In this work, time-series analyses of the chemical composition and source contributions of PM2.5 from an urban background station in Barcelona (BCN) and a rural background station in Montseny (MSY) ...in northeastern Spain from 2009 to 2018 were investigated and compared. A multisite positive matrix factorization analysis was used to compare the source contributions between the two stations, while the trends for both the chemical species and source contributions were studied using the Theil–Sen trend estimator. Between 2009 and 2018, both stations showed a statistically significant decrease in PM2.5 concentrations, which was driven by the downward trends of levels of chemical species and anthropogenic source contributions, mainly from heavy oil combustion, mixed combustion, industry, and secondary sulfate. These source contributions showed a continuous decrease over the study period, signifying the continuing success of mitigation strategies, although the trends of heavy oil combustion and secondary sulfate have flattened since 2016. Secondary nitrate also followed a significant decreasing trend in BCN, while secondary organic aerosols (SOA) very slightly decreased in MSY. The observed decreasing trends, in combination with the absence of a trend for the organic aerosols (OA) at both stations, resulted in an increase in the relative proportion of OA in PM2.5 by 12% in BCN and 9% in MSY, mostly from SOA, which increased by 7% in BCN and 4% in MSY. Thus, at the end of the study period, OA accounted for 40% and 50% of the annual mean PM2.5 at BCN and MSY, respectively. This might have relevant implications for air quality policies aiming at abating PM2.5 in the study region and for possible changes in toxicity of PM2.5 due to marked changes in composition and source apportionment.
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•PM2.5 levels decreased with −2.8%yr−1 at Barcelona and -3.3%yr−1 in Montseny•Multisite PMF identified 9 common sources between the urban and rural stations•Decrease driven by anthropogenic sources and secondary sulfate at both stations•The relative contribution of secondary organic aerosols increased over 2009–2018•Secondary organic aerosols is the biggest contributing source at both stations
Artificial intelligence (AI) is a branch of Informatics that uses algorithms to tirelessly process data, understand its meaning and provide the desired outcome, continuously redefining its logic. AI ...was mainly introduced via artificial neural networks, developed in the early 1950s, and with its evolution into "computational learning models." Machine Learning analyzes and extracts features in larger data after exposure to examples; Deep Learning uses neural networks in order to extract meaningful patterns from imaging data, even deciphering that which would otherwise be beyond human perception. Thus, AI has the potential to revolutionize the healthcare systems and clinical practice of doctors all over the world. This is especially true for radiologists, who are integral to diagnostic medicine, helping to customize treatments and triage resources with maximum effectiveness. Related in spirit to Artificial intelligence are Augmented Reality, mixed reality, or Virtual Reality, which are able to enhance accuracy of minimally invasive treatments in image guided therapies by Interventional Radiologists. The potential applications of AI in IR go beyond computer vision and diagnosis, to include screening and modeling of patient selection, predictive tools for treatment planning and navigation, and training tools. Although no new technology is widely embraced, AI may provide opportunities to enhance radiology service and improve patient care, if studied, validated, and applied appropriately.
A multiyear climatological study of Saharan dust intrusions in the central Mediterranean in terms of aerosol optical parameters vertical profiles is carried out for the first time. Observations are ...performed at Istituto di Metodologie per l'Analisi Ambientale (IMAA) Raman/elastic lidar station located in Tito Scalo, Potenza (40°36′N, 15°44′E), from May 2000 to April 2003, in the framework of European Aerosol Research Lidar Network (EARLINET). Desert dust aerosols are observed between 1.8 and 9 km in 112 days. Mean values within the desert dust layer of 76 Mm−1, 1.0 Mm−1 sr−1 and 0.54 Mm−1 sr−1 are observed for aerosol extinction at 355 nm and aerosol backscatter at 355 and 532 nm. Desert dust layer optical depth at 355 nm ranges between 0.001 and 0.68, with a mean of 0.13. The source origin is the central Sahara in about 65% of the cases, the western Sahara in about 31%, and only in four cases the eastern Sahara. The most extended database of Saharan dust lidar ratio data was collected: Values range between 6 and 126 sr following a 3‐modal Gaussian distribution centered at 22, 37 and 57 sr. A mean value of 37 sr is found around the center of the Saharan dust layer. At its extremes, where dust particles are mixed to PBL and free troposphere background aerosols, a mean value of 57 sr is found. Finally, low lidar ratio values of about 22 sr are observed when large amount of dust is transported at low altitudes over the Mediterranean Sea.
