Recommendations for reporting equivalent black carbon (eBC) mass concentrations based on long-term pan-European in-situ observations.

A reliable determination of equivalent black carbon (eBC) mass concentrations derived from filter absorption photometers (FAPs) measurements depends on the appropriate quantification of the mass absorption cross-section (MAC) for converting the absorption coefficient (babs) to eBC. This study investigates the spatial-temporal variability of the MAC obtained from simultaneous elemental carbon (EC) and babs measurements performed at 22 sites. We compared different methodologies for retrieving eBC integrating different options for calculating MAC including: locally derived, median value calculated from 22 sites, and site-specific rolling MAC. The eBC concentrations that underwent correction using these methods were identified as LeBC (local MAC), MeBC (median MAC), and ReBC (Rolling MAC) respectively. Pronounced differences (up to more than 50 %) were observed between eBC as directly provided by FAPs (NeBC; Nominal instrumental MAC) and ReBC due to the differences observed between the experimental and nominal MAC values. The median MAC was 7.8 ± 3.4 m2 g-1 from 12 aethalometers at 880 nm, and 10.6 ± 4.7 m2 g-1 from 10 MAAPs at 637 nm. The experimental MAC showed significant site and seasonal dependencies, with heterogeneous patterns between summer and winter in different regions. In addition, long-term trend analysis revealed statistically significant (s.s.) decreasing trends in EC. Interestingly, we showed that the corresponding corrected eBC trends are not independent of the way eBC is calculated due to the variability of MAC. NeBC and EC decreasing trends were consistent at sites with no significant trend in experimental MAC. Conversely, where MAC showed s.s. trend, the NeBC and EC trends were not consistent while ReBC concentration followed the same pattern as EC. These results underscore the importance of accounting for MAC variations when deriving eBC measurements from FAPs and emphasize the necessity of incorporating EC observations to constrain the uncertainty associated with eBC.

Constructing transferable and interpretable machine learning models for black carbon concentrations.

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.

The variability of mass concentrations and source apportionment analysis of equivalent black carbon across urban Europe.

This study analyzed the variability of equivalent black carbon (eBC) mass concentrations and their sources in urban Europe to provide insights into the use of eBC as an advanced air quality (AQ) parameter for AQ standards. This study compiled eBC mass concentration datasets covering the period between 2006 and 2022 from 50 measurement stations, including 23 urban background (UB), 18 traffic (TR), 7 suburban (SUB), and 2 regional background (RB) sites. The results highlighted the need for the harmonization of eBC measurements to allow for direct comparisons between eBC mass concentrations measured across urban Europe. The eBC mass concentrations exhibited a decreasing trend as follows: TR > UB > SUB > RB. Furthermore, a clear decreasing trend in eBC concentrations was observed in the UB sites moving from Southern to Northern Europe. The eBC mass concentrations exhibited significant spatiotemporal heterogeneity, including marked differences in eBC mass concentration and variable contributions of pollution sources to bulk eBC between different cities. Seasonal patterns in eBC concentrations were also evident, with higher winter concentrations observed in a large proportion of cities, especially at UB and SUB sites. The contribution of eBC from fossil fuel combustion, mostly traffic (eBCT) was higher than that of residential and commercial sources (eBCRC) in all European sites studied. Nevertheless, eBCRC still had a substantial contribution to total eBC mass concentrations at a majority of the sites. eBC trend analysis revealed decreasing trends for eBCT over the last decade, while eBCRC remained relatively constant or even increased slightly in some cities.

European aerosol phenomenology - 8: Harmonised source apportionment of organic aerosol using 22 Year-long ACSM/AMS datasets.

Organic aerosol (OA) is a key component of total submicron particulate matter (PM1), and comprehensive knowledge of OA sources across Europe is crucial to mitigate PM1 levels. Europe has a well-established air quality research infrastructure from which yearlong datasets using 21 aerosol chemical speciation monitors (ACSMs) and 1 aerosol mass spectrometer (AMS) were gathered during 2013-2019. It includes 9 non-urban and 13 urban sites. This study developed a state-of-the-art source apportionment protocol to analyse long-term OA mass spectrum data by applying the most advanced source apportionment strategies (i.e., rolling PMF, ME-2, and bootstrap). This harmonised protocol was followed strictly for all 22 datasets, making the source apportionment results more comparable. In addition, it enables quantification of the most common OA components such as hydrocarbon-like OA (HOA), biomass burning OA (BBOA), cooking-like OA (COA), more oxidised-oxygenated OA (MO-OOA), and less oxidised-oxygenated OA (LO-OOA). Other components such as coal combustion OA (CCOA), solid fuel OA (SFOA: mainly mixture of coal and peat combustion), cigarette smoke OA (CSOA), sea salt (mostly inorganic but part of the OA mass spectrum), coffee OA, and ship industry OA could also be separated at a few specific sites. Oxygenated OA (OOA) components make up most of the submicron OA mass (average = 71.1%, range from 43.7 to 100%). Solid fuel combustion-related OA components (i.e., BBOA, CCOA, and SFOA) are still considerable with in total 16.0% yearly contribution to the OA, yet mainly during winter months (21.4%). Overall, this comprehensive protocol works effectively across all sites governed by different sources and generates robust and consistent source apportionment results. Our work presents a comprehensive overview of OA sources in Europe with a unique combination of high time resolution (30-240 min) and long-term data coverage (9-36 months), providing essential information to improve/validate air quality, health impact, and climate models.

