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What’s New at AMC-Lahti
Welcome to the AMC-Lahti Team News page. Here we share updates on our latest research activities, events, and team milestones. Stay connected with what’s happening inside our lab and beyond.
MSM / AMC Group Retreat – Hyytiälä, 8–10 September
A three-day retreat bringing together members of the MSM and AMC groups in Hyytiälä, combining scientific presentations, thematic discussions, and social activities to strengthen collaboration and group visibility.
The retreat took place over three days in Hyytiälä. On Day 1 (Monday, 8 September), the program started with a welcome session, introduction, and logistics, followed by the first round of individual participant presentations. The day concluded with dinner and a sauna in the evening. On Day 2 (Tuesday, 9 September), participants continued with a second round of presentations, visited the SMEAR II station, and then split into breakout sessions by research themes: AI applications in atmospheric science (led by Zhi-Song) and the FLEXPART/SOSAA modeling system (led by Petri). The day wrapped up with group reporting and discussions, followed by a lake-side barbecue and sauna. On Day 3 (Wednesday, 10 September), the focus shifted to strategic discussions on whether MSM–AMC–Lahti should merge groups and websites, along with a session on increasing visibility both scientifically and publicly. The retreat ended with final discussions and a closing dinner.
Our group was proud to participate once again in the European Aerosol Conference (EAC 2025) in Lecce, Italy 🇮🇹
Every year, our group takes part in EAC, which brings together researchers from across the world to discuss the latest advances in aerosol science, atmospheric chemistry, climate, air quality, and their societal impacts.
This year, six of our PhD students presented their work:
🖼️ Poster presentations by
Wenqing Peng – Physics-informed neural ODEs for chemical reaction rate estimation
Zeqi Cui – Machine learning to optimize OH-oxidation of naphthalene
Valery Ashu – Uncertainty quantification of reaction coefficients with MCMC
Ammar Kheder – Deep spatio-temporal neural network for air quality reanalysis
Haitong Zhang – Residential wood combustion emissions in Lahti, Finland
🎤 Oral presentation by
Benjamin Foreback – FLEXPART atmospheric back-trajectories for pollution transport to Beijing
summer school, “Application of AI/ML techniques in Atmospheric Science”
The University of Helsinki (UH), Institute for Atmospheric and Earth System Research (INAR), Lappeenranta-Lahti University of Technology (LUT), Department of Computational Engineering and the Lahti University Campus are pleased to announce the summer school “Application of AI/ML techniques in Atmospheric Science, 11th to 15th of August 2025 in Lahti, Finland.
This course will provide introductory lectures on relevant atmospheric topics (e.g., atmospheric chemistry, aerosol dynamics, Earth System models and numerical weather prediction) and computational methods (for data science and machine learning). Additionally, we will form mixed groups with different scientific backgrounds for the hands-on training, covering about half of the course time. Each group will use previously created atmospheric datasets in the exercises to train an end-to-end neural network, like LSTM, RNN, and Transformer. The students will learn basic GPU settings (Google Colab or local machines) and cuda-enable deep learning frameworks (PyTorch). We also provide advanced topics for highly motivated students, including data visualisation, analysis, and model optimisation. At the end of the course, each group will present their outcome and discuss the dis- and advantages of the applied method.
Our PhD student Ammar Kheder presented his first paper at SCIA 2025 in Iceland 🇮🇸
We are proud to share that our PhD student Ammar Kheder presented his first research paper, “Deep Spatio-Temporal Neural Network for Air Quality Reanalysis”, at the 23rd Scandinavian Conference on Image Analysis (SCIA 2025) in Iceland 🇮🇸.
Air quality prediction is key to mitigating health impacts and guiding decisions, yet existing models tend to focus on temporal trends while overlooking spatial generalization. We propose AQ-Net, a spatiotemporal reanalysis model for both observed and unobserved stations in the near future. AQ-Net utilizes the LSTM and multi-head attention for the temporal regression. We also propose a cyclic encoding technique to ensure continuous time representation. To learn fine-grained spatial air quality estimation, we incorporate AQ-Net with the neural kNN to explore feature-based interpolation, such that we can fill the spatial gaps given coarse observation stations. To demonstrate the efficiency of our model for spatiotemporal reanalysis, we use data from 2013–2017 collected in northern China for PM analysis. Extensive experiments show that AQ-Net excels in air quality reanalysis, highlighting the potential of hybrid spatio-temporal models to better capture environmental dynamics—especially in urban areas where both spatial and temporal variability are critical.
Benjamin Foreback has successfully defended his PhD thesis
Benjamin Foreback has successfully defended his PhD thesis titled “Extreme air pollution events in Beijing: Examining their origins and dynamics” at 12:00 on Thursday, the 19th of June 2025 in Aalto Auditorium at Lahti University Campus (Niemenkatu 73, 15140 Lahti).
The opponent was Professor John Lin (University of Utah), and the custos will be Professor Michael Boy (University of Helsinki/LUT University).
Dissertation will also be electronically available before the defense at: Helda e-Thesis
Air quality is an ongoing global challenge faced by modern society, and poor air quality is responsible for more than 6 million premature deaths annually. In addition to problems from long-term exposure to air pollution, short-term exposure to extreme air pollutant concentrations, such as during haze episodes, can lead to acute health problems and hospitalizations. Severe air pollution episodes also cause significant economic damage. This thesis examines two different types of extreme air pollution events. First, we used measurements to investigate the effects caused by fireworks celebrations during the Chinese New Year in Beijing over the course of seven years. Then we conducted a modeling study for a severe wintertime haze episode that affected Beijing and the surrounding region in November 2018. In our Chinese New Year measurement report, we analyzed observations from multiple data sources between 2013 and 2019. In all seven years, we observed a significant increase in concentrations of particulate matter and gaseous pollutants coinciding with the celebration of Chinese New Year. We found a downward trend in concentrations since 2016, which we speculate could be because of increased public awareness of the effects of fireworks on air quality.
Petri Clusius has successfully defended his PhD thesis
Petri Clusius has successfully defended his PhD thesis titled “Process modelling of emissions, chemistry and SOA formation in real environments” on Friday, June 13th at 12:00 in Physicum, Auditorio E204 (Gustaf Hällströmin katu 2, 00560 Helsinki, Finland).
The opponent was Senior Scientist Ulas Im from Århus University, and the Custos is Professor Micael Boy from the University of Helsinki.
If you are unable to attend in person and would like to follow the public examination online, it will be accessible remotely via UniTube.
Dissertation will also be electronically available before the defense at: Helda e-Thesis
Atmospheric aerosols influence weather and global climate through their interactions with clouds. Furthermore, aerosols directly impact air quality and Earth’s radiative budget. Aerosols enter the atmosphere either through the direct emission of particulate matter or via phase transitions from the gaseous to the liquid or solid phase, a process known as new particle formation. Particles in the size range of 50–150 nm in diameter are essential for cloud formation in the atmosphere, as they serve as the initial nuclei for the heterogeneous nucleation of water vapour. The concentration of these cloud condensation nuclei (CCN) affects cloud lifetime, optical properties, and precipitation. The relative contribution of aerosols, and consequently CCN, from direct particle emissions and new particle formation varies significantly across different environments. However, quantifying this remains challenging due to the complex interplay between atmospheric chemistry, direct particle emissions, new particle formation, and the deposition of gases onto these particles. Advances in measurement techniques now allow aerosol composition to be resolved as a function of size, yet identifying the origins and process history of individual particles remains difficult based on measurements alone.
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