Fluorescence fingerprinting is a technique to uniquely characterize water samples based on their distinct composition of dissolved organic matter (DOM) measured via 3D fluorescence spectroscopy. It is an effective tool for monitoring the chemical composition of various water systems. This study examines a river affected by several municipal and industrial wastewater treatment plant (WWTP) effluents and aims to source-tracing them via fluorescence fingerprints based on parallel factor analysis (PARAFAC) components. Additional principal component analysis (PCA) clusters the WWTP effluents according to similarity. The results yield seven PARAFAC components characterizing the WWTP effluents. Considering the ratios among the components, these distinct fluorescence fingerprints are attributable to the studied industrial sectors: leather industry, meat processing, electronics industry, and municipal wastewater treatment. Furthermore, the fluorescence signal of the receiving river is examined by PCA and assessment of flow-weighted fluorescence intensities for source-tracing the fingerprints of the WWTP effluents. An analysis of the contribution of each WWTP effluent shows that during low flow, the fluorescence signal in the river is dominated by WWTP emissions. In contrast, during high flow events, the impact of WWTP emissions is masked by diffuse emissions. The techniques presented in this study have the potential to define generalizable fluorescence fingerprints for WWTP effluents of various industrial sectors and source-trace them in the receiving river. This approach represents a step closer to implementing complex fluorescence monitoring tools in rivers, tracing the impact of municipal and industrial WWTP effluents on riverine OM.

Fluorescence spectroscopy has numerous applications to characterize natural and human-influenced water bodies regarding dissolved organic matter (DOM) and contamination. Analyzing samples in a timely manner is crucial to gaining valid and reproducible excitation-emission matrices (EEM) but often difficult, specifically in transnational projects with long transport distances. In this study, eight samples of different water sources (tap water, differently polluted rivers, and wastewater treatment plant (WWTP) effluents) were stored under standardized conditions for 59 days and analyzed regularly. With this data set, the sample and fluorescence spectra stability was evaluated. Established analysis methods such as peak picking and fluorescence metrics were compared over time and benchmarked against dissolved organic carbon (DOC) and a maximal change of 10% in terms of their variability. Additional high-performance liquid chromatography (HPLC) data to identify single organic compounds provides insights into these DOM alterations and allows for conclusions about the underlying biological processes. Our results corroborate in a systematic way that the higher the organic or microbial load, the faster the sample must be processed. For all water sources, considerable changes were found between days zero and one, indicating a potential systematic bias between in-situ and laboratory measurements. The absolute signals of individual peaks vary substantially after only a few days. In contrast, relative metrics are robust for a much longer time. For specific metrics, when filtered and stored under cool and dark conditions, tap water may be stored for up to 59 days, non-polluted river water for up to 31-59 days, and WWTP effluents for up to 14-59 days. The storability thus depends both on the specific water source and the analytical plan. By systematizing our understanding of how the specific water source and DOM concentration determine the stability of samples during storage, these conclusions facilitate efforts to establish a standardized protocol.

Fluorescence spectroscopy has become a widely used technique to characterize dissolved organic matter (DOM) and organic hazardous micro-pollutants in natural and human-influenced water bodies. Especially in rivers highly impacted by municipal and industrial wastewater treatment plants' effluents, the fluorescence signal at low-flow is mainly dominated by these discharges. At river high-flow, their influence decreases due to dilution effects, and at the same time, other compounds of DOM, stemming from diffuse inputs, can increase or even dominate. Therefore, whereas the analysis of DOM is little informative on the changing sources and pathways of emissions, fluorescence spectroscopy can enhance our understanding and our possibilities of monitoring such dynamics in river catchments. This paper analyzed samples from seven high-flow events in an Austrian river. Firstly, independent DOM components were discriminated using a parallel factor analysis (PARAFAC) to show the varying composition of DOM during different phases of high-flow events. Furthermore, partial least squares (PLS) and sparse PLS (sPLS) regression were applied to identify excitation and emission wavelengths, serving as proxy parameters for quantifying dissolved organic carbon (DOC) and chloride. The PLS models show the best prediction accuracy but use the entire excitation-emission matrix in exchange. In selecting predictors, the use of excitation and emission wavelengths adjusted via sPLS is superior to the extracted PARAFAC components. The sPLS model yields 16 wavelength combinations for DOC (RMSEsPLS = 0.41 mg L-1) and 18 wavelength combinations for chloride (RMSEsPLS = 2.21 mg L-1). In contrast to other established optical measurement methods, which require different calibrations for low- and high-flow conditions, these models based on sPLS succeed in quantifying those parameters across the entire range of flow conditions and events of various magnitudes with a relative precision of about 5 %. These results show how the application of multivariate statistical techniques enhances the exploitation of the information provided by fluorescence spectroscopy.

