda for logistic assistance, and the Instituto de Ciencias Agrarias, Instituto de Salud Carlos III, Alava Ingenieros, TSI, Solma Environmental Solutions, and Airmodus for their support. ; Peer reviewed

Contributors: Hanna K. Lappalainen1,2, Veli-Matti Kerminen1, Tuukka Petäjä1, Theo Kurten3, Aleksander Baklanov4,5, Anatoly Shvidenko6, Jaana Bäck7, Timo Vihma2, Pavel Alekseychik1, Stephen Arnold8, Mikhail Arshinov9, Eija Asmi2, Boris Belan9, Leonid Bobylev10, Sergey Chalov11, Yafang Cheng12, Natalia Chubarova11, Gerrit de Leeuw1,2, Aijun Ding13, Sergey Dobrolyubov11, Sergei Dubtsov14, Egor Dyukarev15, Nikolai Elansky16, Kostas Eleftheriadis17, Igor Esau18, Nikolay Filatov19, Mikhail Flint20, Congbin Fu13, Olga Glezer21, Aleksander Gliko22, Martin Heimann23, Albert A. M. Holtslag24, Urmas Hõrrak25, Juha Janhunen26, Sirkku Juhola27, Leena Järvi1, Heikki Järvinen1, Anna Kanukhina28, Pavel Konstantinov11, Vladimir Kotlyakov29, Antti-Jussi Kieloaho1, Alexander S. Komarov30, Joni Kujansuu1, Ilmo Kukkonen31, Ella Kyrö1, Ari Laaksonen2, Tuomas Laurila2, Heikki Lihavainen2, Alexander Lisitzin32, Aleksander Mahura5, Alexander Makshtas33, Evgeny Mareev34, Stephany Mazon1, Dmitry Matishov35,†, Vladimir Melnikov36, Eugene Mikhailov37, Dmitri Moisseev1, Robert Nigmatulin33, Steffen M. Noe38, Anne Ojala7, Mari Pihlatie1, Olga Popovicheva39, Jukka Pumpanen40, Tatjana Regerand19, Irina Repina16, Aleksei Shcherbinin27, Vladimir Shevchenko33, Mikko Sipilä1, Andrey Skorokhod16, Dominick V. Spracklen8, Hang Su12, Dmitry A. Subetto19, Junying Sun41, Arkady Yu Terzhevik19, Yuri Timofeyev42, Yuliya Troitskaya34, Veli-Pekka Tynkkynen42, Viacheslav I. Kharuk43, Nina Zaytseva22, Jiahua Zhang44, Yrjö Viisanen2, Timo Vesala1, Pertti Hari7, Hans Christen Hansson45, Gennady G. Matvienko9, Nikolai S. Kasimov11, Huadong Guo44, Valery Bondur46, Sergej Zilitinkevich1,2,11,34, and Markku Kulmala1 1Department of Physics, University of Helsinki, 00014 Helsinki, Finland 2Finnish Meteorological Institute, Research and Development, 00101 Helsinki, Finland 3Department of Chemistry, University of Helsinki, 00014 Helsinki, Finland 4World Meteorological Organization, 1211 Genève, Switzerland 5Danish Meteorological Institute, Research and Development Department, 2100, Copenhagen 6International Institute for Applied Systems Analysis, 2361 Laxenburg, Austria 7Department of Forest Sciences, University of Helsinki, 00014 Helsinki, Finland 8Institute for Climate and Atmospheric Science, School of Earth and Environment, University of Leeds, Leeds, LS2 9JT, UK 9Institute of Atmospheric Optics, Russian Academy of Sciences, Tomsk 634021, Russia 10Nansen International Environmental and Remote Sensing Center, St. Petersburg, Russia 11Lomonosov Moscow State University, Faculty of Geography, Moscow 119899, Russia 12Max Planck Institute for Chemistry, 55128 Mainz, Germany 13Institute for Climate and Global Change Research & School of Atmospheric Sciences, Nanjing University, 210023 Nanjing, China 14Institute of Chemical Kinetics & Combustion, Russian Academy of Sciences, 630090 Novosibirsk, Russia 15Institute of Monitoring of Climatic & Ecological Systems SB RAS, 634055 Tomsk, Russia 16A. M. Obukhov Institute of Atmospheric Physics, Russian Academy of Sciences, Russia 17National Centre of Scientific Research "DEMOKRITOS", Greece 18Nansen Environmental and Remote Sensing Center/Bjerknes Centre for Climate Research, 5006 Bergen, Norway 19Northern Water Problems Institute, Karelian Research Center, Russian Academy of Sciences,185003 Petrozavodsk, Russia 20P. P. Shirshov, Institute of Oceanology, Russian Academy of Sciences, Russian Academy of Sciences, 117997 