Particle number and mass exposure concentrations by commuter transport modes in Milan, Italy

There is increasing awareness amongst the general public about exposure to atmospheric pollution while travelling in urban areas especially when taking active travelling modes such as walking and cycling. This study presents a comparative investigation of ultrafine particles (UFP), PM10, PM2.5, PM1 exposure levels associated with four transport modes (i.e., walking, cycling, car, and subway) in the city of Milan measured by means of portable instruments. Significant differences in particle exposure between transport modes were found. The subway mode was characterized by the highest PM mass concentrations: PM10, PM2.5, PM1 subway levels were respectively about 2-4-3 times higher than those of the car and open air active modes (i.e. cycling and walking). Conversely, these latter modes displayed the highest UFP levels about 2 to 3 times higher than the subway and car modes, highlighting the influence of direct traffic emissions. The car mode (closed windows, air conditioning and air recirculation on) reported the lowest PM and UFP concentration levels. In particular, the open-air/car average concentration ratio varied from about 2 for UFP up to 4 for PM1 and 6 for PM10 and PM2.5, showing differences that increase with increasing particle size. This work points out that active mode travelling in Milan city centre in summertime results in higher exposure levels than the car mode. Walkers’ and cyclists’ exposure levels is expected to be even higher during wintertime, due to the higher ambient PM and UFP concentration. Interventions intended to re-design the urban mobility should therefore include dedicated routes in order to limit their exposure to PM and UFP by increasing their distance from road traffic

Fingerprints of the urban particle number size distribution in Helsinki, Finland : Local versus regional characteristics

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The effect of local sources on aerosol particle number size distribution, concentrations and fluxes in Helsinki, Finland

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An explainable model of host genetic interactions linked to COVID-19 severity

We employed a multifaceted computational strategy to identify the genetic factors contributing to increased risk of severe COVID-19 infection from a Whole Exome Sequencing (WES) dataset of a cohort of 2000 Italian patients. We coupled a stratified k-fold screening, to rank variants more associated with severity, with the training of multiple supervised classifiers, to predict severity based on screened features. Feature importance analysis from tree-based models allowed us to identify 16 variants with the highest support which, together with age and gender covariates, were found to be most predictive of COVID-19 severity. When tested on a follow-up cohort, our ensemble of models predicted severity with high accuracy (ACC = 81.88%; AUCROC = 96%; MCC = 61.55%). Our model recapitulated a vast literature of emerging molecular mechanisms and genetic factors linked to COVID-19 response and extends previous landmark Genome-Wide Association Studies (GWAS). It revealed a network of interplaying genetic signatures converging on established immune system and inflammatory processes linked to viral infection response. It also identified additional processes cross-talking with immune pathways, such as GPCR signaling, which might offer additional opportunities for therapeutic intervention and patient stratification. Publicly available PheWAS datasets revealed that several variants were significantly associated with phenotypic traits such as "Respiratory or thoracic disease", supporting their link with COVID-19 severity outcome.A multifaceted computational strategy identifies 16 genetic variants contributing to increased risk of severe COVID-19 infection from a Whole Exome Sequencing dataset of a cohort of Italian patients

The polymorphism L412F in TLR3 inhibits autophagy and is a marker of severe COVID-19 in males

The polymorphism L412F in TLR3 has been associated with several infectious diseases. However, the mechanism underlying this association is still unexplored. Here, we show that the L412F polymorphism in TLR3 is a marker of severity in COVID-19. This association increases in the sub-cohort of males. Impaired macroautophagy/autophagy and reduced TNF/TNFα production was demonstrated in HEK293 cells transfected with TLR3L412F-encoding plasmid and stimulated with specific agonist poly(I:C). A statistically significant reduced survival at 28 days was shown in L412F COVID-19 patients treated with the autophagy-inhibitor hydroxychloroquine (p = 0.038). An increased frequency of autoimmune disorders such as co-morbidity was found in L412F COVID-19 males with specific class II HLA haplotypes prone to autoantigen presentation. Our analyses indicate that L412F polymorphism makes males at risk of severe COVID-19 and provides a rationale for reinterpreting clinical trials considering autophagy pathways. Abbreviations: AP: autophagosome; AUC: area under the curve; BafA1: bafilomycin A1; COVID-19: coronavirus disease-2019; HCQ: hydroxychloroquine; RAP: rapamycin; ROC: receiver operating characteristic; SARS-CoV-2: severe acute respiratory syndrome coronavirus 2; TLR: toll like receptor; TNF/TNF-α: tumor necrosis factor

Variability of Black Carbon and Ultrafine Particle Concentration on Urban Bike Routes in a Mid-Sized City in the Po Valley (Northern Italy)

Cyclists might experience increased air pollution exposure, due to the proximity to traffic, and higher intake, due to their active travel mode and higher ventilation. Several local factors, like meteorology, road and traffic features, and bike lanes features, affect cyclists’ exposure to traffic-related pollutants. This paper investigates the concentration levels and the effect of the features of the bike lanes on cyclists’ exposure to airborne ultrafine particulate matter (UFP) and black carbon (BC) in the mid-sized city of Piacenza, located in the middle of the Po Valley, Northern Italy. Monitoring campaigns were performed by means of portable instruments along different urban bike routes with bike lanes, characterized by different distances from the traffic source (on-road cycle lane, separated cycle lane, green cycle path), during morning (9:00 am–10:00 am) and evening (17:30 pm–18:30 pm) workday rush hours in both cold and warm seasons. The proximity to traffic significantly affected cyclists’ exposure to UFP and BC: exposure concentrations measured for the separated lane and for the green path were 1–2 times and 2–4 times lower than for the on-road lane. Concurrent measurements showed that exposure concentrations to PM10, PM2.5, and PM1 were not influenced by traffic proximity, without any significant variation between on-road cycle lane, separated lane, or green cycle path. Thus, for the location of this study PM mass-based metrics were not able to capture local scale concentration gradients in the urban area as a consequence of the rather high urban and regional background that hides the contribution of local scale sources, such as road traffic. The impact of route choice on cyclists’ exposure to UFPs and BC during commuting trips back and forth from a residential area to the train station has been also estimated through a probabilistic approach through an iterative Monte Carlo technique, based on the measured data. Compared to the best choice, a worst-route choice can result in an increased cumulative exposure up to about 50% for UFPs, without any relevant difference between cold and warm season, and from 20% in the cold season up to 90% in the warm season for equivalent black carbon concentration (EBC)

Uncertainty propagation methods in dioxin/furans emission estimation models

International audienceIn this paper we propose a comparison between two different approaches for uncertainty propa-gation in Environmental Impact Assessment (EIA) procedures. Both a purely Probabilistic (PMC) and a Hy-brid probabilistic-possibilistic Monte Carlo method (HMC) are applied on an estimation model of dio-xin/furans emission from a waste gasification plant. The analysis shows that when input variables affected by scarcity of information are present, HMC seems to be a valid alternative method that properly propagates un-certainty from data to output avoiding arbitrary and subjective assumptions on the input probability distribu-tion functions. HMC could improve the transparency of the EIA procedure with positive effects on the com-municability and credibility of its predictions