Within the new policy framework shaped by the EU Green Deal and the Circular Economy Action Plans, the field of wastewater and sludge treatment in Europe is subject to high expectations and new challenges related to mitigation of greenhouse gas emissions, micropollutant removal and resource recovery. With respect to phosphorus recovery, sev-eral technologies and processes have been thoroughly investigated. Nevertheless, a systemic and detailed understand-ing of the existing infrastructure and of the related environmental and economic implications is missing. Such basis is essential to avoid unwanted consequences in designing new strategies, given the long lifespan of any infrastructural change. This study couples a newly collected and highly detailed database for all wastewater treatment plants in Austria bigger than 2000 population equivalent with a combination of analyses, namely Substance Flow Analysis with focus on nutrient and metal distribution in different environmental and anthropogenic compartments, Energy Flow Analysis, Life Cycle Assessment and cost estimation. The case study of Austria is of special interest, given its highly autonomous administration in federal states and its contrasting traits, ranging from flat metropolitan areas like Vienna to low-populated alpine areas. The significant impact of electricity demand of wastewater treatment on the overall Cumulative Energy Demand (CED) shows the importance of optimization measures. Further, the current system of wastewater and sludge disposal have a low efficiency in recovering nutrients and in directing pollutants as heavy metals into final sinks. Sludge composting with subsequent use in landscaping does not only show an unfa-vorable environmental balance, but it is the only relevant route leading to additional CED and Global Warming Poten-tial emissions and to the highest transport volume. Altogether, the outcomes of this study provide a sound basis to further develop national strategies for resource recovery aimed to optimize trade-offs between different economic and environmental objectives.

BACKGROUND:For improving health literacy (HL) by national and international public health policy, measuring population HL by a comprehensive instrument is needed. A short instrument, the HLS19-Q12 based on the HLS-EU-Q47, was developed, translated, applied, and validated in 17 countries in the WHO European Region.METHODS:For factorial validity/dimensionality, Cronbach alphas, confirmatory factor analysis (CFA), Rasch model (RM), and Partial Credit Model (PCM) were used. For discriminant validity, correlation analysis, and for concurrent predictive validity, linear regression analysis were carried out.RESULTS:The Cronbach alpha coefficients are above 0.7. The fit indices for the single-factor CFAs indicate a good model fit. Some items show differential item functioning in certain country data sets. The regression analyses demonstrate an association of the HLS19-Q12 score with social determinants and selected consequences of HL. The HLS19-Q12 score correlates sufficiently highly (r ≥ 0.897) with the equivalent score for the HLS19-Q47 long form.CONCLUSIONS:The HLS19-Q12, based on a comprehensive understanding of HL, shows acceptable psychometric and validity characteristics for different languages, country contexts, and methods of data collection, and is suitable for measuring HL in general, national, adult populations. There are also indications for further improvement of the instrument.

Inference of the marginal likelihood of sample allele configurations using backward algorithms yields identical results with the Kingman coalescent, the Moran model, and the diffusion model (up to a scaling of time). For inference of probabilities of ancestral population allele frequencies at any given point in the past - either of discrete ancestral allele configurations as in the coalescent, or of ancestral allele proportions as in the backward diffusion - backward approaches need to be combined with corresponding forward ones. This is done in so-called forward-backward algorithms. In this article, we utilize orthogonal polynomials in forward-backward algorithms. They enable efficient calculation of past allele configurations of an extant sample and probabilities of ancestral population allele frequencies in equilibrium and in non-equilibrium. We show that the genealogy of a sample is fully described by the backward polynomial expansion of the marginal likelihood of its allele configuration.

