Publications

Using bio-optical parameters as a tool for detecting changes in the phytoplankton community (SW Portugal).

Goela, P.C.; Icely, J.; Cristina, S.; Danchenko, S.; DelValls, T.A.; Newton, A.

Using data insertion with the NAME model to simulate the 8 May 2010 Eyjafjallajökull volcanic ash cloud.

Wilkins, K.L.; Watson, I.M.; Kristiansen, N.I.; Webster, H.N.; Thomson, D.J.; Dacre, H.F.; Prata, A.J.

Using dispersion models at microscale to assess long-term air pollution in urban hot spots: A FAIRMODE joint intercomparison exercise for a case study in Antwerp

Martín, F.; Janssen, S.; Rodrigues, V.; Sousa, J.; Santiago, J.L.; Rivas, E.; Stocker, J.; Jackson, R.; Russo, F.; Villani, M.G.; Tinarelli, G.; Barbero, D.; José, R. San; Pérez-Camanyo, J.L.; Santos, Gabriela Sousa; Bartzis, J.; Sakellaris, I.; Horváth, Z.; Környei, L.; Liszkai, B.; Kovács, A.; Jurado, X.; Reiminger, N.; Thunis, P.; Cuvelier, C.

In the framework of the Forum for Air Quality Modelling in Europe (FAIRMODE), a modelling intercomparison exercise for computing NO2 long-term average concentrations in urban districts with a very high spatial resolution was carried out. This exercise was undertaken for a district of Antwerp (Belgium). Air quality data includes data recorded in air quality monitoring stations and 73 passive samplers deployed during one-month period in 2016. The modelling domain was 800 × 800 m2. Nine modelling teams participated in this exercise providing results from fifteen different modelling applications based on different kinds of model approaches (CFD – Computational Fluid Dynamics-, Lagrangian, Gaussian, and Artificial Intelligence). Some approaches consisted of models running the complete one-month period on an hourly basis, but most others used a scenario approach, which relies on simulations of scenarios representative of wind conditions combined with post-processing to retrieve a one-month average of NO2 concentrations.

The objective of this study is to evaluate what type of modelling system is better suited to get a good estimate of long-term averages in complex urban districts. This is very important for air quality assessment under the European ambient air quality directives. The time evolution of NO2 hourly concentrations during a day of relative high pollution was rather well estimated by all models. Relative to high resolution spatial distribution of one-month NO2 averaged concentrations, Gaussian models were not able to give detailed information, unless they include building data and street-canyon parameterizations. The models that account for complex urban geometries (i.e. CFD, Lagrangian, and AI models) appear to provide better estimates of the spatial distribution of one-month NO2 averages concentrations in the urban canopy. Approaches based on steady CFD-RANS (Reynolds Averaged Navier Stokes) model simulations of meteorological scenarios seem to provide good results with similar quality to those obtained with an unsteady one-month period CFD-RANS simulations.

Using emission estimates as exposure metric: Respiratory disease and outdoor air pollution in Kanpur, India. NILU PP

Bartonova, A.; Liu, H.-Y.; Sharma, M.; Katiyar, K.; Dikshit, O.; Schindler, M.

Using European time use data for modelling individual exposure to air pollution. NILU F

Gerharz, L.E.; Loh, M.; Yang, A.; Kuhn, A.

Using Google Earth Engine Annual Embeddings to Characterize Urban NO₂: First Results from Ecuador and Germany

Alvarez, Cesar; Wurm, Michael; Schneider, Philipp

The AlphaEarth Foundations model, recently released in Google Earth Engine as annual satellite embeddings, provides a new way to work with multi-sensor Earth observation data. Each 10-m pixel is summarized as a 64-dimensional vector that captures the yearly trajectory of surface conditions using information learned from optical, radar, LiDAR, and other datasets, including climatic model outputs and digital terrain data. Rather than representing physical measurements directly, these embeddings condense complex spatial and temporal patterns into compact descriptors that can be used as inputs for machine-learning regression models. This allows researchers to explore environmental patterns—such as air quality—that are influenced by geographical, environmental, and meteorological conditions in cities.In this study, we evaluate whether these annual embeddings, represented as 64 bands (A00–A63), can describe spatial patterns of urban NO₂ without explicitly supplying additional land-use, meteorological, or emission datasets. We present first results from two contrasting environments: Quito, a high-altitude Andean basin in Ecuador, and Essen, a dense urban–industrial region in western Germany. Models trained only with the embedding bands and ground-based NO₂ observations reproduce meaningful spatial gradients in both cities, suggesting that the embeddings encode attributes relevant to emission intensity, urban structure, and pollutant dispersion.These early results highlight the potential of foundation-model satellite embeddings as lightweight, scalable predictors for urban air-quality analyses. They also show how these embeddings can be combined with advanced AI-based regression models, offering a new option for studying air pollution patterns in cities where data availability is often limited by the small number of air-quality monitoring stations.

Using life cycle assessment to inform municipal climate mitigation planning

Thorne, Rebecca Jayne; Bouman, Evert; Guerreiro, Cristina D.b.b.; Majchrzak, Anna; Calus, Sylwia

Local governments can play a key role in reducing emissions associated with local energy use. 17 Polish municipalities provided data on energy use and CO2 emissions for 2015. Life Cycle Assessment (LCA) was used to calculate lifecycle impact indicators for greenhouse gases, particulate matter, acidification and eutrophication associated with the annual energy demand in each municipality. Results showed that impacts from energy use increase almost proportionally with total energy used in the participating municipalities due to the heavy reliance on fossil fuels. Analysis of two municipalities of similar size showed that impacts can be attributed to different usage sectors. For one municipality, energy plans should focus on reducing emissions from private transport and associated fuel use. For the other, energy plans should focus on reducing energy demand from residential buildings. This means that a ‘one-size-fits-all’ energy plan, which may be developed at a national level, would not fit all municipalities. The application of LCA allows for identifying and informing energy planning with impact reduction potential for multiple environmental pressures. Analysis of the provided energy use and CO2 data showed large uncertainties in CO2 emission intensities and allowing for sufficient time and guidance in the energy and emissions accounting is recommended.

Using low-cost air quality sensors for personal exposure assessment

Schneider, Philipp; Castell, Nuria; Bartonova, Alena

Using models to screen the domain of industrial chemicals for potential environmental contaminants.

Sobek, A.; Arnot, J.A.; Breivik, K.; Brown, T.N.; Wania, F.; McLachlan, M.S.

Using proxies to elucidate environmental threats to endangered species.

Kylin, H.; Bouwman, H.; Evans, S.

Using self-organising maps to explore ozone profile validation results - SCIAMACHY limb compared to ground-based lidar observations.

van Gijsel, J.A.E.; Zurita-Milla, R.; Stammes, P.; Godin-Beekmann, S.; Leblanc, T.; Marchand, M.; McDermid, I.S.; Stebel, K.; Steinbrecht, W.; Swart, D.P.J.

Using solid-phase extraction cartridges for sampling of volatile polyfluorinated alkyl substances (PFAS) in indoor air.

Jahnke, A.; Berger, U.; Huber, S.; Kylin, H.; Temme, C.