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Fine particulate matter (PM2.5) is a key air quality indicator due to its adverse health impacts. Accurate PM2.5 assessment requires high-resolution (e.g., atleast 1 km) daily data, yet current methods face challenges in balancing accuracy, coverage, and resolution. Chemical transport models such as those from the Copernicus Atmosphere Monitoring Service (CAMS) offer continuous data but their relatively coarse resolution can introduce uncertainties. Here we present a synergistic Machine Learning (ML)-based approach called S-MESH (Satellite and ML-based Estimation of Surface air quality at High resolution) for estimating daily surface PM2.5 over Europe at 1 km spatial resolution and demonstrate its performance for the years 2021 and 2022. The approach enhances and downscales the CAMS regional ensemble 24 h PM2.5 forecast by training a stacked XGBoost model against station observations, effectively integrating satellite-derived data and modeled meteorological variables. Overall, against station observations, S-MESH (mean absolute error (MAE) of 3.54 μg/m3) shows higher accuracy than the CAMS forecast (MAE of 4.18 μg/m3) and is approaching the accuracy of the CAMS regional interim reanalysis (MAE of 3.21 μg/m3), while exhibiting a significantly reduced mean bias (MB of −0.3 μg/m3 vs. −1.5 μg/m3 for the reanalysis). At the same time, S-MESH requires substantially less computational resources and processing time. At concentrations >20 μg/m3, S-MESH outperforms the reanalysis (MB of −7.3 μg/m3 and -10.3 μg/m3 respectively), and reliably captures high pollution events in both space and time. In the eastern study area, where the reanalysis often underestimates, S-MESH better captures high levels of PM2.5 mostly from residential heating. S-MESH effectively tracks day-to-day variability, with a temporal relative absolute error of 5% (reanalysis 10%). Exhibiting good performance at high pollution events coupled with its high spatial resolution and rapid estimation speed, S-MESH can be highly relevant for air quality assessments where both resolution and timeliness are critical.
Elsevier
2025
Geopolitical events have shown to threaten European energy security in 2022. In Norway, accustomed to low energy prices, the southern part saw 4 times higher electricity prices in 2022 than long term average, whereas in the north, energy prices remained stable. This offers an opportunity to examine the effect of price on household energy consumption and PM2.5 emissions from the residential sector. In the south, electricity consumption went down by 10% while in the north it remained unchanged relative to expected values. While the documented correlation between increased electricity prices and reduced consumption is well-established, our study uniquely captures a substantial shift towards wood as an alternative energy source. In the south, wood for heating increased by approximately 40%, effectively replacing half of the electricity saved. This increase happened despite prices being curbed by strong government subsidies on electricity. Faced with higher energy costs in Europe, we simulate a scenario where consumers across Europe look for affordable energy. With gas and electricity prices predicted to remain well above long-term averages until 2030, biomass will be an attractive option. Our study shows how a shift can endanger Europe's Zero-Pollution strategy, and the need for initiatives targeting the reduction of residential biomass heating.
Elsevier
2024
2024
Air quality and transport behaviour: sensors, field, and survey data from Warsaw, Poland
The present study describes the data sets produced in Warsaw, Poland with the aim of developing tools and methods for the implementation of human-centred and data-driven solutions to the enhancement of sustainable mobility transition. This study focuses on school commutes and alternatives to private cars for children drop off and pick up from primary schools. The dataset enables the complex analysis of interactions between determinants of transport mode choice, revealed choices, and air quality impact. We draw on four data collection methods, namely, (i) air quality and noise sensors’ measurements, (ii) in-person observations of transport behaviours, (iii) travel diaries, and (iv) social surveys. Moreover, all trip data from travel diaries are complemented with the calculated attributes of alternative travel modes. The data produced in the project can be also combined with publicly available information on air quality, public transport schedules, and traffic flows. The present data sets help to open new venues for interdisciplinary analyses of sustainable mobility transition effectiveness and efficiency.
