Mesoscale geomorphic change on barrier estuarine regime at the tropical coast of Chandipur, India: Evidence from assessment of grain-size distribution

It is crucial for policy makers and environmental managers to determine the future dynamics of coastal wetlands, especially the existence of their response, disruption, and recovery regimes. Reconstruction of meso-scale evolution in coastal ecosystems can help to adapt coastal resource management techniques to the natural scales of system activity, thereby encouraging true biodiversity. This research provides an overview of decadal (mesoscale) geomorphic transition by high-resolution grain size analysis of a sediment deposit from a barrier estuary regime on the Chandipur coast, India. Coastal marshland’s grain size distribution (GSD) has generally been analyzed using End Member Mixing Models (EMMA) and Probability Density Function (PDF) methods (e.g. log-normal, log skew-Laplace). Although these techniques do not consider the compositional nature of the records, which can undermine the outcomes of the interpretation of sedimentary deposits. The approach to reliable granulometric analysis of lithostratigraphic sequences aims at establishing direct links between fluid dynamics and subsequent shifts in the texture of sediments. In this study, GSD analysis of marsh sediment is represented by compositional data analysis (CoDa) and a multivariate statistical framework. Barrier estuary evolution, presented by time lapses of satellite maps coupled with grain size and carbon content of marsh sediment, primarily reflects the evolving hydrodynamics of the back barrier area. These findings will provide a statistically robust analysis of the depositional system in coastal marshland. Multiannual environmental variations in the back barrier configuration illustrate the importance of this applied approach with respect to bridging the basis of estuarine evolution and process information.

Cross-Sectional Associations Between 24-hour Activity Behaviours and Motor Competence in Youth: A Compositional Data Analysis

Background The study aimed to examine the cross-sectional associations between 24-hour activity compositions and motor competence in children and adolescents, while stratifying by sex and school type, and investigate the predicted differences in motor competence when time was reallocated between activity behaviours. Methods Data were collected from 359 participants (aged 11.5±1.4 years; 49.3% boys; 96.9% White British). Seven-day 24-hour activity behaviours (sleep, sedentary time, light physical activity (LPA), moderate-to-vigorous physical activity (MVPA)) were assessed using wrist-worn accelerometers. Motor competence outcomes were obtained using the Dragon Challenge (process, product, time, and overall scores). Linear mixed models examined associations between activity behaviour compositions and motor competence outcomes for all participants and stratified by school type (primary or secondary) and sex. Post-hoc analyses modelled the influence of reallocating fixed durations of time between activity behaviours on outcomes. Results In all participants, relative to other activity behaviours, MVPA had the strongest associations with motor competence outcomes. The stratified models displayed that MVPA had the strongest associations with outcomes in both sexes, irrespective of school type. The largest positive, and negative predicted differences occurred when MVPA replaced LPA or sleep, and when LPA or sleep replaced MVPA, respectively. Conclusions Relative to other activity behaviours, MVPA appears to have the greatest influence overall on motor competence outcomes. Reallocating time from LPA or sleep to MVPA reflected the largest positive predicted changes in motor competence outcomes. Therefore, our findings reinforce the key role of MVPA for children’s and adolescents’ motor competence.

A new approach to assess the degree of contamination and determine sources and risks related to PTEs in an urban environment: the case study of Santiago (Chile)

In 2017, a geochemical survey was carried out across the Commune of Santiago, a local administrative unit located at the center of the namesake capital city of Chile, and the concentration of a number of major and trace elements (53 in total) was determined on 121 topsoil samples. Multifractal IDW (MIDW) interpolation method was applied to raw data to generate geochemical baseline maps of 15 potential toxic elements (PTEs); the concentration–area (C-A) plot was applied to MIDW grids to highlight the fractal distribution of geochemical data. Data of PTEs were elaborated to statistically determine local geochemical baselines and to assess the spatial variation of the degree of soil contamination by means of a new method taking into account both the severity of contamination and its complexity. Afterwards, to discriminate the sources of PTEs in soils, a robust Principal Component Analysis (PCA) was applied to data expressed in isometric log-ratio (ilr) coordinates. Based on PCA results, a Sequential Binary Partition (SBP) was also defined and balances were determined to generate contrasts among those elements considered as proxies of specific contamination sources (Urban traffic, productive settlements, etc.). A risk assessment was finally completed to potentially relate contamination sources to their potential effect on public health in the long term. A probabilistic approach, based on Monte Carlo method, was deemed more appropriate to include uncertainty due to spatial variation of geochemical data across the study area. Results showed how the integrated use of multivariate statistics and compositional data analysis gave the authors the chance to both discriminate between main contamination processes characterizing the soil of Santiago and to observe the existence of secondary phenomena that are normally difficult to constrain. Furthermore, it was demonstrated how a probabilistic approach in risk assessment could offer a more reliable view of the complexity of the process considering uncertainty as an integral part of the results.

The Evaluation of the Impact of a Saharan Event on Particulate Matter Using Compositional Data Analysis

The proposed approach based on compositional data analysis was applied on simultaneous measurements of the mineral element concentrations of PM10 and PM2.5 from a typical suburban site with and without a Saharan event. The suburban site is located in the city of Rome. The selected mineral elements were Al, Si, Ca, Fe, Ti, Mg, and Sr. The data relating to these elements are reported in a previous study. The considered elements are mainly related to mineral matter. The proposed approach allows statistically validating that the mineral element concentrations of PM during days with a Saharan event differ from those without a Saharan event in terms of mineral element composition and size distribution. In particular, the results showed that the compositional data analysis applied to simultaneous measurements of mineral element concentrations of PM10 and PM2.5 is a helpful technique that can be used to study environmental sites affected by natural sources such as Saharan events. Moreover, the presented technique can be handy in all those conditions where it is important to discriminate whether the occurrence of an exceedance or a violation of the daily limit value established for PM could also be due to natural sources.

