Articles | Volume 19, issue 19
https://doi.org/10.5194/acp-19-12531-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-19-12531-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Technical note
| 
09 Oct 2019
Technical note |
| 09 Oct 2019
| 09 Oct 2019
Technical note: Effects of uncertainties and number of data points on line fitting – a case study on new particle formation
Department of Applied Physics, University of Eastern Finland,
Kuopio, Finland
Mikko R. A. Pitkänen
Department of Applied Physics, University of Eastern Finland,
Kuopio, Finland
Finnish Meteorological Institute, Atmospheric Research Centre of
Eastern Finland, Kuopio, Finland
Tuomo Nieminen
Department of Applied Physics, University of Eastern Finland,
Kuopio, Finland
currently at: Institute for Atmospheric and Earth System
Research, University of Helsinki, Helsinki, Finland
Antti Lipponen
Finnish Meteorological Institute, Atmospheric Research Centre of
Eastern Finland, Kuopio, Finland
Sini Isokääntä
Department of Applied Physics, University of Eastern Finland,
Kuopio, Finland
Antti Arola
Finnish Meteorological Institute, Atmospheric Research Centre of
Eastern Finland, Kuopio, Finland
Kari E. J. Lehtinen
Department of Applied Physics, University of Eastern Finland,
Kuopio, Finland
Finnish Meteorological Institute, Atmospheric Research Centre of
Eastern Finland, Kuopio, Finland
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- 1960–1985: anthropogenic aerosols were responsible for the dimming phenomenon in Europe. AOD increased over Southeast Brazil and decreased noticeably over Japan while a small negative trend was found over Oceania;
- 1986–2015: generally, the atmosphere has become much cleaner everywhere after the reversal trend around 1980s mainly observed in Europe.
Aino Ovaska, Daniel Holmberg, Elio Rauth, Mansour Alghamdi, Paulo Artaxo, Eija Asmi, John Backman, Benjamin Bergmans, Matthew Boyer, Liezl Bredenkamp, Maurizio Busetto, Delano Campos De Oliveira, Juan Andrés Casquero-Vera, Darius Ceburnis, Tak Chan, Tommy Chan, Sebastien Conil, Daniele Contini, Suzanne Crumeyrolle, Valentin Duflot, Kostas Eleftheriadis, Johan Esveld, Ekaterina Ezhova, Markus Fiebig, Shahzad Gani, Olga Garmash, Francisco Gómez-Moreno, Roy M. Harrison, Andras Hoffer, Rakesh Hooda, Antti Hyvärinen, Tareq Hussein, Jorma Joutsensaari, Nikos Kalivitis, Heinz Kaminski, Jutta Kesti, Radovan Krejci, Adam Kristensson, Chongai Kuang, Markku Kulmala, Lauri Laakso, Ari Leskinen, Heikki Lihavainen, Andreas Massling, Maik Merkel, Steffen Noe, Jakub Ondracek, Noemí Perez, Jean-Eudes Petit, Tuukka Petäjä, Michael Pikridas, Christopher Pöhlker, Mira Pöhlker, Jean-Philippe Putaud, Ximeng Qi, Cristina Reche, Sergio Rodríguez, Petr Roztocil, Jean Sciare, Karine Sellegri, Dongjie Shang, Ashish Singh, Mikko Sipilä, Henrik Skov, Mar Sorribas, Tamanna Subba, Junying Sun, Peter Tunved, Ville Vakkari, Pieter G. Van Zyl, Aki Virkkula, Jens Voigtländer, Kay Weinhold, Alfred Wiedensohler, Hee-Jung Yoo, Putian Zhou, Kai Puolamäki, Tuomo Nieminen, Veli-Matti Kerminen, Victoria A. Sinclair, and Pauli Paasonen
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Maria Christina Gatou, Antti Lipponen, Ari Leskinen, Petri Tiitta, Maria Filioglou, Xiaoxia Shang, Sanna Pätsi, Annika Saarto, Marjut Roponen, Elina Giannakaki, and Mika Komppula
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Andrea Porcheddu, Ville Kolehmainen, Timo Lähivaara, Robert Levy, Yingxi Rona Shi, Hai Zhang, and Antti Lipponen
EGUsphere, https://doi.org/10.5194/egusphere-2026-1987, https://doi.org/10.5194/egusphere-2026-1987, 2026
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We developed a new way to improve satellite measurements of atmospheric aerosols, which affect air quality and climate. By combining data from two geostationary satellites and using advanced machine learning methods that capture changes over time, we achieved more accurate and detailed results. This approach reduces errors in current satellite-based aerosol data and can better support monitoring and decision making related to air pollution and climate impacts.
Antti Lipponen, Jorge Lezaca, Yves-Marie Saint-Drenan, Marion Schroedter-Homscheidt, and Antti Arola
EGUsphere, https://doi.org/10.5194/egusphere-2026-2743, https://doi.org/10.5194/egusphere-2026-2743, 2026
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We studied what causes errors in Copernicus Atmosphere Monitoring Service solar radiation data using explainable machine learning methods and ground-based observations. The analysis showed that cloud properties are the main source of errors, while aerosols and surface reflectance have smaller effects. The results improve understanding of the strengths and weaknesses of current models and can support future model development, forecasting and solar energy applications.