Following the emergence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) responsible for COVID-19 in December 2019 in Wuhan (China) and its spread to the rest of the world, the ...World Health Organization declared a global pandemic in March 2020. Without effective treatment in the initial pandemic phase, social distancing and mandatory quarantines were introduced as the only available preventative measure. In contrast to the detrimental societal impacts, air quality improved in all countries in which strict lockdowns were applied, due to lower pollutant emissions. Here we investigate the effects of the COVID-19 lockdowns in Europe on ambient black carbon (BC), which affects climate and damages health, using in situ observations from 17 European stations in a Bayesian inversion framework. BC emissions declined by 23 kt in Europe (20 % in Italy, 40 % in Germany, 34 % in Spain, 22 % in France) during lockdowns compared to the same period in the previous 5 years, which is partially attributed to COVID-19 measures. BC temporal variation in the countries enduring the most drastic restrictions showed the most distinct lockdown impacts. Increased particle light absorption in the beginning of the lockdown, confirmed by assimilated satellite and remote sensing data, suggests residential combustion was the dominant BC source. Accordingly, in central and Eastern Europe, which experienced lower than average temperatures, BC was elevated compared to the previous 5 years. Nevertheless, an average decrease of 11 % was seen for the whole of Europe compared to the start of the lockdown period, with the highest peaks in France (42 %), Germany (21 %), UK (13 %), Spain (11 %) and Italy (8 %). Such a decrease was not seen in the previous years, which also confirms the impact of COVID-19 on the European emissions of BC.
Access to detailed comparisons in air quality variations encountered when commuting through a city offers the urban traveller more informed choice on how to minimise personal exposure to inhalable ...pollutants. In this study we report on an experiment designed to compare atmospheric contaminants inhaled during bus, subway train, tram and walking journeys through the city of Barcelona. Average number concentrations of particles 10-300 nm in size, N, are lowest in the commute using subway trains (N<2.5×104part.cm−3), higher during tram travel and suburban walking (2.5×104cm−3<N<5.0×104cm−3), and highest in diesel bus or walking in the city centre (N>5.0×104cm−3), with extreme transient peaks at busy traffic crossings commonly exceeding 1.0×105cm−3 and accompanied by peaks in Black Carbon and CO. Subway particles are coarser (mode 90nm) than in buses, trams or outdoors (<70nm), and concentrations of fine particulate matter (PM2.5) and Black Carbon are lower in the tram when compared to both bus and subway. CO2 levels in public transport reflect passenger numbers, more than tripling from outdoor levels to >1200ppm in crowded buses and trains. There are also striking differences in inhalable particle chemistry depending on the route chosen, ranging from aluminosiliceous at roadsides and near pavement works, ferruginous with enhanced Mn, Co, Zn, Sr and Ba in the subway environment, and higher levels of Sb and Cu inside the bus. We graphically display such chemical variations using a ternary diagram to emphasise how “air quality” in the city involves a consideration of both physical and chemical parameters, and is not simply a question of measuring particle number or mass.
•Big differences in the aerosols inhaled in bus, subway, tram and walking journeys•Particle number concentration is lowest in subway trains and highest in diesel bus•City centre traffic crossings show particle transient peaks >1×105part./cm3•Tram is the cleanest form of city public transport when compared to bus and subway•Subway particles are rich in Fe–Mn, and diesel bus particles are richer in Sb–Cu
•We evaluated multiple machine learning models to estimate black carbon concentration.•BC correlates well with accumulation mode and nitrogen dioxide at the studied sites.•The model trained in ...Barcelona shows good accuracy in other European cities.•The model trained at urban background works well at traffic sites.•We calculated the static and dynamic relative importance to explain black-box models.
