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In this study, the performance of two types of source apportionment models was evaluated by assessing the results provided by 40 different groups in the framework of an intercomparison organised by FAIRMODE WG3 (Forum for air quality modelling in Europe, Working Group 3). The evaluation was based on two performance indicators: z-scores and the root mean square error weighted by the reference uncertainty (RMSEu), with pre-established acceptability criteria. By involving models based on completely different and independent input data, such as receptor models (RMs) and chemical transport models (CTMs), the intercomparison provided a unique opportunity for their cross-validation. In addition, comparing the CTM chemical profiles with those measured directly at the source contributed to corroborate the consistency of the tested model results. The most commonly used RM was the US EPA- PMF version 5. RMs showed very good performance for the overall dataset (91% of z-scores accepted) while more difficulties were observed with the source contribution time series (72% of RMSEu accepted). Industrial activities proved to be the most difficult sources to be quantified by RMs, with high variability in the estimated contributions. In the CTMs, the sum of computed source contributions was lower than the measured gravimetric PM10 mass concentrations. The performance tests pointed out the differences between the two CTM approaches used for source apportionment in this study: brute force (or emission reduction impact) and tagged species methods. The sources meeting the z-score and RMSEu acceptability criteria tests were 50% and 86%, respectively. The CTM source contributions to PM10 were in the majority of cases lower than the RM averages for the corresponding source. The CTMs and RMs source contributions for the overall dataset were more comparable (83% of the z-scores accepted) than their time series (successful RMSEu in the range 25% - 34%). The comparability between CTMs and RMs varied depending on the source: traffic/exhaust and industry were the source categories with the best results in the RMSEu tests while the most critical ones were soil dust and road dust. The differences between RMs and CTMs source reconstructions confirmed the importance of cross validating the results of these two families of models. Keywords: Source apportionment, PM10, Receptor models, Chemical transport models, Intercomparison, Lens

This study aims to highlight the potentialities of an innovative methodological approach for monitoring and chemical characterization of odors, especially in high concern and complex industrial areas. The proposed approach was developed in order to monitor and identify odor-active compounds responsible for odor annoyance coming from different industrial activities such as landfills, wastewater treatment plants, and petroleum plants. The methodology’s strengths are as follows: (1) the tailored approach for each typology of industrial areas/sites; (2) integration of technologies able to provide real-time information about the emissive sources; (3) mapping of air pollutants on the territory aimed to identify and discriminate among different fugitive emissions responsible for odor annoyance; (4) collection of more representative air samples only during the nuisance events, thanks to the implementation of innovative sampling systems and citizens’ involvement; and (5) increased analytical sensitivity in odor-active VOCs detection. This methodology reveals to be a useful tool to collect real-time information about the emission sources and their impacts on the surrounding area giving credit to citizens’ complaints. Moreover, it allows to overcome the limitations of the conventional approaches related to the lack of instrumental sensitivity and to identify the chemical compounds contributing to the odor annoyance.

This study aims to investigate the air quality in primary school placed in district of Taranto (south of Italy), an area of high environmental risk because of closeness between large industrial complex and urban settlement. The chemical characterization of PM2.5 was performed to identify origin of pollutants detected inside school and the comparison between indoor and outdoor levels of PAHs and metals allowed evaluating intrusion of outdoor pollutants or the existence of specific indoor sources. The results showed that the indoor and outdoor levels of PM2.5, BaP, Cd, Ni, As, and Pb never exceeded the target values issued by World Health Organization (WHO). Nevertheless, high metals and PAHs concentrations were detected especially when school were downwind to the steel plant. The I/O ratio showed the impact of outdoor pollutants, especially of industrial markers as Fe, Mn, Zn, and Pb, on indoor air quality. This result was confirmed by values of diagnostic ratio as B(a)P/B(g)P, IP/(IP + BgP), BaP/Chry, and BaP/(BaP + Chry), which showed range characteristics of coke and coal combustion. However, Ni and As showed I/O ratio of 2.5 and 1.4, respectively, suggesting the presence of indoor sources.

