Psychometric validation of two versions of the adolescent Depression Self-Rating Scale (DSRS-A and DSRS-A Screener).

AIM: Examination of psychometric properties and diagnostic accuracy of the Depression Self-Rating Scale for Adolescents (DSRS-A) as well as development and evaluations of a shorter version, DSRS-A-Screener. METHODS: Analyses of component structure and internal consistency were performed in a community-based sample of adolescents N = 4,506 and among consecutive outpatients from three child psychiatric settings in Sweden (n = 137). Concurrent validity was measured as a correlation between a summation index of the scale items and the total major depressive disorder (MDD) symptom severity score from the Kiddie Schedule of Affective Disorders and Schizophrenia (K-SADS). Diagnostic accuracy was examined in the clinical sample, with the K-SADS interview as the reference test, by receiver operating characteristic analysis (ROC), calculations of sensitivity, specificity among other measures. With the purpose to select items for a shorter scale, associations between scale items and MDD were examined with binary logistic regression. This shorter scale was thereafter examined similarly. RESULTS: Based on association with MDD, five items were selected for the brief DSRS-A Screener that showed one component structure, internal consistency Cronbach's alpha .80 and.82, respectively. In the clinical population concurrent validity was Spearman's rho .63 and ROC analysis showed AUC .84 (95% CI .78-.91; p < .001). The optimal cut-off for screening was 2 with sensitivity .85 and specificity of .64. CONCLUSION: The DSRS-A Screener compared to the original scale, maintained or improved reliability, validity, and showed moderate diagnostic accuracy.

Estimating remobilization of potentially toxic elements in soil and road dust of an industrialized urban environment.

The mobility of potentially toxic elements (PTEs) is of paramount concern in urban settings, particularly those affected by industrial activities. Here, contaminated soils and road dusts of the medium-size, industrialized city of Volos, Central Greece, were subjected to single-step extractions (0.43 M HNO3 and 0.5 M HCl) and the modified BCR sequential extraction procedure. This approach will allow for a better understanding of the geochemical phase partitioning of PTEs and associated risks in urban environmental matrices. Based on single extraction procedures, Pb and Zn exhibited the highest remobilization potential. Of the non-residual phases, the reducible was the most important for Pb, and the oxidizable for Cu and Zn in both media. On the other hand, mobility of Ni, Cr, and Fe was low, as inferred by their dominance into the residual fraction. Interestingly, we found a significant increase of the residual fraction in the road dust samples compared to soils. Carbonate content and organic matter controlled the extractabilities of PTEs in the soil samples. By contrast, for the road dust, magnetic susceptibility exerted the main control on the geochemical partitioning of PTEs. We suggest that anthropogenic particles emitted by heavy industries reside in the residual fraction of the SEP, raising concerns about the assessment of this fraction in terms of origin of PTEs and potential environmental risks. Conclusively, the application of sequential extraction procedures should be complemented with source identification of PTEs with the aim to better estimate the remobilization of PHEs in soil and road dust influenced by industrial emissions.

Validation of the World Health Organization Adult ADHD Self-Report Scale for adolescents.

BACKGROUND: The World Health Organization Adult ADHD Self Report Scale (ASRS) is a widely used diagnostic tool for assessment of attention-deficit hyperactivity disorder (ADHD) symptoms in clinical psychiatry in Sweden. The ASRS consists of 18 questions, the first six of which can be used as a short screening version (ASRS-S). There is a version for adolescents-ASRS-Adolescent (ASRS-A)-and the corresponding screening version (ASRS-A-S), which has not been validated to date. AIM: The aim was to validate the ASRS-A and the ASRS-A-S for use in adolescent clinical populations. METHODS: Adolescent psychiatric outpatients (n = 134, mean age 15 years, 40% boys) reported on the ASRS-A, and were interviewed with the Kiddie Schedule of Affective Disorders and Schizophrenia (K-SADS), a semi-structured interview, together with a parent. RESULTS: Internal consistency was 0.79 for the ASRS-A-S and 0.92 for the ASRS-A (Cronbach's alpha). Internal consistency values were 0.79 and 0.87 for the inattention subscale, and 0.68 and 0.89 for the hyperactivity subscale, respectively. Concurrent validity values, measured with Spearman's correlation coefficient, between the total K-SADS ADHD symptom severity score and the sum of ASRS-A-S and ASRS-A total scores were 0.51 and 0.60, respectively. Psychometric properties of the ASRS-A-S and the ASRS-A were: sensitivity 74% and 79%; negative predictive value 81% and 84%; specificity 59% and 60%; and positive predictive value 49% and 51%, respectively. Both versions showed better properties for girls than for boys. CONCLUSION: Both the ASRS-A-S and the ASRS-A showed promising psychometric properties for use in adolescent clinical populations.

Major and trace element contents in apples from a cultivated area of central Greece.