Nick Schutgens, Elisabeth J. Andrews, Antti Arola, Yusuf Bhatti, Guanliang Fu, Otto Hasekamp, Pekka Kolmonen, Antti Lipponen, Tero Mielonen, Leighton Reygare, and Andrew M. Sayer
EGUsphere, https://doi.org/10.5194/egusphere-2026-2656, https://doi.org/10.5194/egusphere-2026-2656, 2026
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
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Aerosol are fine particles in the air that affect public health and the climate. We develop a new method to study aerosol through patterns (called autocorrelations) in their time-series. We show that autocorrelations are linked to aerosol deposition processes and contain information on the important but uncertain aerosol lifetime. Autocorrelations can be observed from satellites and we provide the first ever global climatology of aerosol autocorrelations.
Matti Niskanen, Aku Ursin, Arttu Ylisirniö, and Kari E. J. Lehtinen
EGUsphere, https://doi.org/10.5194/egusphere-2026-2013, https://doi.org/10.5194/egusphere-2026-2013, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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Measuring tiny airborne particles by their movement in an electric field is common in atmospheric science, but the results almost never include uncertainty estimates. The largest source of uncertainty in these measurements relates to particle charging. We developed a Bayesian method to systematically quantify this uncertainty. Applied to a month of atmospheric data, accounting for charging uncertainty widened the plausible range of particle concentrations by a factor of two to ten.
Vishnu Nair, Edward Gryspeerdt, Antti Arola, Antti Lipponen, and Timo Virtanen
Atmos. Chem. Phys., 26, 4049–4066, https://doi.org/10.5194/acp-26-4049-2026, https://doi.org/10.5194/acp-26-4049-2026, 2026
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William Wandji Nyamsi, Anders V. Lindfors, Angela Meyer, Antti Lipponen, and Antti Arola
Atmos. Meas. Tech., 19, 899–922, https://doi.org/10.5194/amt-19-899-2026, https://doi.org/10.5194/amt-19-899-2026, 2026
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Aino Ovaska, Elio Rauth, Daniel Holmberg, Paulo Artaxo, John Backman, Benjamin Bergmans, Don Collins, Marco Aurélio Franco, Shahzad Gani, Roy M. Harrison, Rakesh K. Hooda, Tareq Hussein, Antti-Pekka Hyvärinen, Kerneels Jaars, Adam Kristensson, Markku Kulmala, Lauri Laakso, Ari Laaksonen, Nikolaos Mihalopoulos, Colin O'Dowd, Jakub Ondracek, Tuukka Petäjä, Kristina Plauškaitė, Mira Pöhlker, Ximeng Qi, Peter Tunved, Ville Vakkari, Alfred Wiedensohler, Kai Puolamäki, Tuomo Nieminen, Veli-Matti Kerminen, Victoria A. Sinclair, and Pauli Paasonen
Aerosol Research, 3, 589–618, https://doi.org/10.5194/ar-3-589-2025, https://doi.org/10.5194/ar-3-589-2025, 2025
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We trained machine learning models to estimate the number of aerosol particles large enough to form clouds and generated daily estimates for the entire globe. The models performed well in many continental regions but struggled in remote and marine areas. Still, this approach offers a way to quantify these particles in areas that lack direct measurements, helping us understand their influence on clouds and climate on a global scale.
Ilona Ylivinkka, Helmi-Marja Keskinen, Lauri R. Ahonen, Liine Heikkinen, Pasi P. Aalto, Tuomo Nieminen, Katrianne Lehtipalo, Krista Luoma, Sujai Banerji, Juho Aalto, Janne Levula, Jutta Kesti, Ekaterina Ezhova, Markku Kulmala, and Tuukka Petäjä
Aerosol Research, 3, 503–520, https://doi.org/10.5194/ar-3-503-2025, https://doi.org/10.5194/ar-3-503-2025, 2025
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Three different particulate matter (PM) measurement techniques were compared in Finland, showing good correlation (R about 0.8). Although the measured PM concentrations were low, decreasing trends were observed in all seasons. The decrease followed the trends observed in concentrations of anthropogenic pollutants, resulting from the EU legislation to improve air quality. The highest PM concentrations were measured in summer, indicating the dominance of organic compounds to the PM mass.
Sini Talvinen, Paul Kim, Emanuele Tovazzi, Eemeli Holopainen, Roxana Cremer, Thomas Kühn, Harri Kokkola, Zak Kipling, David Neubauer, João C. Teixeira, Alistair Sellar, Duncan Watson-Parris, Yang Yang, Jialei Zhu, Srinath Krishnan, Annele Virtanen, and Daniel G. Partridge
Atmos. Chem. Phys., 25, 14449–14478, https://doi.org/10.5194/acp-25-14449-2025, https://doi.org/10.5194/acp-25-14449-2025, 2025
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Climate models struggle to predict how clouds and aerosols interact, affecting Earth’s energy balance. This study compares models to observations to see how they describe effects of clouds and rain on aerosols. While both models show similar overall trends, seasonal differences emerged. These, however, align with differences in key variables participating in cloud formation. The study provides insights on how to improve the representation of aerosol-cloud interactions in climate models.