Handball game passed by normative changes in 2016, and some influenced the use of the additional court player (ACP), both related to organizational, strategic, tactical, and technical issues. This study aimed to analyze, from the speeches of Brazilian handball coaches, the influence of ACP on handball training process after 2016. Seven coaches were interviewed, who have already been classified among the top four clubs in the Brazilian national men and women leagues and/or who participated in the technical staff of the Brazilian national teams (men and/or women). Data were thematically analyzed. The 2016 normative change raised new handball tactical landscapes, as the coaches developed changings on offensive and defensive actions using and playing against ACP.

A high time-resolved monitoring campaign of bulk deposition of PAHs and metals was conducted near the industrial area and at an urban background site in province of Taranto (Italy) in order to evaluate the impact of the biggest European steel plant. The deposition fluxes of the sum of detected PAHs at the industrial area ranged from 92 to 2432 ng m−2d−1. In particular the deposition fluxes of BaP, BaA, and BkF were, on average, 10, 14, and 8 times higher than those detected at the urban background site, respectively. The same finding was for metals. The deposition fluxes of Ni (19.8 µg m−2 d−1) and As (2.2 µg m−2 d−1) at the industrial site were about 5 times higher than those at the urban background site, while the deposition fluxes of Fe (57 mg m−2d−1) and Mn (1.02 mg m−2d−1) about 31 times higher. Precipitation and wind speed played an important role in PAH deposition fluxes. Fe and Mn fluxes at the industrial site resulted high when wind direction favored the transport of air masses from the steel plant to the receptor site. The impact of the industrial area was also confirmed by IP/(IP + BgP), IP/BgP, and BaP/BgP diagnostic ratios.

The atmosphere is a carrier on which some natural and anthropogenic organic and inorganic chemicals are transported, and the wet and dry deposition events are the most important processes that remove those chemicals, depositing it on soil and water. A wide variety of different collectors were tested to evaluate site-specificity, seasonality and daily variability of settleable particle concentrations. Deposition fluxes of POPs showed spatial and seasonal variations, diagnostic ratios of PAHs on deposited particles, allowed the discrimination between pyrolytic or petrogenic sources. Congener pattern analysis and bulk deposition fluxes in rural sites confirmed long-range atmospheric transport of PCDDs/Fs. More and more sophisticated and newly designed deposition samplers have being used for characterization of deposited mercury, demonstrating the importance of rain scavenging and the relatively higher magnitude of Hg deposition from Chinese anthropogenic sources. Recently biological monitors demonstrated that PAH concentrations in lichens were comparable with concentrations measured in a conventional active sampler in an outdoor environment. In this review the authors explore the methodological approaches used for the assessment of atmospheric deposition, from the analysis of the sampling methods, the analytical procedures for chemical characterization of pollutants and the main results from the scientific literature.

Recently, volatile organic compound (VOC) determination in exhaled breath has seen growing interest due to its promising potential in early diagnosis of several pathological conditions, including chronic kidney disease (CKD). Therefore, this study aimed to identify the breath VOC pattern providing an accurate, reproducible and fast CKD diagnosis at early stages of disease. A cross-sectional observational study was carried out, enrolling a total of 30 subjects matched for age and gender. More specifically, the breath samples were collected from (a) 10 patients with end-stage kidney disease (ESKD) before undergoing hemodialysis treatment (DIAL); (b) 10 patients with mild-moderate CKD (G) including 3 patients in stage G2 with mild albuminuria, and 7 patients in stage G3 and (c) 10 healthy controls (CTRL). For each volunteer, an end-tidal exhaled breath sample and an ambient air sample (AA) were collected at the same time on two sorbent tubes by an automated sampling system and analyzed by Thermal Desorption–Gas Chromatography–Mass Spectrometry. A total of 110 VOCs were detected in breath samples but only 42 showed significatively different levels with respect to AA. Nonparametric tests, such as Wilcoxon/Kruskal–Wallis tests, allowed us to identify the most weighting variables able to discriminate between AA, DIAL, G and CTRL breath samples. A promising multivariate data mining approach incorporating only selected variables (showing p-values lower than 0.05), such as nonanal, pentane, acetophenone, pentanone, undecane, butanedione, ethyl hexanol and benzene, was developed and cross-validated, providing a prediction accuracy equal to 87% and 100% in identifying patients with both mild–moderate CKD (G) and ESKD (DIAL), respectively.