Moscow, Russia 21Institute of Geography, Russian Academy of Sciences, Moscow, Russia 22Depart ment of Earth Sciences of the Russian Academy of Sciences, Russian Academy of Sciences, 119991, Moscow, Russia 23Max-Planck-Institute for Biogeochemistry, 07745 Jena, Germany 24Wageningen University, 6708 Wageningen, Nederland 25Institute of Physics, University of Tartu, 18 Ülikooli St., 50090 Tartu, Estonia 26University of Helsinki, Department of World Cultures, 00014 Helsinki, Finland 27Department of Environmental Sciences, University of Helsinki, 00014 Helsinki, Finland 28Russian State Hydrometeorological University, 195196 Saint Petersburg, Russia 29Institute of Geography, Russian Academy of Sciences, Moscow, Russia 30Institute of Physico-chemical & Biological Problems in Soil Science, Russian Academy of Sciences, 142290 Institutskaya, Russia 31University of Helsinki, Geophysics and Astronomy, 00014 Helsinki, Finland 32Shirshov Institute of Oceanology, Russian Academy of Sciences, 117997 Moscow, Russia 33Actic & Antarctic Research Institute, Russian Academy of Sciences, St. Petersburg 199397, Russia 34Department of Radiophysics, Nizhny Novgorod State University, Nizhny Novgorod, Russia 35Southern Center of Russian Academy of Sciences, Rostov on Don, Russia 36Tyumen Scientific Center, Siberian Branch, Russian Academy of Science, Russia 37Saint Petersburg State University, 7/9 Universitetskaya nab., St. Petersburg, 199034 Russia 38Institute of Agricultural and Environmental Sciences, Estonian University of Life Sciences, 51014 Tartu, Estonia 39Skobeltsyn Institute of Nuclear Physics, Moscow State University, Department Microelectronics, Russia 40University of Eastern Finland, Department of Environmental Science, P.O. Box 1627, FI-70211 Kuopio, Finland 41Craduate University of Chinese Academy of Sciences, 100049 Beijing, China 42Aleksanteri Institute and Department of Social Research, 00014 University of Helsinki, Finland 43Sukachev Forest Institute, Russian Academy of Sciences, Krasnoyarsk 660036, Russia 44Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, 100094, China 45Environmental Science and Analytical Chemistry, Stockholm University, Sweden 46AEROCOSMOS Research Institute for Aerospace Monitoring, 105064, Moscow, Russia †deceased, 20 August 2015 ; The Northern Eurasian regions and Arctic Ocean will very likely undergo substantial changes during the next decades. The arctic-boreal natural environments play a crucial role in the global climate via the albedo change, carbon sources and sinks, as well as atmospheric aerosol production via biogenic volatile organic compounds. Furthermore, it is expected that the global trade activities, demographic movement and use of natural resources will be increasing in the Arctic regions. There is a need for a novel research approach, which not only identifies and tackles the relevant multi-disciplinary research questions, but is also able to make a holistic system analysis of the expected feedbacks. In this paper, we introduce the research agenda of the Pan-Eurasian Experiment (PEEX), a multi-scale, multi-disciplinary and international program started in 2012 (https://www.atm.helsinki.fi/peex/). PEEX is setting a research approach where large-scale research topics are investigated from a system perspective and which aims to fill the key gaps in our understanding of the feedbacks and interactions between the land–atmosphere–aquatic–society continuum in the Northern Eurasian region. We introduce here the state of the art of the key topics in the PEEX research agenda and give the future prospects of the research which we see relevant in this context. ; Peer reviewed