Antti Vartiainen, Santtu Mikkonen, Ville Leinonen, Tuukka Petäjä, Alfred Wiedensohler, Thomas Kühn, and Tuuli Miinalainen
Atmos. Meas. Tech., 18, 5763–5782, https://doi.org/10.5194/amt-18-5763-2025, https://doi.org/10.5194/amt-18-5763-2025, 2025
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Global climate models, commonly used for climate predictions, struggle at capturing local-scale variations in air quality. We have used measurements of ultrafine particles (UFPs), a less understood air pollutant with potentially significant health implications, for training machine learning models that can substantially reduce the inaccuracy in UFP concentrations predicted by a climate model. This approach could aid epidemiological studies of ultrafine particles by extending exposure records.
Antti Kukkurainen, Antti Mikkonen, Antti Arola, Antti Lipponen, Ville Kolehmainen, and Neus Sabater
Geosci. Model Dev., 18, 7529–7544, https://doi.org/10.5194/gmd-18-7529-2025, https://doi.org/10.5194/gmd-18-7529-2025, 2025
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HAPI2LIBIS is a new software tool that enhances the capabilities of the radiative transfer model libRadtran. It simplifies high-wavelength-resolution simulations by using up-to-date molecular data from the HITRAN (High-Resolution Transmission Molecular Absorption) database and streamlining computations. This tool helps researchers analyze how gases interact with radiation in the Earth's atmosphere and at the surface, improving atmospheric studies and satellite observations and making detailed modeling more accurate and accessible.
Andrea Porcheddu, Ville Kolehmainen, Timo Lähivaara, and Antti Lipponen
Atmos. Meas. Tech., 18, 4771–4789, https://doi.org/10.5194/amt-18-4771-2025, https://doi.org/10.5194/amt-18-4771-2025, 2025
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This study proposes a novel machine learning method to estimate pollution levels (PM2.5) on urban areas at fine scale. Our model generates hourly PM2.5 maps with high spatial resolution, by combining satellite data, ground measurements, geophysical model data, and different geographical indicators. The model properly accounts for spatial and temporal variability of the urban pollution levels, and can be highly beneficial for air quality monitoring and health protection.
Henri Oikarinen, Anni Hartikainen, Pauli Simonen, Miska Olin, Ukko-Ville Mäkinen, Petteri Marjanen, Laura Salo, Ville Silvonen, Sampsa Martikainen, Jussi Hoivala, Mika Ihalainen, Pasi Miettinen, Pasi Yli-Pirilä, Olli Sippula, Santtu Mikkonen, and Panu Karjalainen
Atmos. Meas. Tech., 18, 4271–4292, https://doi.org/10.5194/amt-18-4271-2025, https://doi.org/10.5194/amt-18-4271-2025, 2025
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Fuel-operated auxiliary heaters are used in vehicles to provide extra heating to improve passenger comfort and vehicle functionality in cold climates. Currently heater emissions are not regulated as part of vehicle emissions, so this research was done to assess harmful gaseous and airborne particle emissions from them. Heaters were found to be major source of particles, especially when particles formed after combustion were accounted for, and large carbon monoxide emissions were also observed.
Piaopiao Ke, Anna Lintunen, Pasi Kolari, Annalea Lohila, Santeri Tuovinen, Janne Lampilahti, Roseline Thakur, Maija Peltola, Otso Peräkylä, Tuomo Nieminen, Ekaterina Ezhova, Mari Pihlatie, Asta Laasonen, Markku Koskinen, Helena Rautakoski, Laura Heimsch, Tom Kokkonen, Aki Vähä, Ivan Mammarella, Steffen Noe, Jaana Bäck, Veli-Matti Kerminen, and Markku Kulmala
Biogeosciences, 22, 3235–3251, https://doi.org/10.5194/bg-22-3235-2025, https://doi.org/10.5194/bg-22-3235-2025, 2025
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Our research explores diverse ecosystems’ roles in climate cooling via the concept of CarbonSink+ potential. We measured CO2 uptake and local aerosol production in forests, farms, peatlands, urban gardens, and coastal areas across Finland and Estonia. The long-term data reveal that, while forests are vital with regard to CarbonSink+ potential, farms and urban gardens also play significant roles. These insights can help optimize management policy of natural resources to mitigate global warming.
Xinyang Li, Tuomo Nieminen, Rima Baalbaki, Putian Zhou, Pauli Paasonen, Risto Makkonen, Martha Arbayani Zaidan, Nina Sarnela, Chao Yan, Tuija Jokinen, Imre Salma, Máté Vörösmarty, Tuukka Petäjä, Veli-Matti Kerminen, Markku Kulmala, and Lubna Dada
Aerosol Research, 3, 271–291, https://doi.org/10.5194/ar-3-271-2025, https://doi.org/10.5194/ar-3-271-2025, 2025
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Particle formation rate is one of the key factors in studying the physical properties of aerosols. By developing powerful and simple semi-empirical particle formation rate models, we can predict particle formation rates and compare them with real-time measurements to aid in discovering hidden particle formation mechanisms as well as global simulations of particle population to fill the knowledge gap caused by the uncertainty in aerosol cooling effects on Earth's atmosphere.