Edible ice is often produced by special machines that can represent a source of significant chemical and microbiological contamination. In this work, the presence of phthalic acid esters (phthalates, PAEs) and heavy metals in ice cubes distributed by 77 vending machines installed in two different zones in southern Italy and fed by water from the public water supply was investigated. Solid-phase microextraction coupled to gas chromatography−mass spectrometry (SPME-GC/MS) was used to evaluate contamination with four PAEs, which were selected because they are commonly used in the production of food-contact plastics, while inductively coupled plasma mass spectrometry (ICP/MS) was used to quantify the heavy metals. It was found that ice samples, especially those from one of the two considered zones (zone 2), exceeded the dibutyl phthalate (DBP) threshold limit value; some ice cubes from the other zone (zone 1) instead showed levels of both lead (Pb) and nickel (Ni) up to one order of magnitude higher than those observed in samples collected in zone 2 and higher than the maximum permitted values (European Directive n. 2184/2020). Since the water source connected to the ice vending machines was found to be free from significant levels of all considered target compounds and metals, the high levels of DBP, Ni, and Pb in ice cubes could be attributed to the components and/or to the state of repair of the ice vending machines themselves.

The presented study protocol outlines a comprehensive investigation into the interplay among the human microbiota, volatilome, and disease biomarkers, with a specific focus on Behçet’s disease (BD) using methods based on explainable artificial intelligence. The protocol is structured in three phases. During the initial three-month clinical study, participants will be divided into control and experimental groups. The experimental groups will receive a soluble fiber-based dietary supplement alongside standard therapy. Data collection will encompass oral and fecal microbiota, breath samples, clinical characteristics, laboratory parameters, and dietary habits. The subsequent biological data analysis will involve gas chromatography, mass spectrometry, and metagenetic analysis to examine the volatilome and microbiota composition of salivary and fecal samples. Additionally, chemical characterization of breath samples will be performed. The third phase introduces Explainable Artificial Intelligence (XAI) for the analysis of the collected data. This novel approach aims to evaluate eubiosis and dysbiosis conditions, identify markers associated with BD, dietary habits, and the supplement. Primary objectives include establishing correlations between microbiota, volatilome, phenotypic BD characteristics, and identifying patient groups with shared features. The study aims to identify taxonomic units and metabolic markers predicting clinical outcomes, assess the supplement’s impact, and investigate the relationship between dietary habits and patient outcomes. This protocol contributes to understanding the microbiome’s role in health and disease and pioneers an XAI-driven approach for personalized BD management. With 70 recruited BD patients, XAI algorithms will analyze multi-modal clinical data, potentially revolutionizing BD management and paving the way for improved patient outcomes.

O uso do jogador de quadra adicional (JQA) no handebol sofreu implicações após mudanças regulamentares (2016). Os estudos envolvem a eficácia do JQA em competições, mas poucos esforços têm sido envidados para investigar motivos e expectativas dos treinadores para a sua utilização. Este estudo analisou as razões pelas quais os treinadores optam por utilizarem o JQA e as consequências em situações distintas do jogo. Sete treinadores de equipes adultas que qualificaram suas equipes entre as quatro melhores (Liga Nacional) foram entrevistados (entrevista semiestruturada). A análise temática reflexiva foi utilizada para a identificação dos temas e discussão. Os resultados revelaram dois subtemas: a utilização do JQA para produzir superioridade numérica ofensiva e as características e riscos do JQA para mitigar a inferioridade numérica. Comportamentos pertinentes ao jogo sem goleiro em superioridade numérica ou para aproximar a relação numérica foram percebidos e permitem refletir sobre avaliação e adaptação de modelos de jogo.

