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Ground-level ozone (O3) is a harmful air pollutant formed in the atmosphere by the interaction between sunlight and precursor gases. Exposure to current O3 levels in Europe is a major source of premature mortality from air pollution. However, mitigation actions have been mainly designed and implemented at the national and regional scales, lacking a comprehensive assessment of the geographic sources of O3 pollution and its associated health impacts. Here we quantify both national and imported contributions to O3 and their related mortality burden across 813 contiguous regions in 35 European countries, representing about 530 million people. Imported O3 contributed to 88.3% of all O3-attributable deaths (intercountry range 83–100%). The greatest share of imported O3 had its origins outside the study domain (that is, hemispheric sources), which was responsible for 56.7% of total O3-attributable mortality (range 42.5–87.2%). It was concluded that achieving the air-quality guidelines set out by the World Health Organization and avoiding the health impacts of O3 require not only the implementation of national or coordinated pan-European actions but also global strategies., H.A. and J.B. acknowledge funding from the EU’s Horizon 2020 and Horizon Europe research and innovation programs under grant agreement nos. 865564 (European Research Council Consolidator Grant EARLY-ADAPT, https://early-adapt.eu/), 101069213 (European Research Council Proof-of-Concept HHS-EWS, https://forecaster. health) and 101123382 (European Research Council Proof-of-Concept FORECAST-AIR), as well as support from grant no. CEX2018-000806-S funded by MCIN/AEI/10.13039/501100011033 and support from Generalitat de Catalunya through the CERCA Program. H.A. also acknowledges funding from the EU’s Horizon Europe research and innovation program under grant agreement no. 101065876 (MSCA Postdoctoral Fellowship TEMP-MOMO). J.B. also acknowledges funding from the Spanish Ministry of Science and Innovation under grant agreement no. RYC2018-025446-I (program Ramón y Cajal). M.T.P. acknowledges Universitat de Barcelona for a postdoctoral grant in regard to the requalification of teaching staff of the Spanish University System (DOGC, 8448, 2.7.2021). BSC coauthors acknowledge support by Ministerio para la Transición Ecológica y el Reto Demográfico as part of Plan Nacional del Ozono project BOE-A-2021-20183, as well as through the VITALISE project (PID2019- 108086RA-I00, MCIN/AEI/10.13039/501100011033) funded by Agencia Estatal de Investigacion. The BSC coauthors also acknowledge the AXA Research Fund and Red Temática ACTRIS España (CGL2017- 90884-REDT), and H2020 ACTRIS IMP (no. 871115), the Department of Research and Universities of the Government of Catalonia through the Atmospheric Composition Research Group (code 2021 SGR 01550), as well as computer resources of MareNostrum and technical support provided by BSC through the RES (nos. AECT-2022-1-0008 and AECT- 2022-2-0003). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript., Peer Reviewed, Objectius de Desenvolupament Sostenible::11 - Ciutats i Comunitats Sostenibles, Objectius de Desenvolupament Sostenible::3 - Salut i Benestar, Postprint (published version)

Els objectius d’aquest Pràcticum són estudiar aquest projecte en profunditat, aconseguir una valoració al màxim d’ajustada d’aquest i aportar propostes constructives per resoldre certs punts considerats millorables un cop analitzat i auditat el projecte inicial. Primer de tot em centro en observar, analitzar i comprendre al màxim el projecte rebut. En una primera fase, per pressupostar el projecte, realitzo la petició d’ofertes corresponent, elaboro els diferents comparatius entre empreses del mateix sector a qui he demanat pressupostos i, finalment, utilitzant el programa Presto he valorat el pressupost gràcies a la informació dels comparatius. Un cop valorat el projecte inicialment rebut, en una segona fase he auditat el projecte per detectar possibles errades del projecte i diferències entre amidaments i documentació gràfica. Un cop detectades, elaboro un segon pressupost ajustant-lo les dades obtingudes de l’auditoria i el comparo amb les dades obtingudes al pressupost de la primera fase. A la tercera fase presento 5 propostes per modificar, amb intenció de millorar, el projecte rebut. Aquestes propostes sorgeixen a mida que desenvolupo l’estudi i detecto punts modificables i millorables del projecte. La tercera fase implica una recerca exhaustiva de materials i sistemes d’aplicació i instal·lació. Un cop realitzada l’auditoria respecte el projecte inicial, considero que l’ajust respecte la valoració del projecte rebut és considerable. Detectar partides amb errors als amidaments o partides duplicades, ha sigut fonamental per aconseguir aquest ajust. Aquest projecte m’ha permès constatar la importància de realitzar una bona feina en fase d’estudis dels projectes, doncs les diferències entre abans i després de realitzar l’auditoria del projecte rebut són notables. Sense un anàlisi en profunditat d’aquest, el pressupost que presentaria al client no s’ajustaria a la realitat de l’obra i tampoc hauria arribat a plantejar-me alguns dels canvis de projecte, presentats