Forty-two soil and apple samples from central Greece were collected and analyzed with regards to the content in major (Ca, Fe, K, Mg, Na, P, and S) and trace elements (As, B, Ba, Cd, Co, Cr, Cu, Hg, Mn, Mo, Ni, Pb, Sb, Sc, Se, Sr, Ti, and Zn). Soil samples were analyzed by inductively coupled plasma atomic emission spectrometry, while for the apples inductively coupled plasma mass spectrometry was implemented. Several elements such as As, B, Cd, Cr, Cu, Fe, Mn, Ni, Pb, Se, Ti, and Zn, represent high concentrations in apples from the study area. These relatively high concentrations may be a consequence of the local geology, along with the excessive application of agricultural products such as fertilizers and agrochemicals.

Groundwater hydrogeochemistry of Trikala municipality, central Greece.

Sixty-four samples from the groundwater resources of Trikala municipality, central Greece, were collected during two periods (2006 and 2007) and analyzed for physico-chemical parameters (temperature, pH, specific electrical conductivity, and total dissolved solids), major ions (Ca(2+), Cl(-), HCO(3)(-), K(+), Mg(2+), Na(+), NO(3)(-), SO(4)(2-)), and several potentially toxic elements (Al, B, Ba, Br, Ca, Ce, Cl, Cr, Cu, Fe, K, La, Li, Mg, Mn, Mo, Na, Nd, Ni, P, Pb, Rb, S, Sc, Si, Sn, Sr, U, V, Y, Zn). European Council directives and USEPA guidelines were used to assess the water quality. The results indicate that all samples are fresh water, suitable for human consumption. All basic ions and physico-chemical parameters have average concentrations below their recommended optimum limits with the exception of electrical conductivity, for January 2007, and nitrate for October 2006 and January 2007 sampling periods. This exceedance is the result of dissolution of minerals such as calcite and dolomite that are present in the surrounding rocks and the application of fertilizers, respectively. Lead is the only element with an average value that exceeds the recommended EC guideline, while special attention should be paid to one borehole (T9) which has elevated NO(3)(-) values which may pose a risk to human health.

A spatial interpolation approach for environmental assessment of aquaculture effects on water quality of Pagasitikos Gulf (Eastern Mediterranean).

The impact of fish farming on the physicochemical and biological parameters of the water column was investigated between July and October 2021, in Pagasitikos Gulf. For this purpose, a sampling strategy based on Geographical Information System (GIS) was developed and a grid of 28 sampling stations was created for in situ measurements. The method of spatial interpolation and especially the Radial Basis Function (RBF) was used to estimate the spatial distribution of DO, chl α and nutrients by means of GIS. General Linear Model (GLM) analysis showed significant differences for all measured parameters except for NO3. Nutrients remained at low concentrations without exceeding the permitted limits for eutrophication and welfare of cultivated organisms. GIS exhibited high usefulness for data analysis, with further studies warranted for its optimization on applications related to the environmental impact of fish farming.

The Impact of Different Cultivation Practices on Surface Runoff, Soil and Nutrient Losses in a Rotational System of Legume-Cereal and Sunflower.

Soil erosion is among the biggest problems in the agricultural sector that can affect ecosystems and human societies. A field of 5° slope was selected to study the runoff, soil and nutrient loss as well as crop productivity in different treatments-conventional tillage (CT) vs. no-tillage (NT), plant vs. no plant cover, contour cultivation (CC) vs. perpendicular to the contour cultivation, (PC) under natural rainfall. The experiment was conducted in central Greece in two cultivation periods. In autumn, the field was cultivated with intercropping Triticosecale and Pisum sativum and in spring with sunflower. The total rainfall was 141.4 mm in the 1st year and 311 mm in the 2nd. We found that runoff in the treatment of no tillage with contour cultivation was 85% lower in both years compared to the no tillage-no plant control. Therefore, the contour cultivation-no tillage treatment had a positive effect by decreasing phosphorus and potassium loss from soil: indeed, there was a decrease in P and K by 55% and 62%, respectively, in the NT compared to the CC treatments. We conclude that the NT-CC treatment with plant cover was the most effective in reducing water runoff and soil nutrient loss and increasing yield.

Tracing the sources of bioaccessible metal(loid)s in urban environments: A multidisciplinary approach.