Recently, volatile organic compound (VOC) determination in exhaled breath has seen growing interest due to its promising potential in early diagnosis of several pathological conditions, including chronic kidney disease (CKD). Therefore, this study aimed to identify the breath VOC pattern providing an accurate, reproducible and fast CKD diagnosis at early stages of disease. A cross-sectional observational study was carried out, enrolling a total of 30 subjects matched for age and gender. More specifically, the breath samples were collected from (a) 10 patients with end-stage kidney disease (ESKD) before undergoing hemodialysis treatment (DIAL); (b) 10 patients with mild-moderate CKD (G) including 3 patients in stage G2 with mild albuminuria, and 7 patients in stage G3 and (c) 10 healthy controls (CTRL). For each volunteer, an end-tidal exhaled breath sample and an ambient air sample (AA) were collected at the same time on two sorbent tubes by an automated sampling system and analyzed by Thermal Desorption–Gas Chromatography–Mass Spectrometry. A total of 110 VOCs were detected in breath samples but only 42 showed significatively different levels with respect to AA. Nonparametric tests, such as Wilcoxon/Kruskal–Wallis tests, allowed us to identify the most weighting variables able to discriminate between AA, DIAL, G and CTRL breath samples. A promising multivariate data mining approach incorporating only selected variables (showing p-values lower than 0.05), such as nonanal, pentane, acetophenone, pentanone, undecane, butanedione, ethyl hexanol and benzene, was developed and cross-validated, providing a prediction accuracy equal to 87% and 100% in identifying patients with both mild–moderate CKD (G) and ESKD (DIAL), respectively. [ABSTRACT FROM AUTHOR]

Edible ice is often produced by special machines that can represent a source of significant chemical and microbiological contamination. In this work, the presence of phthalic acid esters (phthalates, PAEs) and heavy metals in ice cubes distributed by 77 vending machines installed in two different zones in southern Italy and fed by water from the public water supply was investigated. Solid-phase microextraction coupled to gas chromatography−mass spectrometry (SPME-GC/MS) was used to evaluate contamination with four PAEs, which were selected because they are commonly used in the production of food-contact plastics, while inductively coupled plasma mass spectrometry (ICP/MS) was used to quantify the heavy metals. It was found that ice samples, especially those from one of the two considered zones (zone 2), exceeded the dibutyl phthalate (DBP) threshold limit value; some ice cubes from the other zone (zone 1) instead showed levels of both lead (Pb) and nickel (Ni) up to one order of magnitude higher than those observed in samples collected in zone 2 and higher than the maximum permitted values (European Directive n. 2184/2020). Since the water source connected to the ice vending machines was found to be free from significant levels of all considered target compounds and metals, the high levels of DBP, Ni, and Pb in ice cubes could be attributed to the components and/or to the state of repair of the ice vending machines themselves. [ABSTRACT FROM AUTHOR]

El trabajo presenta propuestas que procuran atender el acuciante déficit habitacional a nivel nacional, así como las urgencias generadas por desastres socio-naturales, dando respuesta a las necesidades emergentes de los sectores más desprotegidos y vulnerables, a partir de explorar y formular soluciones habitacionales modulares sustentables de bajo costo, mediante la utilización de sistemas abiertos y adaptativos prefabricados/ industrializados con distintas materialidades. Se parte de la hipótesis que actuar para el hábitat de la emergencia, con un enfoque inclusivo, requiere desde lo proyectual imaginación en pos de lograr eficiencia y síntesis creativa superadora de lo existente. Se utilizó una estrategia exploratoria a partir de identificar referentes nacionales e internacionales, utilizando técnicas e instrumentos propios de la investigación proyectual. En este marco, se presentan dos experiencias, una para habitar temporalmente y otra un Sistema Incremental Modular (SIM), que tiende a dar respuesta a sectores vulnerables a partir de distintas configuraciones espaciales.

In this paper, a new protocol is described, based on solid phase microextraction (SPME) coupled with gas chromatography–mass spectrometry (GC-MS), to monitor ex vivo changes in endogenous volatile organic compounds (VOCs) released by surgically resected colonic tissues (normal colonic mucosa and adenomatous polyps) from seven patients undergoing operative colonoscopy to identify their molecular pattern. The exhalated volatile organic molecules from these patients were sampled by the ReCIVA® breath sampler, shortly before surgery, and analyzed by GC-MS. Comparing VOC patterns identified in the tissues and in the breath of the same patients, a possible correlation can be found between the levels of methylbenzene and benzaldehyde exhaled and the presence of colonic adenomatous polypoid lesions.