Matti Niskanen, Aku Seppänen, Henri Oikarinen, Miska Olin, Panu Karjalainen, Santtu Mikkonen, and Kari Lehtinen
Geosci. Model Dev., 18, 2983–3001, https://doi.org/10.5194/gmd-18-2983-2025, https://doi.org/10.5194/gmd-18-2983-2025, 2025
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Particle size is a key factor determining the properties of aerosol particles which have a major influence on the climate and on human health. When measuring the particle sizes, however, sometimes the sampling lines that transfer the aerosol to the measurement device distort the size distribution, making the measurement unreliable. We propose a method to correct for the distortions and estimate the true particle sizes, improving measurement accuracy.
Timo H. Virtanen, Anu-Maija Sundström, Elli Suhonen, Antti Lipponen, Antti Arola, Christopher O'Dell, Robert R. Nelson, and Hannakaisa Lindqvist
Atmos. Meas. Tech., 18, 929–952, https://doi.org/10.5194/amt-18-929-2025, https://doi.org/10.5194/amt-18-929-2025, 2025
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We find that small particles suspended in the air (aerosols) affect the satellite observations of carbon dioxide (CO2) made by the Orbiting Carbon Observatory-2 satellite instrument. Satellite estimates of CO2 appear to be too high for clean areas and too low for polluted areas. Our results show that CO2 and aerosols are often co-emitted, and this is partly masked out in the current retrievals. Correctly accounting for the aerosol effect is important for CO2 emission estimates by satellites.
Harri Kokkola, Juha Tonttila, Silvia M. Calderón, Sami Romakkaniemi, Antti Lipponen, Aapo Peräkorpi, Tero Mielonen, Edward Gryspeerdt, Timo Henrik Virtanen, Pekka Kolmonen, and Antti Arola
Atmos. Chem. Phys., 25, 1533–1543, https://doi.org/10.5194/acp-25-1533-2025, https://doi.org/10.5194/acp-25-1533-2025, 2025
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Understanding how atmospheric aerosols affect clouds is a scientific challenge. One question is how aerosols affects the amount of cloud water. We used a cloud-scale model to study these effects on marine clouds. The study showed that variations in cloud properties and instrument noise can cause bias in satellite-derived cloud water content. However, our results suggest that for similar weather conditions with well-defined aerosol concentrations, satellite data can reliably track these effects.
Ekaterina Ezhova, Topi Laanti, Anna Lintunen, Pasi Kolari, Tuomo Nieminen, Ivan Mammarella, Keijo Heljanko, and Markku Kulmala
Biogeosciences, 22, 257–288, https://doi.org/10.5194/bg-22-257-2025, https://doi.org/10.5194/bg-22-257-2025, 2025
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Machine learning (ML) models are gaining popularity in biogeosciences. They are applied as gap-filling methods and used to upscale carbon fluxes to larger areas. Here we use explainable artificial intelligence (XAI) methods to elucidate the performance of machine learning models for carbon dioxide fluxes in boreal forests. We show that statistically equal models treat input variables differently. XAI methods can help scientists make informed decisions when applying ML models in their research.
Andrea Porcheddu, Ville Kolehmainen, Timo Lähivaara, and Antti Lipponen
Atmos. Meas. Tech., 17, 5747–5764, https://doi.org/10.5194/amt-17-5747-2024, https://doi.org/10.5194/amt-17-5747-2024, 2024
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This study focuses on improving the accuracy of satellite-based PM2.5 retrieval, crucial for monitoring air quality and its impact on health. It employs machine learning to correct the AOD-to-PM2.5 conversion ratio using various data sources. The approach produces high-resolution PM2.5 estimates with improved accuracy. The method is flexible and can incorporate additional training data from different sources, making it a valuable tool for air quality monitoring and epidemiological studies.
Henk Eskes, Athanasios Tsikerdekis, Melanie Ades, Mihai Alexe, Anna Carlin Benedictow, Yasmine Bennouna, Lewis Blake, Idir Bouarar, Simon Chabrillat, Richard Engelen, Quentin Errera, Johannes Flemming, Sebastien Garrigues, Jan Griesfeller, Vincent Huijnen, Luka Ilić, Antje Inness, John Kapsomenakis, Zak Kipling, Bavo Langerock, Augustin Mortier, Mark Parrington, Isabelle Pison, Mikko Pitkänen, Samuel Remy, Andreas Richter, Anja Schoenhardt, Michael Schulz, Valerie Thouret, Thorsten Warneke, Christos Zerefos, and Vincent-Henri Peuch
Atmos. Chem. Phys., 24, 9475–9514, https://doi.org/10.5194/acp-24-9475-2024, https://doi.org/10.5194/acp-24-9475-2024, 2024
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The Copernicus Atmosphere Monitoring Service (CAMS) provides global analyses and forecasts of aerosols and trace gases in the atmosphere. On 27 June 2023 a major upgrade, Cy48R1, became operational. Comparisons with in situ, surface remote sensing, aircraft, and balloon and satellite observations show that the new CAMS system is a significant improvement. The results quantify the skill of CAMS to forecast impactful events, such as wildfires, dust storms and air pollution peaks.
