Quantum effects in muon spin spectroscopy within the stochastic self-consistent harmonic approximation

In muon spin rotation experiments the positive implanted muon vibrates with large zero point amplitude by virtue of its light mass. Quantum mechanical calculations of the host material usually treat the muon as a point impurity, ignoring this large vibrational amplitude. As a first order correction, the muon zero point motion is usually described within the harmonic approximation, despite the large anharmonicity of the crystal potential. Here we apply the stochastic self-consistent harmonic approximation, a quantum variational method devised to include strong anharmonic effects in total energy and vibrational frequency calculations, in order to overcome these limitations and provide an accurate ab initio description of the quantum nature of the muon. We applied this full quantum treatment to the calculation of the muon contact hyperfine field in textbook-case metallic systems, such as Fe, Ni, Co including MnSi and MnGe, significantly improving agreement with experiments. Our results show that muon vibrational frequencies are strongly renormalized by anharmonicity. Finally, in contrast to the harmonic approximation, we show that including quantum anharmonic fluctuations, the muon stabilizes at the octahedral site in bcc Fe.Comment: 10 page

Strongly lensed supernovae as a self-sufficient probe of the distance duality relation

Theoretical Physic

Multiannual assessment of the desert dust impact on air quality in Italy combining PM10 data with physics-based and geostatistical models

Desert dust storms pose real threats to air quality and health of millions of people in source regions, with associated impacts extending to downwind areas. Europe (EU) is frequently affected by atmospheric transport of desert dust from the Northern Africa and Middle East drylands. This investigation aims at quantifying the role of desert dust transport events on air quality (AQ) over Italy, which is among the EU countries most impacted by this phenomenon. We focus on the particulate matter (PM) metrics regulated by the EU AQ Directive. In particular, we use multiannual (2006–2012) PM10 records collected in hundreds monitoring sites within the national AQ network to quantify daily and annual contributions of dust during transport episodes. The methodology followed was built on specific European Commission guidelines released to evaluate the natural contributions to the measured PM-levels, and was partially modified, tested and adapted to the Italian case in a previous study. Overall, we show that impact of dust on the yearly average PM10 has a clear latitudinal gradient (from less than 1 to greater than 10 µg/m3 going from north to south Italy), this feature being mainly driven by an increased number of dust episodes per year with decreasing latitude. Conversely, the daily-average dust-PM10 (≅12 µg/m3) is more homogenous over the country and shown to be mainly influenced by the site type, with enhanced values in more urbanized locations. This study also combines the PM10 measurements-approach with geostatistical modelling. In particular, exploiting the dust-PM10 dataset obtained at site- and daily-resolution over Italy, a geostatistical, random-forest model was set up to derive a daily, spatially-continuous field of desert-dust PM10 at high (1-km) resolution. This finely resolved information represent the basis for a follow up investigation of both acute and chronic health effects of desert dust over Italy, stemming from daily and annual exposures, respectively.This work was performed as an ‘After-LIFE’ activity of the EU LIFE+2010 DIAPASON project (LIFE+10 ENV/IT/391) and is a contributing activity to the COST Action InDust (CA16202) and to the EU ERA4CS project DustClim (Grant n. 690462). We thank the Barcelona Supercomputing Center (BSC, http://www.bsc.es/earth-sciences/mineral-dust-forecast-system/) for maintaining the BSC-DREAM8b daily dust forecasts used in this study. S. Basart acknowledges AXA Research Fund for supporting the long-term mineral dust research at the Earth Sciences Department at BSC. N. Alvan Romero carried out this research during an internship at ISAC-CNR under the supervision of F. Barnaba. Constructive comments by Jorge Pey and two other anonymous Reviewers are also gratefully acknowledged.Peer ReviewedPostprint (published version

Perfluorinated compounds (pfcs) in river waters of central Italy. Monthly variation and ecological risk assessment (era)

