Ambient air particulate total lung deposited surface area (LDSA) levels in urban Europe

This study aims to picture the phenomenology of urban ambient total lung deposited surface area (LDSA) (including head/throat (HA), tracheobronchial (TB), and alveolar (ALV) regions) based on multiple path particle dosimetry (MPPD) model during 2017–2019 period collected from urban background (UB, n = 15), traffic (TR, n = 6), suburban background (SUB, n = 4), and regional background (RB, n = 1) monitoring sites in Europe (25) and USA (1). Briefly, the spatial-temporal distribution characteristics of the deposition of LDSA, including diel, weekly, and seasonal patterns, were analyzed. Then, the relationship between LDSA and other air quality metrics at each monitoring site was investigated. The result showed that the peak concentrations of LDSA at UB and TR sites are commonly observed in the morning (06:00–8:00 UTC) and late evening (19:00–22:00 UTC), coinciding with traffic rush hours, biomass burning, and atmospheric stagnation periods. The only LDSA night-time peaks are observed on weekends. Due to the variability of emission sources and meteorology, the seasonal variability of the LDSA concentration revealed significant differences (p = 0.01) between the four seasons at all monitoring sites. Meanwhile, the correlations of LDSA with other pollutant metrics suggested that Aitken and accumulation mode particles play a significant role in the total LDSA concentration. The results also indicated that the main proportion of total LDSA is attributed to the ALV fraction (50 %), followed by the TB (34 %) and HA (16 %). Overall, this study provides valuable information of LDSA as a predictor in epidemiological studies and for the first time presenting total LDSA in a variety of European urban environments.Peer ReviewedPostprint (published version

New particle formation event detection with convolutional neural networks

New aerosol particle formation (NPF) events play a significant role in altering aerosol concentrations and dispersion within the atmosphere, making them vital for both climate and air quality research. The primary objective of investigating NPF events is to precisely determine their occurrence dates. In this study, we introduced the ConvNeXt model for the first time to identify NPF events, and compared its performance with two other deep learning models, EfficientNet and Swin Transformer. Our main aim was to automate an objective identification and classification of NPF events accurately. All three models employed transfer learning to effectively capture critical features associated with NPF. Our results demonstrated that the ConvNeXt model significantly outperformed the other models, achieving an impressive accuracy rate of 95.3% on event days, surpassing EfficientNet (92.8%) and Swin Transformer (94.9%). Furthermore, we performed tests using different ConvNeXt variants (ConvNeXt-T/S/B/L/XL) and different pre-training weights, revealing that different configurations of ConvNeXt models exhibited improved NPF event recognition capabilities. Finally, we conducted generalizability experiments using the ConvNeXt-XL model, achieving the highest accuracy of 96.4% on event days. This study not only underscores the recognition prowess of ConvNeXt models but also highlights their practical utility in accurately detecting NPF events in real-world scenarios. This contribution aids in advancing our comprehension of aerosol dynamics in atmospheric environments, providing valuable insights for climate and air quality research.This study is supported by the RI-URBANS project (Research Infrastructures Services Reinforcing Air Quality Monitoring Capacities in European Urban & Industrial Areas, European Union's Horizon 2020 research and innovation program, Green Deal, European Commission, contract 101036245). This study is also supported by National Natural Science Foundation of China (42101470, 72242106), and Xinjiang Uygur Autonomous Region (2023D01A57), a grant from State Key Laboratory of Resources and Environmental Information System, in part by the Chunhui Project Foundation of the Education Department of China (HZKY20220053), and by the Hungarian Research, Development and Innovation Office (K132254). M. Savadkoohi would like to thank the Spanish Ministry of Science and Innovation for her FPI grant (PRE-2020-095498) and the support from “Agencia Estatal de Investigaci'on” from the Spanish Ministry of Science and Innovation under the project CAIAC (PID2019-108990RB-I00).Peer reviewe