Xiaoxia Shang, Antti Lipponen, Maria Filioglou, Anu-Maija Sundström, Mark Parrington, Virginie Buchard, Anton S. Darmenov, Ellsworth J. Welton, Eleni Marinou, Vassilis Amiridis, Michael Sicard, Alejandro Rodríguez-Gómez, Mika Komppula, and Tero Mielonen
Atmos. Chem. Phys., 24, 1329–1344, https://doi.org/10.5194/acp-24-1329-2024, https://doi.org/10.5194/acp-24-1329-2024, 2024
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In June 2019, smoke particles from a Canadian wildfire event were transported to Europe. The long-range-transported smoke plumes were monitored with a spaceborne lidar and reanalysis models. Based on the aerosol mass concentrations estimated from the observations, the reanalysis models had difficulties in reproducing the amount and location of the smoke aerosols during the transport event. Consequently, more spaceborne lidar missions are needed for reliable monitoring of aerosol plumes.
Arto Heitto, Cheng Wu, Diego Aliaga, Luis Blacutt, Xuemeng Chen, Yvette Gramlich, Liine Heikkinen, Wei Huang, Radovan Krejci, Paolo Laj, Isabel Moreno, Karine Sellegri, Fernando Velarde, Kay Weinhold, Alfred Wiedensohler, Qiaozhi Zha, Federico Bianchi, Marcos Andrade, Kari E. J. Lehtinen, Claudia Mohr, and Taina Yli-Juuti
Atmos. Chem. Phys., 24, 1315–1328, https://doi.org/10.5194/acp-24-1315-2024, https://doi.org/10.5194/acp-24-1315-2024, 2024
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Particle growth at the Chacaltaya station in Bolivia was simulated based on measured vapor concentrations and ambient conditions. Major contributors to the simulated growth were low-volatility organic compounds (LVOCs). Also, sulfuric acid had major role when volcanic activity was occurring in the area. This study provides insight on nanoparticle growth at this high-altitude Southern Hemispheric site and hence contributes to building knowledge of early growth of atmospheric particles.
Kalle Nordling, Jukka-Pekka Keskinen, Sami Romakkaniemi, Harri Kokkola, Petri Räisänen, Antti Lipponen, Antti-Ilari Partanen, Jaakko Ahola, Juha Tonttila, Muzaffer Ege Alper, Hannele Korhonen, and Tomi Raatikainen
Atmos. Chem. Phys., 24, 869–890, https://doi.org/10.5194/acp-24-869-2024, https://doi.org/10.5194/acp-24-869-2024, 2024
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Our results show that the global model is stable and it provides meaningful results. This way we can include a physics-based presentation of sub-grid physics (physics which happens on a 100 m scale) in the global model, whose resolution is on a 100 km scale.
Diego Aliaga, Santeri Tuovinen, Tinghan Zhang, Janne Lampilahti, Xinyang Li, Lauri Ahonen, Tom Kokkonen, Tuomo Nieminen, Simo Hakala, Pauli Paasonen, Federico Bianchi, Doug Worsnop, Veli-Matti Kerminen, and Markku Kulmala
Aerosol Research, 1, 81–92, https://doi.org/10.5194/ar-1-81-2023, https://doi.org/10.5194/ar-1-81-2023, 2023
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We introduce a novel method for evaluating days when small particles are formed in the atmosphere. Instead of the traditional binary division between event and non-event days, our method, known as "nano ranking analysis", provides a continuous, non-categorical metric for each day. By utilizing data from Hyytiälä, Finland, we show that our approach effectively quantifies these events. This innovative method paves the way for a deeper understanding of the factors influencing particle formation.
Markku Kulmala, Anna Lintunen, Hanna Lappalainen, Annele Virtanen, Chao Yan, Ekaterina Ezhova, Tuomo Nieminen, Ilona Riipinen, Risto Makkonen, Johanna Tamminen, Anu-Maija Sundström, Antti Arola, Armin Hansel, Kari Lehtinen, Timo Vesala, Tuukka Petäjä, Jaana Bäck, Tom Kokkonen, and Veli-Matti Kerminen
Atmos. Chem. Phys., 23, 14949–14971, https://doi.org/10.5194/acp-23-14949-2023, https://doi.org/10.5194/acp-23-14949-2023, 2023
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To be able to meet global grand challenges, we need comprehensive open data with proper metadata. In this opinion paper, we describe the SMEAR (Station for Measuring Earth surface – Atmosphere Relations) concept and include several examples (cases), such as new particle formation and growth, feedback loops and the effect of COVID-19, and what has been learned from these investigations. The future needs and the potential of comprehensive observations of the environment are summarized.
Ville Leinonen, Miska Olin, Sampsa Martikainen, Panu Karjalainen, and Santtu Mikkonen
Atmos. Meas. Tech., 16, 5075–5089, https://doi.org/10.5194/amt-16-5075-2023, https://doi.org/10.5194/amt-16-5075-2023, 2023
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Emission factor calculation was studied to provide models that do not use traditional CO2-based calculation in exhaust plume analysis. Two types of models, one based on the physical dependency of dilution of the exhaust flow rate and speed and two based on the statistical, measured dependency of dilution of the exhaust flow rate, acceleration, speed, altitude change, and/or wind, were developed. These methods could possibly be extended to also calculate non-exhaust emissions in the future.