Chronic kidney disease (CKD) may result in end-stage renal disorder and an increased mortality rate. Presently, kidney transplantation represents the only definitive treatment to restore normal life expectancy. Nevertheless, there is an elevated risk of organ rejection in the short–medium term after surgery. This preclinic study proposes the optimization of an innovative, fast, non-invasive, and cheap thermal desorption-gas chromatograph–mass spectrometry (TD-GC–MS) protocol, which provides simple monitoring of the respiratory profile of CKD-affected patients, before and during the first year after surgery, and aims to preemptively predict the rejection of the transplanted kidney. Twenty volatile organic compounds (VOCs), known as targets and, which are representative of the major classes of molecules discriminating between CKD-affected patients and healthy individuals, were selected from the literature, and employed to optimize the methodology. Calibration curves, linearity concentration ranges, the limit of detection (LOD), and the limit of quantification (LOQ) were estimated for the chosen molecules as well as the intraday and interday reproducibility of the method. The applicability of the TD-GC–MS developed approach was tested by analyzing the breath of healthy and pathological subjects using the ReCIVA® device. Sixty-seven molecules were identified, and between these, thirteen of the twenty selected compounds were quantified and were identified to have high prognostic values.

The presented study protocol outlines a comprehensive investigation into the interplay among the human microbiota, volatilome, and disease biomarkers, with a specific focus on Behçet’s disease (BD) using methods based on explainable artificial intelligence. The protocol is structured in three phases. During the initial three-month clinical study, participants will be divided into control and experimental groups. The experimental groups will receive a soluble fiber-based dietary supplement alongside standard therapy. Data collection will encompass oral and fecal microbiota, breath samples, clinical characteristics, laboratory parameters, and dietary habits. The subsequent biological data analysis will involve gas chromatography, mass spectrometry, and metagenetic analysis to examine the volatilome and microbiota composition of salivary and fecal samples. Additionally, chemical characterization of breath samples will be performed. The third phase introduces Explainable Artificial Intelligence (XAI) for the analysis of the collected data. This novel approach aims to evaluate eubiosis and dysbiosis conditions, identify markers associated with BD, dietary habits, and the supplement. Primary objectives include establishing correlations between microbiota, volatilome, phenotypic BD characteristics, and identifying patient groups with shared features. The study aims to identify taxonomic units and metabolic markers predicting clinical outcomes, assess the supplement’s impact, and investigate the relationship between dietary habits and patient outcomes. This protocol contributes to understanding the microbiome’s role in health and disease and pioneers an XAI-driven approach for personalized BD management. With 70 recruited BD patients, XAI algorithms will analyze multimodal clinical data, potentially revolutionizing BD management and paving the way for improved patient outcomes. [ABSTRACT FROM AUTHOR]

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Currently people are aware of the risk related to pollution exposure. Thus odor annoyances are considered a warning about the possible presence of toxic volatile compounds. Malodor often generates immediate alarm among citizens, and electronic noses are convenient instruments to detect mixture of odorant compounds with high monitoring frequency. In this paper we present a study on pattern recognition on ambient air composition in proximity of a gas and oil pretreatment plant by elaboration of data from an electronic nose implementing 10 metal-oxide-semiconductor (MOS) sensors and positioned outdoor continuously during three months. A total of 80,017 e-nose vectors have been elaborated applying the self-organizing map (SOM) algorithm and then k-means clustering on SOM outputs on the whole data set evidencing an anomalous data cluster. Retaining data characterized by dynamic responses of the multisensory system, a SOM with 264 recurrent sensor responses to air mixture sampled at the site and four main air type profiles (clusters) have been identified. One of this sensor profiles has been related to the odor fugitive emissions of the plant, by using ancillary data from a total volatile organic compound (VOC) detector and wind speed and direction data. The overall and daily cluster frequencies have been evaluated, allowing us to identify the daily duration of presence at the monitoring site of air related to industrial emissions. The refined model allowed us to confirm the anomaly detection of the sensor responses.