Changes in sedentary behavior patterns during the transition from childhood to adolescence and their association with adiposity: a prospective study based on compositional data analysis

Background To date, no longitudinal study using a compositional approach has examined sedentary behavior (SB) patterns in relation to adiposity in the pediatric population. Therefore, our aims were to (1) investigate the changes in SB patterns and adiposity from childhood to adolescence, (2) analyze the prospective compositional associations between changes in SB patterns and adiposity, and (3) estimate the changes in adiposity associated with substituting SB with physical activity (PA) of different intensities. Methods The study presents a longitudinal design with a 5-year follow-up. A total of 88 participants (61% girls) were included in the analysis. PA and SB were monitored for seven consecutive days using a hip-worn accelerometer. Adiposity markers (fat mass percentage [FM%], fat mass index [FMI], and visceral adiposity tissue [VAT]) were assessed using the multi-frequency bioimpedance analysis. The prospective associations were examined using compositional data analysis. Results Over the follow-up period, the proportion of time spent in total SB increased by 154.8 min/day (p < 0.001). The increase in total SB was caused mainly by an increase in middle and long sedentary bouts, as these SB periods increased by 79.8 min/day and 62.0 min/day (p < 0.001 for both), respectively. FM%, FMI, and VAT increased by 2.4% points, 1.0 kg/m2, and 31.5 cm2 (p < 0.001 for all), respectively. Relative to the remaining movement behaviors, the increase in time spent in middle sedentary bouts was significantly associated with higher FM% (βilr1 = 0.27, 95% confidence interval [CI]: 0.02 to 0.53) at follow-up. Lower VAT by 3.3% (95% CI: 0.8 to 5.7), 3.8% (95% CI: 0.03 to 7.4), 3.9% (95% CI: 0.8 to 6.9), and 3.8% (95% CI: 0.7 to 6.9) was associated with substituting 15 min/week spent in total SB and in short, middle, and long sedentary bouts, respectively, with an equivalent amount of time spent in vigorous PA. Conclusions This study showed unfavorable changes in SB patterns and adiposity status in the transition from childhood to adolescence. Incorporating high-intensity PA at the expense of SB appears to be an appropriate approach to reduce the risk of excess adiposity in the pediatric population.

Differential Compositional Variation Feature Selection: A Machine Learning Framework with Log Ratios for Compositional Metagenomic Data

The demand for tight integration of compositional data analysis and machine learning methodologies for predictive modeling in high-dimensional settings has increased dramatically with the increasing availability of metagenomics data. We develop the differential compositional variation machine learning framework (DiCoVarML) with robust multi-level log ratio bio-marker discovery for metagenomic datasets. Our framework makes use of the full set of pairwise log ratios, scoring ratios according to their variation between classes and then selecting out a small subset of log ratios to accurately predict classes. Importantly, DiCoVarML supports a targeted feature selection mode enabling researchers to define the number of predictors used to develop models. We demonstrate the performance of our framework for binary classification tasks using both synthetic and real datasets. Selecting from all pairwise log ratios within the DiCoVarML framework provides greater flexibility that can in demonstrated cases lead to higher accuracy and enhanced biological insight.

Compositional data analysis (CoDA) as a tool to evaluate a new low-cost settling-based PM₁₀ sampling head in a desert dust source region

This paper presents a new sampling head design and the method used to evaluate it. The elemental composition of aerosols collected by two different sampling devices in a semi-arid region of Tunisia is compared by means of compositional perturbation vectors and biplots. This set of underused mathematical tools belongs to a family of statistics created specifically to deal with compositional data. The two sampling devices operate at a flow rate in the range of 1 m3 h−1, with a cut-off diameter of 10 µm. The first device is a low-cost laboratory-made system, where the largest particles are removed by gravitational settling in a vertical tube. This new system will be compared to the second device, a brand-new standard commercial PM10 sampling head, where size segregation is achieved by particle impaction on a metal surface. A total of 44 elements (including rare earth elements, REEs, together with Al, As, Ba, Be, Ca, Cd, Co, Cr, Cu, Fe, K, Li, Mg, Mn, Mo, Na, Ni, P, Pb, Rb, S, Sc, Se, Sr, Ti, Tl, U, V, Zn, and Zr) were analysed in 16 paired samples, collected during a 2-week field campaign in Tunisian dry lands, close to source areas, with high levels of large particles. The contrasting meteorological conditions encountered during the field campaign allowed a broad range of aerosol compositions to be collected, with very different aerosol mass concentrations. The compositional data analysis (CoDA) tools show that no compositional differences were observed between samples collected simultaneously by the two devices. The mass concentration of the particles collected was estimated through chemical analysis. Results for the two sampling devices were very similar to those obtained from an online aerosol weighing system, TEOM (tapered element oscillating microbalance), installed next to them. These results suggest that the commercial PM10 impactor head can therefore be replaced by the decanter, without any measurable bias, for the determination of chemical composition and for further assessment of PM10 concentrations in source regions.