Yishuo Guo, Chenjuan Deng, Aino Ovaska, Feixue Zheng, Chenjie Hua, Junlei Zhan, Yiran Li, Jin Wu, Zongcheng Wang, Jiali Xie, Ying Zhang, Tingyu Liu, Yusheng Zhang, Boying Song, Wei Ma, Yongchun Liu, Chao Yan, Jingkun Jiang, Veli-Matti Kerminen, Men Xia, Tuomo Nieminen, Wei Du, Tom Kokkonen, and Markku Kulmala
Atmos. Chem. Phys., 23, 6663–6690, https://doi.org/10.5194/acp-23-6663-2023, https://doi.org/10.5194/acp-23-6663-2023, 2023
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Using the comprehensive datasets, we investigated the long-term variations of air pollutants during winter in Beijing from 2019 to 2022 and analyzed the characteristics of atmospheric pollution cocktail during different short-term special events (e.g., Beijing Winter Olympics, COVID lockdown and Chinese New Year) associated with substantial emission reductions. Our results are useful in planning more targeted and sustainable long-term pollution control plans.
Anton Rusanen, Kristo Hõrrak, Lauri R. Ahonen, Tuomo Nieminen, Pasi P. Aalto, Pasi Kolari, Markku Kulmala, Tuukka Petäjä, and Heikki Junninen
Atmos. Meas. Tech., 16, 2781–2793, https://doi.org/10.5194/amt-16-2781-2023, https://doi.org/10.5194/amt-16-2781-2023, 2023
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We present a framework for setting up SMEAR (Station for Measuring Ecosystem–Atmosphere Relations) type measurement station data flows. This framework, called SMEARcore, consists of modular open-source software components that can be chosen to suit various station configurations. The benefits of using this framework are automation of routine operations and real-time monitoring of measurement results.
William Wandji Nyamsi, Yves-Marie Saint-Drenan, Antti Arola, and Lucien Wald
Atmos. Meas. Tech., 16, 2001–2036, https://doi.org/10.5194/amt-16-2001-2023, https://doi.org/10.5194/amt-16-2001-2023, 2023
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The McClear service provides estimates of surface solar irradiances in cloud-free conditions. By comparing McClear estimates to 1 min measurements performed in Sub-Saharan Africa and the Maldives Archipelago in the Indian Ocean, McClear accurately estimates global irradiance and tends to overestimate direct irrradiance. This work establishes a general overview of the performance of the McClear service.
Meri Räty, Larisa Sogacheva, Helmi-Marja Keskinen, Veli-Matti Kerminen, Tuomo Nieminen, Tuukka Petäjä, Ekaterina Ezhova, and Markku Kulmala
Atmos. Chem. Phys., 23, 3779–3798, https://doi.org/10.5194/acp-23-3779-2023, https://doi.org/10.5194/acp-23-3779-2023, 2023
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We utilised back trajectories to identify the source region of air masses arriving in Hyytiälä, Finland, and their travel time over forests. Combined with atmospheric observations, they revealed how air mass transport over the Fennoscandian boreal forest during the growing season produced an accumulation of cloud condensation nuclei and humidity, promoting cloudiness and precipitation. By 55 h of transport, air masses appeared to reach a balanced state with the forest environment.
Tuuli Miinalainen, Harri Kokkola, Antti Lipponen, Antti-Pekka Hyvärinen, Vijay Kumar Soni, Kari E. J. Lehtinen, and Thomas Kühn
Atmos. Chem. Phys., 23, 3471–3491, https://doi.org/10.5194/acp-23-3471-2023, https://doi.org/10.5194/acp-23-3471-2023, 2023
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We simulated the effects of aerosol emission mitigation on both global and regional radiative forcing and city-level air quality with a global-scale climate model. We used a machine learning downscaling approach to bias-correct the PM2.5 values obtained from the global model for the Indian megacity New Delhi. Our results indicate that aerosol mitigation could result in both improved air quality and less radiative heating for India.
Juha Sulo, Janne Lampilahti, Xuemeng Chen, Jenni Kontkanen, Tuomo Nieminen, Veli-Matti Kerminen, Tuukka Petäjä, Markku Kulmala, and Katrianne Lehtipalo
Atmos. Chem. Phys., 22, 15223–15242, https://doi.org/10.5194/acp-22-15223-2022, https://doi.org/10.5194/acp-22-15223-2022, 2022
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We measured atmospheric ion concentrations continuously in a boreal forest between 2005 and 2021 and observed an increasing interannual trend. The increase in cluster ion concentrations can be largely explained by an overall decreasing level of anthropogenic aerosols in the boreal forest. This suggests that the role of ions in atmospheric new particle formation may be more important in the future.
Bernadette Rosati, Sini Isokääntä, Sigurd Christiansen, Mads Mørk Jensen, Shamjad P. Moosakutty, Robin Wollesen de Jonge, Andreas Massling, Marianne Glasius, Jonas Elm, Annele Virtanen, and Merete Bilde
Atmos. Chem. Phys., 22, 13449–13466, https://doi.org/10.5194/acp-22-13449-2022, https://doi.org/10.5194/acp-22-13449-2022, 2022
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Sulfate aerosols have a strong influence on climate. Due to the reduction in sulfur-based fossil fuels, natural sulfur emissions play an increasingly important role. Studies investigating the climate relevance of natural sulfur aerosols are scarce. We study the water uptake of such particles in the laboratory, demonstrating a high potential to take up water and form cloud droplets. During atmospheric transit, chemical processing affects the particles’ composition and thus their water uptake.