Association between traffic-related air pollution in schools and cognitive development in primary school children: a prospective cohort study.

BACKGROUND: Air pollution is a suspected developmental neurotoxicant. Many schools are located in close proximity to busy roads, and traffic air pollution peaks when children are at school. We aimed to assess whether exposure of children in primary school to traffic-related air pollutants is associated with impaired cognitive development. METHODS AND FINDINGS: We conducted a prospective study of children (n = 2,715, aged 7 to 10 y) from 39 schools in Barcelona (Catalonia, Spain) exposed to high and low traffic-related air pollution, paired by school socioeconomic index; children were tested four times (i.e., to assess the 12-mo developmental trajectories) via computerized tests (n = 10,112). Chronic traffic air pollution (elemental carbon [EC], nitrogen dioxide [NO2], and ultrafine particle number [UFP; 10-700 nm]) was measured twice during 1-wk campaigns both in the courtyard (outdoor) and inside the classroom (indoor) simultaneously in each school pair. Cognitive development was assessed with the n-back and the attentional network tests, in particular, working memory (two-back detectability), superior working memory (three-back detectability), and inattentiveness (hit reaction time standard error). Linear mixed effects models were adjusted for age, sex, maternal education, socioeconomic status, and air pollution exposure at home. Children from highly polluted schools had a smaller growth in cognitive development than children from the paired lowly polluted schools, both in crude and adjusted models (e.g., 7.4% [95% CI 5.6%-8.8%] versus 11.5% [95% CI 8.9%-12.5%] improvement in working memory, p = 0.0024). Cogently, children attending schools with higher levels of EC, NO2, and UFP both indoors and outdoors experienced substantially smaller growth in all the cognitive measurements; for example, a change from the first to the fourth quartile in indoor EC reduced the gain in working memory by 13.0% (95% CI 4.2%-23.1%). Residual confounding for social class could not be discarded completely; however, the associations remained in stratified analyses (e.g., for type of school or high-/low-polluted area) and after additional adjustments (e.g., for commuting, educational quality, or smoking at home), contradicting a potential residual confounding explanation. CONCLUSIONS: Children attending schools with higher traffic-related air pollution had a smaller improvement in cognitive development.

A multidisciplinary approach to characterise exposure risk and toxicological effects of PM10 and PM2.5 samples in urban environments.

Urban aerosol samples collected in Barcelona between 2008 and 2009 were toxicologically characterised by means of two complementary methodologies allowing evaluation of their Reactive Oxidative Stress (ROS)-generating capacity: the plasmid scission assay (PSA) and the dichlorodihydrofluorescin assay (DCFH). The PSA determined the PM dose able to damage 50% of a plasmid DNA molecule (TD(50) values), an indication of the ability of the sample to exert potential oxidative stress, most likely by formation of ·OH. This toxicity indicator did not show dependency on different air mass origins (African dust, Atlantic advection), indicating that local pollutant sources within or near the city are most likely to be mainly responsible for PM health effect variations. The average TD(50) values show PM(2.5-0.1) samples to be more toxic than the PM(10-2.5) fraction, with doses similar to those reported in previous studies in polluted urban areas. In addition, the samples were also evaluated using the oxidant-sensitive probe DCFH confirming the positive association between the amount of DNA damage and the generation of reactive oxidant species capable of inducing DNA strand break. Results provided by the PSA were compared with those from two other different methodologies to evaluate human health risk: (1) the toxicity of particulate PAHs expressed as the calculated toxicity equivalent of benzo[a]pyrene (BaPteq) after application of the EPA toxicity factors, and (2) the cancer risk assessment of the different PM sources detected in Barcelona with the receptor model Positive Matrix Factorisation (PMF) and the computer programme Multilinear Engine 2 (ME-2) using the organic and inorganic chemical compositions of particles. No positive associations were found between PSA and the toxicity of PAHs, probably due to the inefficiency of water in extracting organic compounds. On the other hand, the sum of cancer risk estimates calculated for each of the selected days for the PSA was found to correlate with TD(50) values in the fine fraction, with fuel oil combustion and industrial emissions therefore being most implicated in negative health effects. Further studies are necessary to determine whether toxicity is related to PM chemical composition and sources, or rather to its size distribution.

Variations in vanadium, nickel and lanthanoid element concentrations in urban air.

The emission of trace metal pollutants by industry and transport takes place on a scale large enough to alter atmospheric chemistry and results in measurable differences between the urban background of inhalable particulate matter (PM) in different towns. This is particularly well demonstrated by the technogenic release into the atmosphere of V, Ni, and lanthanoid elements. We compare PM concentrations of these metals in large datasets from five industrial towns in Spain variously influenced by emissions from refinery, power station, shipping, stainless steel, ceramic tiles and brick-making. Increased La/Ce values in urban background inhalable PM, due to La-contamination from refineries and their residual products (fuel oils and petcoke), contrast with Ce-rich emissions from the ceramic related industry, and clearly demonstrate the value of this ratio as a sensitive and reliable tracer for many point source emissions. Similarly, anomalously high V/Ni values (>4) can detect the influence of nearby high-V petcoke and fuel oil combustion, although the use of this ratio in urban background PM is limited by overlapping values in natural and anthropogenic materials. Geochemical characterisation of urban background PM is a valuable compliment to the physical monitoring of aerosols widely employed in urban areas, especially given the relevance of trace metal inhalation to urban health issues.