Perfluorinated compounds (PFCs) are a wide class of emerging pollutants. In this study, we applied the US EPA method 533 for the determination of 21 PFCs in river water samples. In particular, this method was used to investigate the presence of the target PFCs in six rivers in central Italy during a 4-month-long monitoring campaign. In 73% of the analyzed samples, at least some of the target PFCs were detected at concentrations higher than the limit of detection (LOD). The sum of the 21 target analytes ( n-ary sumation 21PFCs) ranged from 4.3 to 68.5 ng L-1, with the highest concentrations measured in the month of June, probably due to a minor river streamflow occurring in the warmer summer months. Considering the individual congeners, PFBA and PFPeA, followed by PFHxA and PFOA, were the predominantly detected compounds. Short- and medium-chain PFCs (C4-C9) prevail over the long-chain PFCs (C10-C18), likely due to the increased industrial use and the higher solubility of short-chain PFCs compared to long-chain PFCs. The ecological risk assessment, conducted by using the risk quotient method, highlighted that the risk for aquatic environments associated with PFBA, PFPeA, PFBS, PFHxA and PFOA was low or negligible. Only for PFOA, there was a medium level of risk in two rivers in the month of June. With regard to PFOS, 54% of the river water samples were classified as "high risk" for the aquatic environment. The remaining 46% of the samples were classified as "medium risk.

Magnetic properties of spin diluted iron pnictides from muSR and NMR in LaFe1-xRuxAsO

The effect of isoelectronic substitutions on the microscopic properties of LaFe1-xRuxAsO, for 0< x< 0.8, has been investigated by means of muSR and 139La NMR. It was found that Ru substitution causes a progressive reduction of the N\`eel temperature (T_N) and of the magnetic order parameter without leading to the onset of superconductivity. The temperature dependence of 139La nuclear spin-lattice relaxation rate 1/T_1 can be suitably described within a two-band model. One band giving rise to the spin density wave ground-state, while the other one is characterized by weakly correlated electrons. Fe for Ru substitution yields to a progressive decrease of the density of states at the Fermi level close to the one derived from band structure calculations. The reduction of T_N with doping follows the predictions of the J_1-J_2 model on a square lattice, which appears to be an effective framework to describe the magnetic properties of the spin density wave ground-state.Comment: 6 pages, 8 figure

A multi-city air pollution population exposure study: Combined use of chemical-transport and random-Forest models with dynamic population data

Abstract Cities are severely affected by air pollution. Local emissions and urban structures can produce large spatial heterogeneities. We aim to improve the estimation of NO2, O3, PM2.5 and PM10 concentrations in 6 Italian metropolitan areas, using chemical-transport and machine learning models, and to assess the effect on population exposure by using information on urban population mobility. Three years (2013–2015) of simulations were performed by the Chemical-Transport Model (CTM) FARM, at 1 km resolution, fed by boundary conditions provided by national-scale simulations, local emission inventories and meteorological fields. A downscaling of daily air pollutants at higher resolution (200 m) was then carried out by means of a machine learning Random-Forest (RF) model, considering CTM and spatial-temporal predictors, such as population, land-use, surface greenness and vehicular traffic, as input. RF achieved mean cross-validation (CV) R2 of 0.59, 0.72, 0.76 and 0.75 for NO2, PM10, PM2.5 and O3, respectively, improving results from CTM alone. Mean concentration fields exhibited clear geographical gradients caused by climate conditions, local emission sources and photochemical processes. Time series of population weighted exposure (PWE) were estimated for two months of the year 2015 and for five cities, by combining population mobility data (derived from mobile phone traffic volumes data), and concentration levels from the RF model. PWE_RF metric better approximated the observed concentrations compared with the predictions from either CTM alone or CTM and RF combined, especially for pollutants exhibiting strong spatial gradients, such as NO2. 50% of the population was estimated to be exposed to NO2 concentrations between 12 and 38 μg/m3 and PM10 between 20 and 35 μg/m3. This work supports the potential of machine learning methods in predicting air pollutant levels in urban areas at high spatial and temporal resolutions

Biases in spatial bisection induced by viewing male and female faces

Research on visual attention triggered by face gender is still relatively sparse. In the present study, three experiments are reported in which male and female participants were required to estimate the midpoint of a line (i.e., the “line bisection task”): at each end of the line a face was presented. Depending on the experimental condition, faces could be of the same gender (i.e., two males or two females) or the opposite gender. Experiment 1 and 2 findings converged in showing that when a male face was presented at the right and a female face at the left endpoint of the line, a clear rightward bias emerged compared to the other experimental conditions, indicating that male faces captured attention more than female faces. Importantly, male faces used across Experiment 1 and 2 were rated as more threatening than female faces, suggesting that perceived level of threat may have been responsible for the observed bias toward the male face. Experiment 3 corroborated this hypothesis by finding an attentional bias toward the male face with high threat (angry) faces but not with low threat (smiling) faces