Ville Leinonen, Harri Kokkola, Taina Yli-Juuti, Tero Mielonen, Thomas Kühn, Tuomo Nieminen, Simo Heikkinen, Tuuli Miinalainen, Tommi Bergman, Ken Carslaw, Stefano Decesari, Markus Fiebig, Tareq Hussein, Niku Kivekäs, Radovan Krejci, Markku Kulmala, Ari Leskinen, Andreas Massling, Nikos Mihalopoulos, Jane P. Mulcahy, Steffen M. Noe, Twan van Noije, Fiona M. O'Connor, Colin O'Dowd, Dirk Olivie, Jakob B. Pernov, Tuukka Petäjä, Øyvind Seland, Michael Schulz, Catherine E. Scott, Henrik Skov, Erik Swietlicki, Thomas Tuch, Alfred Wiedensohler, Annele Virtanen, and Santtu Mikkonen
Atmos. Chem. Phys., 22, 12873–12905, https://doi.org/10.5194/acp-22-12873-2022, https://doi.org/10.5194/acp-22-12873-2022, 2022
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We provide the first extensive comparison of detailed aerosol size distribution trends between in situ observations from Europe and five different earth system models. We investigated aerosol modes (nucleation, Aitken, and accumulation) separately and were able to show the differences between measured and modeled trends and especially their seasonal patterns. The differences in model results are likely due to complex effects of several processes instead of certain specific model features.
Chao Yan, Yicheng Shen, Dominik Stolzenburg, Lubna Dada, Ximeng Qi, Simo Hakala, Anu-Maija Sundström, Yishuo Guo, Antti Lipponen, Tom V. Kokkonen, Jenni Kontkanen, Runlong Cai, Jing Cai, Tommy Chan, Liangduo Chen, Biwu Chu, Chenjuan Deng, Wei Du, Xiaolong Fan, Xu-Cheng He, Juha Kangasluoma, Joni Kujansuu, Mona Kurppa, Chang Li, Yiran Li, Zhuohui Lin, Yiliang Liu, Yuliang Liu, Yiqun Lu, Wei Nie, Jouni Pulliainen, Xiaohui Qiao, Yonghong Wang, Yifan Wen, Ye Wu, Gan Yang, Lei Yao, Rujing Yin, Gen Zhang, Shaojun Zhang, Feixue Zheng, Ying Zhou, Antti Arola, Johanna Tamminen, Pauli Paasonen, Yele Sun, Lin Wang, Neil M. Donahue, Yongchun Liu, Federico Bianchi, Kaspar R. Daellenbach, Douglas R. Worsnop, Veli-Matti Kerminen, Tuukka Petäjä, Aijun Ding, Jingkun Jiang, and Markku Kulmala
Atmos. Chem. Phys., 22, 12207–12220, https://doi.org/10.5194/acp-22-12207-2022, https://doi.org/10.5194/acp-22-12207-2022, 2022
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Atmospheric new particle formation (NPF) is a dominant source of atmospheric ultrafine particles. In urban environments, traffic emissions are a major source of primary pollutants, but their contribution to NPF remains under debate. During the COVID-19 lockdown, traffic emissions were significantly reduced, providing a unique chance to examine their relevance to NPF. Based on our comprehensive measurements, we demonstrate that traffic emissions alone are not able to explain the NPF in Beijing.
Sini Isokääntä, Paul Kim, Santtu Mikkonen, Thomas Kühn, Harri Kokkola, Taina Yli-Juuti, Liine Heikkinen, Krista Luoma, Tuukka Petäjä, Zak Kipling, Daniel Partridge, and Annele Virtanen
Atmos. Chem. Phys., 22, 11823–11843, https://doi.org/10.5194/acp-22-11823-2022, https://doi.org/10.5194/acp-22-11823-2022, 2022
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This research employs air mass history analysis and observations to study how clouds and precipitation affect atmospheric aerosols during transport to a boreal forest site. The mass concentrations of studied chemical species showed exponential decrease as a function of accumulated rain along the air mass route. Our analysis revealed in-cloud sulfate formation, while no major changes in organic mass were seen. Most of the in-cloud-formed sulfate could be assigned to particle sizes above 200 nm.
Jarmo Mäkelä, Laila Melkas, Ivan Mammarella, Tuomo Nieminen, Suyog Chandramouli, Rafael Savvides, and Kai Puolamäki
Biogeosciences, 19, 2095–2099, https://doi.org/10.5194/bg-19-2095-2022, https://doi.org/10.5194/bg-19-2095-2022, 2022
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Causal structure discovery algorithms have been making headway into Earth system sciences, and they can be used to increase our understanding on biosphere–atmosphere interactions. In this paper we present a procedure on how to utilize prior knowledge of the domain experts together with these algorithms in order to find more robust causal structure models. We also demonstrate how to avoid pitfalls such as over-fitting and concept drift during this process.
Jaakko Ahola, Tomi Raatikainen, Muzaffer Ege Alper, Jukka-Pekka Keskinen, Harri Kokkola, Antti Kukkurainen, Antti Lipponen, Jia Liu, Kalle Nordling, Antti-Ilari Partanen, Sami Romakkaniemi, Petri Räisänen, Juha Tonttila, and Hannele Korhonen
Atmos. Chem. Phys., 22, 4523–4537, https://doi.org/10.5194/acp-22-4523-2022, https://doi.org/10.5194/acp-22-4523-2022, 2022
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Clouds are important for the climate, and cloud droplets have a significant role in cloud properties. Cloud droplets form when air rises and cools and water vapour condenses on small particles that can be natural or of anthropogenic origin. Currently, the updraft velocity, meaning how fast the air rises, is poorly represented in global climate models. In our study, we show three methods that will improve the depiction of updraft velocity and which properties are vital to updrafts.