Springer Nature
2024
European pollen reanalysis, 1980–2022, for alder, birch, and olive
The dataset presents a 43 year-long reanalysis of pollen seasons for three major allergenic genera of trees in Europe: alder (Alnus), birch (Betula), and olive (Olea). Driven by the meteorological reanalysis ERA5, the atmospheric composition model SILAM predicted the flowering period and calculated the Europe-wide dispersion pattern of pollen for the years 1980–2022. The model applied an extended 4-dimensional variational data assimilation of in-situ observations of aerobiological networks in 34 European countries to reproduce the inter-annual variability and trends of pollen production and distribution. The control variable of the assimilation procedure was the total pollen release during each flowering season, implemented as an annual correction factor to the mean pollen production. The dataset was designed as an input to studies on climate-induced and anthropogenically driven changes in the European vegetation, biodiversity monitoring, bioaerosol modelling and assessment, as well as, in combination with intra-seasonal observations, for health-related applications.
Springer Nature
2024
Per- and polyfluoroalkyl substances (PFAS) are persistent anthropogenic contaminants, some of which are toxic and bioaccumulative. Perfluoroalkyl carboxylic acids (PFCAs) and perfluoroalkyl sulfonic acids (PFSAs) can form during the atmospheric degradation of precursors such as fluorotelomer alcohols (FTOHs), N-alkylated perfluoroalkane sulfonamides (FASAs), and hydrofluorocarbons (HFCs). Since PFCAs and PFSAs will readily undergo wet deposition, snow and ice cores are useful for studying PFAS in the Arctic atmosphere. In this study, 36 PFAS were detected in surface snow around the Arctic island of Spitsbergen during January–August 2019 (i.e., 24 h darkness to 24 h daylight), indicating widespread and chemically diverse contamination, including at remote high elevation sites. Local sources meant some PFAS had concentrations in snow up to 54 times higher in Longyearbyen, compared to remote locations. At a remote high elevation ice cap, where PFAS input was from long-range atmospheric processes, the median deposition fluxes of C2–C11 PFCAs, PFOS and HFPO–DA (GenX) were 7.6–71 times higher during 24 h daylight. These PFAS all positively correlated with solar flux. Together this suggests seasonal light is important to enable photochemistry for their atmospheric formation and subsequent deposition in the Arctic. This study provides the first evidence for the possible atmospheric formation of PFOS and GenX from precursors.
2024
We determine the global emission distribution of the potent greenhouse gas sulfur hexafluoride (SF6) for the period 2005–2021 using inverse modelling. The inversion is based on 50 d backward simulations with the Lagrangian particle dispersion model (LPDM) FLEXPART and on a comprehensive observation data set of SF6 mole fractions in which we combine continuous with flask measurements sampled at fixed surface locations and observations from aircraft and ship campaigns. We use a global-distribution-based (GDB) approach to determine baseline mole fractions directly from global SF6 mole fraction fields at the termination points of the backward trajectories. We compute these fields by performing an atmospheric SF6 re-analysis, assimilating global SF6 observations into modelled global three-dimensional mole fraction fields. Our inversion results are in excellent agreement with several regional inversion studies in the USA, Europe, and China. We find that (1) annual US SF6 emissions strongly decreased from 1.25 Gg in 2005 to 0.48 Gg in 2021; however, they were on average twice as high as the reported emissions to the United Nations. (2) SF6 emissions from EU countries show an average decreasing trend of −0.006 Gg yr−1 during the period 2005 to 2021, including a substantial drop in 2018. This drop is likely a direct result of the EU's F-gas regulation 517/2014, which bans the use of SF6 for recycling magnesium die-casting alloys as of 2018 and requires leak detection systems for electrical switch gear. (3) Chinese annual emissions grew from 1.28 Gg in 2005 to 5.16 Gg in 2021, with a trend of 0.21 Gg yr−1, which is even higher than the average global total emission trend of 0.20 Gg yr−1. (4) National reports for the USA, Europe, and China all underestimated their SF6 emissions. (5) Our results indicate increasing emissions in poorly monitored areas (e.g. India, Africa, and South America); however, these results are uncertain due to weak observational constraints, highlighting the need for enhanced monitoring in these areas. (6) Global total SF6 emissions are comparable to estimates in previous studies but are sensitive to a priori estimates due to the low network sensitivity in poorly monitored regions. (7) Monthly inversions indicate that SF6 emissions in the Northern Hemisphere were on average higher in summer than in winter throughout the study period.