Black carbon (BC) has received increasing attention from researchers due to its adverse health effects. However, in-situ BC measurements are often not included as a regulated variable in air quality monitoring networks. Machine learning (ML) models have been studied extensively to serve as virtual sensors to complement the reference instruments. This study evaluates and compares three white-box (WB) and four black-box (BB) ML models to estimate BC concentrations, with the focus to show their transferability and interpretability. We train the models with the long-term air pollutant and weather measurements in Barcelona urban background site, and test them in other European urban and traffic sites. Despite the difference in geographical locations and measurement sites, BC correlates the strongest with particle number concentration of accumulation mode (PNacc, r = 0.73–0.85) and nitrogen dioxide (NO2, r = 0.68–0.85) and the weakest with meteorological parameters. Due to its similarity of correlation behaviour, the ML models trained in Barcelona performs prominently at the traffic site in Helsinki (R2 = 0.80–0.86; mean absolute error MAE = 3.90–4.73 %) and at the urban background site in Dresden (R2 = 0.79–0.84; MAE = 4.23–4.82 %). WB models appear to explain less variability of BC than BB models, long short-term memory (LSTM) model of which outperforms the rest of the models. In terms of interpretability, we adopt several methods for individual model to quantify and normalize the relative importance of each input feature. The overall static relative importance commonly used for WB models demonstrate varying results from the dynamic values utilized to show local contribution used for BB models. PNacc and NO2 on average have the strongest absolute static contribution; however, they simultaneously impact the estimation positively and negatively at different sites. This comprehensive analysis demonstrates that the possibility of these interpretable air pollutant ML models to be transfered across space and time.
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 concentrations and source contribution in Northeast Spain. The study highlights the benefits of combining new datasets and modeling techniques to refine emission inventories and better understand and mitigate air pollution impacts.
Background The impact of shipping emissions on urban agglomerations close to major ports and vessel routes is probably one of the lesser understood aspects of anthropogenic air pollution. Little ...research has been done providing a satisfactory comprehension of the relationship between primary pollutant emissions, secondary aerosols formation and resulting air quality. Materials and methods In this study, multi-year (2003-2007) ambient speciated PM₁₀ and PM₂.₅ data collected at four strategic sampling locations around the Bay of Algeciras (southern Spain), and positive matrix factorisation model were used to identify major PM sources with particular attention paid to the quantification of total shipping emissions. The impact of the emissions from both the harbour of Algeciras and vessel traffic at the Western entrance of Mediterranean Sea (Strait of Gibraltar) were quantified. Ambient levels of V, Ni, La and Ce were used as markers to estimate PM emitted by shipping. Results and discussion Shipping emissions were characterised by La/Ce ratios between 0.6 and 0.8 and V/Ni ratios around 3 for both PM₁₀ and PM₂.₅. In contrast, elevated La/Ce values (1-5) are attributable to emissions from refinery zeolitic fluid catalytic converter plant, and low average V/Ni values (around 1) result mainly from contamination from stainless steel plant emissions. The direct contribution from shipping in the Bay of Algeciras was estimated at 1.4-2.6 μg PM₁₀/m³ (3-7%) and 1.2-2.3 μg PM₂.₅/m³ (5-10%). The total contribution from shipping (primary emissions + secondary sulphate aerosol formation) reached 4.7 μg PM₁₀/m³ (13%) and 4.1 μg PM₂.₅/m³ (17%).
Source apportionment (SA) techniques allocate the measured ambient pollutants with their potential source origin; thus, they are a powerful tool for designing air pollution mitigation strategies. ...Positive Matrix Factorization (PMF) is one of the most widely used SA approaches, and its multi-time resolution (MTR) methodology, which enables mixing different instrument data in their original time resolution, was the focus of this study. One year of co-located measurements in Barcelona, Spain, of non-refractory submicronic particulate matter (NR-PM1), black carbon (BC) and metals were obtained by a Q-ACSM (Aerodyne Research Inc.), an aethalometer (Aerosol d.o.o.) and fine offline quartz-fibre filters, respectively. These data were combined in a MTR PMF analysis preserving the high time resolution (30 min for the NR-PM1 and BC, and 24 h every 4th day for the offline samples). The MTR-PMF outcomes were assessed varying the time resolution of the high-resolution data subset and exploring the error weightings of both subsets. The time resolution assessment revealed that averaging the high-resolution data was disadvantageous in terms of model residuals and environmental interpretability. The MTR-PMF resolved eight PM1 sources: ammonium sulphate + heavy oil combustion (25%), ammonium nitrate + ammonium chloride (17%), aged secondary organic aerosol (SOA) (16%), traffic (14%), biomass burning (9%), fresh SOA (8%), cooking-like organic aerosol (5%), and industry (4%). The MTR-PMF technique identified two more sources relative to the 24 h base case data subset using the same species and four more with respect to the pseudo-conventional approach mimicking offline PMF, indicating that the combination of both high and low TR data is significantly beneficial for SA. Besides the higher number of sources, the MTR-PMF technique has enabled some sources disentanglement compared to the pseudo-conventional and base case PMF as well as the characterisation of their intra-day patterns.
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