Short-term health effects from outdoor exposure to biomass burning emissions: A review

Biomass burning (BB) including forest, bush, prescribed fires, agricultural fires, residential wood combustion, and power generation has long been known to affect climate, air quality and human health. With this work we supply a systematic review on the health effects of BB emissions in the framework of the WHO activities on air pollution. We performed a literature search of online databases (PubMed, ISI, and Scopus) from year 1980 up to 2020. A total of 81 papers were considered as relevant for mortality and morbidity effects. High risk of bias was related with poor estimation of BB exposure and lack of adjustment for important confounders. PM10 and PM2.5 concentrations originating from BB were associated with all-cause mortality: the meta-analytical estimate was equal to 1.31% (95% CI 0.71, 1.71) and 1.92% (95% CI -1.19, 5.03) increased mortality per each 10 μg m-3 increase of PM10 and PM2.5, respectively. Regarding cardiovascular mortality 8 studies reported quantitative estimates. For smoky days and for each 10 μg m-3 increase in PM2.5 concentrations, the risk of cardiovascular mortality increased by 4.45% (95% CI 0.96, 7.95) and by 3.30% (95% CI -1.97, 8.57), respectively. Fourteen studies evaluated whether respiratory morbidity was adversely related to PM2.5 (9 studies) or PM10 (5 studies) originating from BB. All found positive associations. The pooled effect estimates were 4.10% (95% CI 2.86, 5.34) and 4.83% (95% CI 0.06, 9.60) increased risk of total respiratory admissions/emergency visits, per 10 μg m-3 increases in PM2.5 and PM10, respectively. Regarding cardiovascular morbidity, sixteen studies evaluated whether this was adversely related to PM2.5 (10 studies) or PM10 (6 studies) originating from BB. They found both positive and negative results, with summary estimates equal to 3.68% (95% CI -1.73, 9.09) and 0.93% (95% CI -0.18, 2.05) increased risk of total cardiovascular admissions/emergency visits, per 10 μg m-3 increases in PM2.5 and PM10, respectively. To conclude, a significant number of studies indicate that BB exposure is associated with all-cause and cardiovascular mortality and respiratory morbidity.The systematic review was funded by WHO as part of a Grant Agreement with Ministry of Foreign Affairs, Norway. Angeliki Karanasiou acknowledges support received through the Ramón y Cajal program (grant RYC-2014-16885) of the Spanish Ministry of Science, Innovation and Universities. This work was supported by the Spanish Ministry of Science and Innovation (Excelencia Severo Ochoa, Project CEX2018-000794-S).Peer reviewe

Estimation of daily PM10 and PM2.5 concentrations in Italy, 2013-2015, using a spatiotemporal land-use random-forest model.

Particulate matter (PM) air pollution is one of the major causes of death worldwide, with demonstrated adverse effects from both short-term and long-term exposure. Most of the epidemiological studies have been conducted in cities because of the lack of reliable spatiotemporal estimates of particles exposure in nonurban settings. The objective of this study is to estimate daily PM10 (PM?<?10??m), fine (PM?<?2.5??m, PM2.5) and coarse particles (PM between 2.5 and 10??m, PM2.5-10) at 1-km2 grid for 2013-2015 using a machine learning approach, the Random Forest (RF). Separate RF models were defined to: predict PM2.5 and PM2.5-10 concentrations in monitors where only PM10 data were available (stage 1); impute missing satellite Aerosol Optical Depth (AOD) data using estimates from atmospheric ensemble models (stage 2); establish a relationship between measured PM and satellite, land use and meteorological parameters (stage 3); predict stage 3 model over each 1-km2 grid cell of Italy (stage 4); and improve stage 3 predictions by using small-scale predictors computed at the monitor locations or within a small buffer (stage 5). Our models were able to capture most of PM variability, with mean cross-validation (CV) R2 of 0.75 and 0.80 (stage 3) and 0.84 and 0.86 (stage 5) for PM10 and PM2.5, respectively. Model fitting was less optimal for PM2.5-10, in summer months and in southern Italy. Finally, predictions were equally good in capturing annual and daily PM variability, therefore they can be used as reliable exposure estimates for investigating long-term and short-term health effects