Inter-annual trends of ultrafine particles in urban Europe

Ultrafine particles (UFP, those with diameters ≤ 100 nm), have been reported to potentially penetrate deeply into the respiratory system, translocate through the alveoli, and affect various organs, potentially correlating with increased mortality. The aim of this study is to assess long-term trends (5–11 years) in mostly urban UFP concentrations based on measurements of particle number size distributions (PNSD). Additionally, concentrations of other pollutants and meteorological variables were evaluated to support the interpretations. PNSD datasets from 12 urban background (UB), 5 traffic (TR), 3 suburban background (SUB) and 1 regional background (RB) sites in 15 European cities and 1 in the USA were evaluated. The non-parametric Theil-Sen's method was used to detect monotonic trends. Meta-analyses were carried out to assess the overall trends and those for different environments. The results showed significant decreases in NO, NO2, BC, CO, and particle concentrations in the Aitken (25–100 nm) and the Accumulation (100–800 nm) modes, suggesting a positive impact of the implementation of EURO 5/V and 6/VI vehicle standards on European air quality. The growing use of Diesel Particle Filters (DPFs) might also have clearly reduced exhaust emissions of BC, PM, and the Aitken and Accumulation mode particles. However, as reported by prior studies, there remains an issue of poor control of Nucleation mode particles (smaller than 25 nm), which are not fully reduced with current DPFs, without emission controls for semi-volatile organic compounds, and might have different origins than road traffic. Thus, contrasting trends for Nucleation mode particles were obtained across the cities studied. This mode also affected the UFP and total PNC trends because of the high proportion of Nucleation mode particles in both concentration ranges. It was also found that the urban temperature increasing trends might have also influenced those of PNC, Nucleation and Aitken modes.</p

Validació lagrangiana dels sistemes d'oceanografia operacionals

Trabajo final presentado por Meritxell Garcia Marlès para el grado de Física de la Universitat Autònoma de Barcelona (UAB), realizado bajo la dirección del Dr. Emilio García Ladona y del Dr. Joaquim Ballabrera Poy del Institut de Ciències del Mar (ICM-CSIC).-- 20 pages, 7 figures, 4 tablesEn aquest treball es presenta la validació lagrangiana d’un model de predicció dels oceans disponible a l’Estret de Gibraltar i al mar Alboran. Un cojunt de boies van ser alliberades a l’Estret de Gibraltar al febrer de 2018. La base de dades d’aquestes boies, que inclou les posicions lagrangianes i les velocitats de deriva, és utilitzada com a referència de les observacions, per a avaluar l’habilitat del model oceànic de predicció PdE SAMPA, que pretén reproduir la dinàmica superficial oceànica de la regió del mar Alboran. Les prediccions s’inicialitzen cada un cert temps, i en aquest treball s’ha estudiat les prediccions cada 24 i cada 72 hores. S’aplica una mètrica lagrangiana per a avaluar el rendiment del model per a reproduir les trajectòries observades. Aquesta mètrica es basa en el càlcul del skill score, el qual mesura la precisió d’una predicció en referència a l’observació, tenint en compte les distàncies de separació entre les trajectòries predites i les trajectòries observades. Els resultats obtinguts mostren que el model SAMPA té pitjor rendiment per a prediccions de més curta durada, ja que s’obtenen pitjors resultats en les prediccions cada 24 hores que cada 72. A més, no reprodueix de forma gaire realista la circulació superficial de la regió estudiad

Phenomenology of ultrafine particle concentrations and size distribution across urban Europe

In spite of the important advances in the science of aerosols and air quality, important scientific and environmental challenges remain unsolved, especially those related to source apportionment of the specific components of atmospheric particulate matter (PM), atmospheric processes influencing aerosols, and the associated climate and health impacts. Moreover, ultrafine particle (UFP) studies are growing, they are still insufficient and much needed. Furthermore, there is a clear lack of information and guidance on UFP measurement, especially in smaller ranges. In addition, it is widely recognised that exposure to PM negatively impacts human health (WHO, 2021). In 2016, ambient air pollution accounted for almost seven million premature deaths per year (WHO, 2016), as derived from the aggravation of cardiovascular and respiratory diseases and cancers. Several studies have also shown that UFP can deeply penetrate the respiratory system, thus causing respiratory and cardiovascular diseases in humans (Cassee et al., 2019). The 2017-2019 hourly particle number size distributions (PNSD) from 26 sites in Europe and 1 in the US were evaluated focusing on 16 urban background (UB) and 6 traffic (TR) sites in the framework of RI-URBANS project. The main objective was to describe the phenomenology of urban ultrafine particles in Europe with a significant air quality focus. The varying lower size detection limits made it difficult to compare PN concentrations (PNC), particularly PN10-25, from different cities. PNCs follow a TR>UB>Suburban (SUB) order. PNC and Black Carbon (BC) progressively increase from Northern Europe to Southern Europe and from Western to Eastern Europe. At the UB sites, typical traffic rush hour PNC peaks are evident, many also showing midday-morning PNC peaks anti-correlated with BC. These peaks result from increased PN10-25, suggesting significant PNC contributions from nucleation, fumigation and shipping. Site types to be identified by daily and seasonal PNC and BC patterns are: (i) PNC mainly driven by traffic emissions, with marked correlations with BC on different time scales; (ii) marked midday/morning PNC peaks and a seasonal anti-correlation with PNC/BC; (iii) both traffic peaks and midday peaks without marked seasonal patterns. Groups (ii) and (iii) included cities with high insolation. PNC, especially PN25-800, was positively correlated with BC, NO2, CO and PM for several sites. The variable correlation of PNSD with different urban pollutants demonstrates that these do not reflect the variability of UFP in urban environments. Specific monitoring of PNSD is needed if nanoparticles and their associated health impacts are to be assessed. Implementation of the CEN-ACTRIS recommendations for PNSD measurements would provide comparable measurements, and measurements of <10 nm PNC are needed for full evaluation of the health effects of this size fraction.Peer reviewe