2024
Toward Standardization of a Lung New Approach Model for Toxicity Testing of Nanomaterials
This study represents an attempt toward the standardization of pulmonary NAMs and the development of a novel approach for toxicity testing of nanomaterials. Laboratory comparisons are challenging yet essential for identifying existing limitations and proposing potential solutions. Lung cells cultivated and exposed at the air-liquid interface (ALI) more accurately represent the physiology of human lungs and pulmonary exposure scenarios than submerged cell and exposure models. A triculture cell model system was used, consisting of human A549 lung epithelial cells and differentiated THP-1 macrophages on the apical side, with EA.hy926 endothelial cells on the basolateral side. The cells were exposed to silver nanoparticles NM-300K for 24 h. The model used here showed to be applicable for assessing the hazards of nanomaterials and chemicals, albeit with some limitations. Cellular viability was measured using the alamarBlue assay, DNA damage was assessed with the enzyme-modified comet assay, and the expression of 40 genes related to cell viability, inflammation, and DNA damage response was evaluated through RT2 gene expression profiling. Despite harmonized protocols used in the two independent laboratories, however, some methodological challenges could affect the results, including sensitivity and reproducibility of the model.
MDPI
2024
SuperDARN Radar Wind Observations of Eastward-Propagating Planetary Waves
An array of SuperDARN meteor radars at northern high latitudes was used to investigate the sources and characteristics of eastward-propagating planetary waves (EPWs) at 95 km, with a focus on wintertime. The nine radars provided the daily mean meridional winds and their anomalies over 180 degrees of longitude, and these anomalies were separated into eastward and westward waves using a fast Fourier transform (FFT) method to extract the planetary wave components of zonal wavenumbers 1 and 2. Years when a sudden stratospheric warming event with an elevated stratopause (ES-SSW) occurred during the winter were contrasted with years without such events and composited through superposed epoch analysis. The results show that EPWs are a ubiquitous—and unexpected—feature of meridional wind variability near 95 km. Present even in non-ES-SSW years, they display a regular annual cycle peaking in January or February, depending on the zonal wavenumber. In years when an ES-SSW occurred, the EPWs were highly variable but enhanced before and after the onset.
MDPI
2024
FLEXPART version 11: improved accuracy, efficiency, and flexibility
Numerical methods and simulation codes are essential for the advancement of our understanding of complex atmospheric processes. As technology and computer hardware continue to evolve, the development of sophisticated code is vital for accurate and efficient simulations. In this paper, we present the recent advancements made in the FLEXible PARTicle dispersion model (FLEXPART), a Lagrangian particle dispersion model, which has been used in a wide range of atmospheric transport studies over the past 3 decades, extending from tracing radionuclides from the Fukushima nuclear disaster, to inverse modelling of greenhouse gases, and to the study of atmospheric moisture cycles.
This version of FLEXPART includes notable improvements in accuracy and computational efficiency. (1) By leveraging the native vertical coordinates of European Centre for Medium Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS) instead of interpolating to terrain-following coordinates, we achieved an improvement in trajectory accuracy, leading to a ∼8 %–10 % reduction in conservation errors for quasi-conservative quantities like potential vorticity. (2) The shape of aerosol particles is now accounted for in the gravitational settling and dry-deposition calculation, increasing the simulation accuracy for non-spherical aerosol particles such as microplastic fibres. (3) Wet deposition has been improved by the introduction of a new below-cloud scheme, by a new cloud identification scheme, and by improving the interpolation of precipitation. (4) Functionality from a separate version of FLEXPART, the FLEXPART CTM (chemical transport model), is implemented, which includes linear chemical reactions. Additionally, the incorporation of Open Multi-Processing parallelisation makes the model better suited for handling large input data. Furthermore, we introduced novel methods for the input and output of particle properties and distributions. Users now have the option to run FLEXPART with more flexible particle input data, providing greater adaptability for specific research scenarios (e.g. effective backward simulations corresponding to satellite retrievals). Finally, a new user manual (https://flexpart.img.univie.ac.at/docs/, last access: 11 September 2024) and restructuring of the source code into modules will serve as a basis for further development.
2024