Comprehensive monitoring of NO2 exceedances is imperative for protecting human health, especially in urban areas with traffic. However, an accurate spatial characterization of the exceedances is challenging due to the typically low density of air quality monitoring stations and the inherent uncertainties in urban air quality models. We study how observational data from different sources and timescales can be combined with a dispersion air quality model to obtain bias-corrected NO2 hourly maps at the street scale. We present a kriging-based data fusion workflow that merges dispersion model output with continuous hourly observations and uses a machine-learning-based land use regression (LUR) model constrained with past short intensive passive dosimeter campaign measurements. While the hourly observations allow the bias adjustment of the temporal variability in the dispersion model, the microscale LUR model adds information on the NO2 spatial patterns. Our method includes an uncertainty calculation based on the estimated error variance of the universal kriging technique, which is subsequently used to produce urban maps of probability of exceeding the 200 µg m−3 hourly and the 40 µg m−3 annual NO2 average limits. We assess the statistical performance of this approach in the city of Barcelona for the year 2019. Our results show that simply merging the monitoring stations with the model output already significantly increases the correlation coefficient (r) by +29 % and decreases the root mean square error (RMSE) by −32 %. When adding the time-invariant microscale LUR model in the data fusion workflow, the improvement is even more remarkable, with +46 % and −48 % for the r and RMSE, respectively. Our work highlights the usefulness of high-resolution spatial information in data fusion methods to better estimate exceedances at the street scale., We have received support from the Barcelona City Council through the UncertAIR project (ID 22S09501-001; Recerca Jove i emergent 2022). This research has been supported by the Ministerio de Ciencia e Innovación through the BROWNING project (grant no. RTI2018-099894-BI00), the Agencia Estatal de Investigación as part of the VITALISE project (grant no. PID2019-108086RA-I00) and the MITIGATE project (grant no. PID2020-116324RA695 I00), the H2020 Marie Skłodowska-Curie Actions (grant no. H2020-MSCA-COFUND-2016-754433), the AXA Research Fund, and the Barcelona Supercomputing Center (grant nos. RES-AECT-2021-1-0027 and RES-AECT-2021-2-0001)., Peer Reviewed, Postprint (author's final draft)

Understanding the atmospheric processes involving carbonaceous aerosols (CAs) is crucial for assessing air pollution impacts on human health and climate. The sources and formation mechanisms of CAs are not well understood, making it challenging to quantify impacts in models. Studies suggest residential wood combustion (RWC) and traffic significantly contribute to CAs in Europe's urban and rural areas. Here, we used an atmospheric chemistry model (MONARCH) and three different emission inventories (two versions of the European-scale emission inventory CAMS-REG_v4 and the HERMESv3 detailed national inventory for Spain) to assess the uncertainties in CAs simulation and source allocation (from traffic, RWC, shipping, fires and others) in Northeast Spain. For this, black carbon (BC) and organic aerosol (OA) measurements performed at three supersites representing different environments (urban, regional and remote) were used. Our findings show the importance of model resolution and detailed emission input data in accurately reproducing BC/OA observations. Even though emissions of total particulate matter are rather consistent between inventories in Spain, we found discrepancies between them mainly related to the spatiotemporal disaggregation (particularly relevant for traffic and RWC) and the treatment of the condensable fraction of CAs in RWC (changes in the speciation of elemental/organic carbon). The main source contribution to BC concentrations in the urban site is traffic, accounting for 71.1%/65.2% (January/July) in close agreement with the fossil contribution derived from observations (78.8%/84.2%), followed by RWC (12.8%/3%) and shipping emissions (5.4%/13.8%). An over-representation of RWC (winter) and shipping (summer) is obtained with CAMS-REG_v4. Noteworthy uncertainties arise in OA results due to condensables in emissions and a limited secondary aerosol production in the model. These findings offer insights into MONARCH's effectiveness in simulating CAs concent

Air pollution is a major environmental concern in urban areas, causing substantial adverse effects on human health. This study simulated street-scale resolution air quality for the West Midlands region in the UK using a regional-to-local coupled system, which combined the regional CMAQ model with the local ADMS-Urban model, without double-counting emissions. CMAQ was used to represent dispersion on large temporal and spatial scales, while ADMS-Urban represents the local short-term dispersion from explicit point and road sources. Both models were evaluated against observations with reasonable performance, i.e. CMAQ captured measured air pollutant concentrations at background sites, while coupled ADMS-Urban RML (Regional Model Link) also captured air pollution concentrations at roadside sites, where local effects were important. Street scale air quality maps were produced from the ADMS-Urban RML, which can be linked to health-related exposure studies. The coupled air quality modelling system for WM serves as an effective tool to evaluate potential regional and local air pollution mitigation policies.