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Radiocarbon measurements on the carbonaceous aerosol fractions are an effective tool for aerosol source apportionment. For these measurements, a new sample preparation line (MISSMARPLE: MIlan Small-SaMple Automated Radiocarbon Preparation LinE for atmospheric aerosol) was built in Milan (Italy). MISSMARPLE can separate different carbon fractions (i.e. total carbon (TC), elemental carbon (EC)), automates the sample combustion processes and the CO2 isolation in the "combustion line", and was designed to handle small samples, of about 50 μg carbon. The CO2 obtained in the combustion line is then reduced to graphite in the graphitization line for subsequent accelerator mass spectrometry (AMS) analysis at the INFN-LABEC in Sesto Fiorentino (Italy). MISSMARPLE was tested for reproducibility of 14C/12C ratio in primary standard samples, for background contamination by the analysis of blank samples (graphite with zero percent Modern Carbon (pMC)), and for accuracy by the analysis of IAEA-C7 for pMC(TC) and NIST RM8785 for pMC(EC) used as secondary standards. Measurements were carried out in different AMS runs. Reproducibility of 14C/12C was within 1.2%; blank values were down to 2.2 ± 0.2 pMC in the latest AMS run, and both IAEA-C7 and NIST RM8785 measurements were within 1σ with the reference value (but for one IAEA-C7 sample within 2.3σ). These results point to MISSMARPLE as a new, valuable tool for aerosol sample preparation for radiocarbon measurements to be exploited not only on traditional 24-h samples but also when small carbon quantities are available (e.g. collected at remote sites or with high temporal resolution). [ABSTRACT FROM AUTHOR]

14 C measurements on the carbonaceous fractions of atmospheric aerosol are an important tool for source apportionment. In this paper, a 14 C-based source apportionment study was carried out on samples collected during winter 2021 at an urban background site in the Po Valley, one of the main pollution hot-spot areas in Europe. The samples were prepared using MISSMARPLE (MIlan Small-SaMple Automated Radiocarbon Preparation LinE for atmospheric aerosol), a recently developed sample preparation line for 14 C measurements on atmospheric aerosol carbon fractions, specifically targeting small samples (about 50 μgC). 14 C-based source apportionment was performed separately for elemental carbon (EC) and organic carbon (OC), after suitable optimization of model parameters. As this is the first source apportionment study using MISSMARPLE-prepared samples, our results were compared with other tracers and source apportionment outcomes obtained through parallel methodologies, demonstrating strong correlations in all tests (R > 0.87). The source apportionment results showed that fossil fuel combustion remains the main source of EC in the investigated area (60%), while modern contributions generally dominate the OC fraction (66%). However, some episodes were identified where fossil fuel combustion was the dominant contributor also to the OC fraction (up to 66%). During these episodes, the importance of secondary OC formation from fossil fuel combustion was highlighted through the EC-tracer approach. Thus, fossil fuel combustion is still a significant source of carbonaceous aerosol in the Po Valley during the winter. Both primary emissions and gaseous precursors must be targeted by future abatement policies to effectively reduce pollution in the area., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Particulate Matter (PM) is a complex and heterogeneous mixture of atmospheric particles recognized as a threat to human health. Oxidative Potential (OP) measurement is a promising and integrative method for estimating PM-induced health impacts since it is recognized as more closely associated with adverse health effects than ordinarily used PM mass concentrations. OP measurements could be introduced in the air quality monitoring, along with the parameters currently evaluated. PM deposition in the lungs induces oxidative stress, inflammation, and DNA damage. The study aimed to compare the OP measurements with toxicological effects on BEAS-2B and THP-1 cells of winter and summer PM 1 collected in the Po Valley (Italy) during 2021. PM 1 was extracted in deionized water by mechanical agitation and tested for OP and, in parallel, used to treat cells. Cytotoxicity, genotoxicity, oxidative stress, and inflammatory responses were assessed by MTT test, DCFH-DA assay, micronucleus, γ-H2AX, comet assay modified with endonucleases, ELISA, and Real-Time PCR. The evaluation of OP was performed by applying three different assays: dithiothreitol (OP DTT ), ascorbic acid (OP AA ), and 2',7'-dichlorofluorescein (OP DCFH ), in addition, the reducing potential was also analysed (RP DPPH ). Seasonal differences were detected in all the parameters investigated. The amount of DNA damage detected with the Comet assay and ROS formation highlights the presence of oxidative damage both in winter and in summer samples, while DNA damage (micronucleus) and genes regulation were mainly detected in winter samples. A positive correlation with OP DCFH (Spearman's analysis, p < 0.05) was detected for IL-8 secretion and γ-H2AX. These results provide a biological