Inter-annual trends of ultrafine particles in urban Europe

International audienc

Phenomenology of ultrafine particle concentrations and size distribution across urban Europe

The 2017-2019 hourly particle number size distributions (PNSD) from 26 sites in Europe and 1 in the US were evaluated focusing on 16 urban background (UB) and 6 traffic (TR) sites in the framework of Research Infrastructures services reinforcing air quality monitoring capacities in European URBAN & industrial areaS (RI-URBANS) project. The main objective was to describe the phenomenology of urban ultrafine particles (UFP) in Europe with a significant air quality focus. The varying lower size detection limits made it difficult to compare PN concentrations (PNC), particularly PN10-25, from different cities. PNCs follow a TR > UB > Suburban (SUB) order. PNC and Black Carbon (BC) progressively increase from Northern Europe to Southern Europe and from Western to Eastern Europe. At the UB sites, typical traffic rush hour PNC peaks are evident, many also showing midday-morning PNC peaks anti-correlated with BC. These peaks result from increased PN10-25, suggesting significant PNC contributions from nucleation, fumigation and shipping. Site types to be identified by daily and seasonal PNC and BC patterns are: (i) PNC mainly driven by traffic emissions, with marked correlations with BC on different time scales; (ii) marked midday/morning PNC peaks and a seasonal anti-correlation with PNC/BC; (iii) both traffic peaks and midday peaks without marked seasonal patterns. Groups (ii) and (iii) included cities with high insolation. PNC, especially PN25-800, was positively correlated with BC, NO2, CO and PM for several sites. The variable correlation of PNSD with different urban pollutants demonstrates that these do not reflect the variability of UFP in urban environments. Specific monitoring of PNSD is needed if nanoparticles and their associated health impacts are to be assessed. Implementation of the CEN-ACTRIS recommendations for PNSD measurements would provide comparable measurements, and measurements of <10 nm PNC are needed for full evaluation of the health effects of this size fraction.This study is supported by the RI-URBANS project (Research Infrastructures Services Reinforcing Air Quality Monitoring Capacities in European Urban & Industrial Areas, European Union’s Horizon 2020 research and innovation programme, Green Deal, European Commission, under grant agreement No 101036245). The authors would like to thank ACTRIS (The Aerosol, Clouds and Trace Gases Research Infrastructure), especially the EBAS Data Centre, for providing datasets for the study. The authors would like to thank also the support from “Agencia Estatal de Investigación” from the Spanish Ministry of Science and Innovation, and FEDER funds under the projects CAIAC (PID2019-108990RB-I00); and the Generalitat de Catalunya (AGAUR 2021 SGR00447) and the Direcció General de Territori. This study is partly funded by the National Institute for Health Research (NIHR) Health Protection Research Unit in Environmental Exposures and Health, a partnership between UK Health Security Agency (UKHSA) and Imperial College London. The views expressed are those of the author(s) and not necessarily those of the NIHR, UKHSA, or the Department of Health and Social Care. The work in Rochester, NY was funded by the New York State Energy Research and Development Authority under contracts #59802 and 125993. This research is also partly supported by the Hungarian Research, Development and Innovation Office (grant no. K132254). We thank the Hessian Agency for Nature Conservation, Environment and Geology (HLNUG), Wiesbaden, Germany for providing concentrations of ancillary pollutants of urban background station at Darmstadt. The Stockholm traffic station (Hornsgatan) datasets were provided thanks to the nPETS project (grant agreement no. 954377) funded by the European Union (EU).Peer reviewe