Marjan Marbouti, Sehyun Jang, Silvia Becagli, Gabriel Navarro, Rita Traversi, Kitack Lee, Tuomo Nieminen, Lisa J. Beck, Markku Kulmala, Veli-Matti Kerminen, and Mikko Sipilä
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2022-52, https://doi.org/10.5194/acp-2022-52, 2022
Publication in ACP not foreseen
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This research was done to understand and investigate the roles of Chl-a, PP and sea ice extent in controlling and producing the in-situ measured MSA, SA, HIO3, HOM and aerosol concentrations over the Greenland and Barents Seas. Our results provide strong support to the hypothesis that MSA, SA and small-particle concentrations in the Svalbard area are directly linked to ocean biological activity and sea ice melting during springtime.
Antti Lipponen, Jaakko Reinvall, Arttu Väisänen, Henri Taskinen, Timo Lähivaara, Larisa Sogacheva, Pekka Kolmonen, Kari Lehtinen, Antti Arola, and Ville Kolehmainen
Atmos. Meas. Tech., 15, 895–914, https://doi.org/10.5194/amt-15-895-2022, https://doi.org/10.5194/amt-15-895-2022, 2022
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We have developed a machine-learning-based model that can be used to correct the Sentinel-3 satellite-based aerosol parameter data of the Synergy data product. The strength of the model is that the original satellite data processing does not have to be carried out again but the correction can be carried out with the data already available. We show that the correction significantly improves the accuracy of the satellite aerosol parameters.
Peifeng Su, Jorma Joutsensaari, Lubna Dada, Martha Arbayani Zaidan, Tuomo Nieminen, Xinyang Li, Yusheng Wu, Stefano Decesari, Sasu Tarkoma, Tuukka Petäjä, Markku Kulmala, and Petri Pellikka
Atmos. Chem. Phys., 22, 1293–1309, https://doi.org/10.5194/acp-22-1293-2022, https://doi.org/10.5194/acp-22-1293-2022, 2022
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We regarded the banana shapes in the surface plots as a special kind of object (similar to cats) and applied an instance segmentation technique to automatically identify the new particle formation (NPF) events (especially the strongest ones), in addition to their growth rates, start times, and end times. The automatic method generalized well on datasets collected in different sites, which is useful for long-term data series analysis and obtaining statistical properties of NPF events.
Arto Heitto, Kari Lehtinen, Tuukka Petäjä, Felipe Lopez-Hilfiker, Joel A. Thornton, Markku Kulmala, and Taina Yli-Juuti
Atmos. Chem. Phys., 22, 155–171, https://doi.org/10.5194/acp-22-155-2022, https://doi.org/10.5194/acp-22-155-2022, 2022
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For atmospheric aerosol particles to take part in cloud formation, they need to be at least a few tens of nanometers in diameter. By using a particle condensation model, we investigated how two types of chemical reactions, oligomerization and decomposition, of organic molecules inside the particle may affect the growth of secondary aerosol particles to these sizes. We show that the effect is potentially significant, which highlights the importance of increasing understanding of these processes.
Anu Kauppi, Antti Kukkurainen, Antti Lipponen, Marko Laine, Antti Arola, Hannakaisa Lindqvist, and Johanna Tamminen
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2021-328, https://doi.org/10.5194/amt-2021-328, 2021
Revised manuscript not accepted
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We present a methodology in Bayesian framework for retrieving atmospheric aerosol optical depth and aerosol type from the pre-computed look-up tables (LUTs). Especially, we consider Bayesian model averaging and uncertainty originating from aerosol model selection and imperfect forward modelling. Our aim is to get more realistic uncertainty estimates. We have applied the methodology to TROPOMI/S5P satellite observations and evaluated the results against ground-based data from the AERONET.
Xiaoxia Shang, Tero Mielonen, Antti Lipponen, Elina Giannakaki, Ari Leskinen, Virginie Buchard, Anton S. Darmenov, Antti Kukkurainen, Antti Arola, Ewan O'Connor, Anne Hirsikko, and Mika Komppula
Atmos. Meas. Tech., 14, 6159–6179, https://doi.org/10.5194/amt-14-6159-2021, https://doi.org/10.5194/amt-14-6159-2021, 2021
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The long-range-transported smoke particles from a Canadian wildfire event were observed with a multi-wavelength Raman polarization lidar and a ceilometer over Kuopio, Finland, in June 2019. The optical properties and the mass concentration estimations were reported for such aged smoke aerosols over northern Europe.
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Short summary
Atmospheric measurement data never come without measurement error. Too often, these errors are neglected when researchers make inferences from their data. We applied multiple line-fitting methods to simulated data mimicking two central variables in aerosol research. Our results show that an ordinary least squares fit, typically used to describe relationships, underestimates the slope of the fit and that methods taking the measurement uncertainty into account performed significantly better.
Atmospheric measurement data never come without measurement error. Too often, these errors are...
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