support to the implementation in air quality monitoring of OP measurements as a useful proxy to estimate PM-induced cellular toxicological responses. In addition, these results provide new insights for the assessment of the ability of secondary aerosol in the background atmosphere to induce oxidative stress and health effects., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Urban textures of the Italian cities are peculiarly shaped by the local geography generating similarities among cities placed in different regions but comparable topographical districts. This suggested the following scientific question: can different topographies generate significant differences on the PM 10 chemical composition at Italian urban sites that share similar geography despite being in different regions? To investigate whether such communalities can be found and are applicable at Country-scale, we propose here a novel methodological approach. A dataset comprising season-averages of PM 10 mass concentration and chemical composition data was built, covering the decade 2005-2016 and referring to urban sites only (21 cities). Statistical analyses, estimation of missing data, identification of latent clusters and source apportionment modeling by Positive Matrix Factorization (PMF) were performed on this unique dataset. The first original result is the demonstration that a dataset with atypical time resolution can be successfully exploited as an input matrix for PMF obtaining Country-scale representative chemical profiles, whose physical consistency has been assessed by different tests of modeling performance. Secondly, this dataset can be considered a reference repository of season averages of chemical species over the Italian territory and the chemical profiles obtained by PMF for urban Italian agglomerations could contribute to emission repositories. These findings indicate that our approach is powerful, and it could be further employed with datasets typically available in the air pollution monitoring networks., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

This paper presents a source apportionment study performed on a dataset collected at a trafficked site in Coimbra (Portugal) during the period December 2018-June 2019. The novelty of this work consists in the methodological approach used and the sensitivity study carried out to give hints to potential future applications. Indeed, a multi-time resolution and multi-parameter study was performed joining together aerosol data from 24-h chemically characterized samples and high-time resolution multi-wavelength absorption coefficients retrieved by an Aethalometer. A detailed sensitivity study on the most suitable combination of time resolution and uncertainties was carried out to obtain reliable physical and stable solutions over all analyses. In parallel, a regular EPA-PMF source apportionment study using chemical and optical variables averaged on 24 h is presented and discussed in comparison to the more complex multi-time and multi-parameter approach. Apart from results pertaining to the identification and relevance of different sources in Coimbra, the methodological results shown here can give guidance for readers who want to implement optical variables jointly with chemical ones in the same model run., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Background: COVID-19 containment measures curb viral spread but may hamper walking mobility. As a low daily step count is associated with increased noncommunicable diseases and mortality, assessing the relationship between pandemic responses and walking mobility can help trade-off public health measures. We investigated the association between containment stringency and walking mobility across 60 countries in the period between January 21, 2020 and January 21, 2022 and modeled how this could impact mortality hazard., Methods: Walking mobility was measured through the Apple Mobility Trends, containment measures stringency index through the Oxford COVID-19 response tracker (which considers local policies on closures, healthcare, and economy), and meteorological data by National Oceanic and Atmospheric Administration weather stations. Walking mobility was regressed over stringency in a mixed-effect model with weather variables as covariates. The impact of stringency on all-cause mortality due to reduced mobility was modeled based on regression results, prepandemic walking mobility, and the association between step count and all-cause mortality hazard., Results: Across the 60 countries, the average stringency was 55 (9) (mean [SD]) out of 100. Stringency was negatively associated with walking mobility; a log-linear model fitted data better than a linear one, with a regression coefficient for stringency on ln (walking mobility) (95% confidence interval) of  -1.201 × 10-2 (-1.221 × 10-2 to -1.183 × 10-2). Increasing stringency, thus decreasing walking mobility, nonlinearly incremented the modeled all-cause mortality hazard by up to ∼40%., Conclusions: In this study, walking mobility was negatively associated with containment measures stringency; the relationship between stringency, mobility, and the subsequent impact on health outcomes may be nonlinear. These findings can help in balancing pandemic containment policies.