Understanding the links between sources of magnetic particles and bioaccessibility of metal(loids) in environmental sampling media is crucial for better evaluating human health risks, although relevant information in the scientific literature is scarce. Here, soil, road and house dust samples from a heavy industrial area in Greece were characterized in a multidisciplinary study combining magnetic measurements, SEM/EDS analyses, bioaccessibility measurements and Pb isotopic analyses of bioaccessible Pb. The oral and inhalable bioaccessible fractions of As, Cd, Cr, Cu, Fe, Mn, Ni, Pb and Zn were assessed by applying simulated gastric and lung solutions. SEM/EDS analysis revealed the abundant presence of anthropogenic Fe-containing spherules of industrial origin in all sampling media, often containing minor contents of Cr, Cu, Mn, Pb and Zn. The inhalation bioaccessibility (%) in all environmental compartments was higher than the oral one for most elements analyzed in the present study. Clear associations between magnetic susceptibility and bioaccessible amounts of most of analyzed elements were encountered for the soil and road dust. The isotopic analyses of bioaccessible Pb showed that there are significant differences in the isotopic ratios between total and bioaccessible Pb. We conclude that Pb solubilized by the simulated gastric and lung extractions is principally anthropogenic, representing a mixture of industrial Pb and Pb related to the past usage of leaded petrol. Low values of 206Pb/207Pb were accompanied by high bioaccessible contents of Cd, Pb and Zn indicating that anthropogenic (mostly industrial) sources exert influence on the bioaccessible forms of these metals. Coupling magnetic and bioaccessibility measurements with stable isotopic technique of bioaccessible Pb is more reliable for determining Pb and other metal sources with high oral and inhalation bioaccessibility.

Metal(loid) and isotopic tracing of Pb in soils, road and house dusts from the industrial area of Volos (central Greece).

This study examines the metal(loid) contents (As, Cd, Cr, Cu, Mn, Ni, Pb, Sb, Tl and Zn) and Pb isotopes in different environmental compartments (soil, road dust, house dust) from the industrial vicinity of Volos, central Greece. The area surrounding two steel factories, a cement plant, an industrial area and the city core were considered as potential hot spots of metal(loid) contamination. Significant anthropogenic enrichments of Cd, Pb and Zn in relation to local baseline were identified for the soil (median Enrichment Factors of 7, 15 and 8, respectively) and road dusts around the steel factory located at Velestino area. The high contents of As, Sb and Tl in the soil and road dust around the cement plant are attributed to natural sources of contamination associated with adjacent mineralization. The soil samples in the city core exhibited moderate enrichments with respect to typical tracers (Pb, Zn) of anthropogenic contamination in urban areas. Anthropogenic influences in terms of metal(loid) concentrations were more pronounced for the road and house dust material. The Pb isotopic ratios of soil (206Pb/207Pb = 1.154 to 1.194), road dust (206Pb/207Pb = 1.144 to 1.174) and house dust (206Pb/207Pb = 1.129 to 1.171) were between those of the local bedrock and anthropogenic Pb sources. Industrial Pb from the steel plant was the predominant anthropogenic Pb source with relative contributions of ~49% for the soil, ~42% for the road dust and ~44% for the house dust samples. For the road and house dust material, the geochemical signature obtained from Pb isotopic compositions and elemental ratios suggests additional contributors from vehicular emissions. The results of this study demonstrate the suitability of soil to trace natural and anthropogenic impacts in industrial areas and the sensitivity of the road and house dust material to record anthropogenic (industrial and vehicular-derived) contamination in such environments.

Design and implementation of a hybrid model based on two-layer decomposition method coupled with extreme learning machines to support real-time environmental monitoring of water quality parameters.

Accurate prediction of water quality parameters plays a crucial and decisive role in environmental monitoring, ecological systems sustainability, human health, aquaculture and improved agricultural practices. In this study a new hybrid two-layer decomposition model based on the complete ensemble empirical mode decomposition algorithm with adaptive noise (CEEMDAN) and the variational mode decomposition (VMD) algorithm coupled with extreme learning machines (ELM) and also least square support vector machine (LSSVM) was designed to support real-time environmental monitoring of water quality parameters, i.e. chlorophyll-a (Chl-a) and dissolved oxygen (DO) in a Lake reservoir. Daily measurements of Chl-a and DO for June 2012-May 2013 were employed where the partial autocorrelation function was applied to screen the relevant inputs for the model construction. The variables were then split into training, validation and testing subsets where the first stage of the model testing captured the superiority of the ELM over the LSSVM algorithm. To improve these standalone predictive models, a second stage implemented a two-layer decomposition with the model inputs decomposed in the form of high and low frequency oscillations, represented by the intrinsic mode function (IMF) through the CEEMDAN algorithm. The highest frequency component, IMF1 was further decomposed with the VMD algorithm to segregate key model input features, leading to a two-layer hybrid VMD-CEEMDAN model. The VMD-CEEMDAN-ELM model was able to reduce the root mean square and the mean absolute error by about 14.04% and 7.12% for the Chl-a estimation and about 5.33% and 4.30% for the DO estimation, respectively, compared with the standalone counterparts. Overall, the developed methodology demonstrates the robustness of the two-phase VMD-CEEMDAN-ELM model in identifying and analyzing critical water quality parameters with a limited set of model construction data over daily horizons, and thus, to actively support environmental monitoring tasks, especially in case of high-frequency, and relatively complex, real-time datasets.