This is the first study investigating the use of a web-based version of the Youth Self-Report (YSR) compared to paper-pencil (PP) assessment. Students aged 10-18 years were recruited from Austrian schools and either completed a PP (N = 841) or a web-based (N = 2,769) version. Psychometric properties and indicators of data quality were analyzed. Moreover, cost estimations for the web-based and PP assessments were given. Acceptable model fits (RMSEA < .05) of the 8-syndrome model were observed for both versions and measurement invariance testing revealed strong invariance between both formats. While there was no significant difference in the number of missing items, a slightly higher proportion of web-based datasets were analyzable (97.7% vs. 93.7%). A larger amount of information was provided in the open-ended questions in the web-based version (p < .001). Mean problem scores were equivalent, with the exception of the thought problems syndrome scale in which a slightly higher score was observed in the web-based version (p < .001). In our study, the web-based format reduced costs by 72% compared to the PP format. Our findings suggest that large web-based epidemiological surveys are cost-efficient, can be applied without the risk of disadvantages compared to PP assessments and might be superior regarding some aspects of data quality.

During the last years fluorescence spectroscopy was developed to be a fast and inexpensive way for water quality measurement in various water systems to characterize natural and human influenced water bodies regarding organic matter and contamination. Analyzing samples in a timely manner is crucial to gain valid and reproducible excitation emission matrix (EEM) data, but often difficult, specifically in transnational projects. In this project the shift of fluorescence spectra and other parameters over time is evaluated. Ten different ground-, creek- and river water, as well as wastewater treatment plant effluent samples were stored for more than 20 days. EEM data, as well as high pressure liquid chromatography (HPLC) spectra data and chemical standard parameters like pH, ORP (oxidation/reduction potential), TOC, NH4, NO3, NO2 and PO4 were measured daily and correlated. With this dataset the sample and fluorescence spectra stability were evaluated. Different mathematical and statistical methods, including Parallel Factor Analysis (PARAFAC) as well as novel statistical approaches, were applied for assessment of EEM and HPLC spectra. This further enables the direct comparison of the included analysis methods.

Fluorescence Spectroscopy is a very promising tool for the identification of dissolved organic material (DOM) in aquatic systems. It is rapid, sensitive and relatively inexpensive. Knowledge gaps and challenging interpretation of large and complex datasets are currently hindering the full exploitation of its potential. To cite only few of the most crucial challenges, different fluorophores can contribute to overlapping peaks in the Excitation Emission Matrix (EEM), peaks can be shifted or their intensity can be reduced or enhanced through different environmental factors, and more powerful data processing tools are required. EEM data are typically analyzed by means of Parallel Factor Analysis (PARAFAC), which is a powerful technique that will also be applied here. Nevertheless, PARAFAC and similar analytical tools have a range of limitations. Therefore, we propose to develop and test a novel systemic approach and Bayesian statistical techniques to overcome existing obstacles. Contrary to the above mentioned, Bayesian statistics allow integrating prior information within the analysis in a transparent, formal and reproducible way. In this field, a vast body of knowledge and data has been gathered, which can be formalized in the form of priors and be included in the interpretation of data to make the analysis more powerful and robust. We will explore different applications in an EEM dataset consisting of samples from well-studied water systems with diverse characteristics covering spatial and temporal variability.

In population genetics, extant samples are usually used for inference of past population genetic forces. With the Kingman coalescent and the backward diffusion equation, inference of the marginal likelihood proceeds from an extant sample backward in time. Conditional on an extant sample, the Moran model can also be used backward in time with identical results, up to a scaling of time. In particular, all three approaches -- the coalescent, the backward diffusion, and the Moran model -- lead to the identical marginal likelihood of the sample. If probabilities of ancestral states are also inferred, either of discrete ancestral allele particle configurations, as in the coalescent, or of ancestral population allele proportions, as in the backward diffusion, the backward algorithm needs to be combined with the corresponding forward algorithm to the forward-backward algorithm. Generally orthogonal polynomials, solving the diffusion equation, are numerically simpler than the other approaches: they implicitly sum over many intermediate ancestral particle configurations; furthermore, while the Moran model requires iterative matrix multiplication with a transition matrix of a dimension of the population size squared, expansion of the polynomials is only necessary up to the sample size. For discrete samples, forward-in-time moving pure birth processes similar to the Polya- or Hoppe-urn models complement the backward-looking coalescent. Because, the sample size is a random variable forward in time, pure-birth processes are unsuited to model population demography given extant samples. With orthogonal polynomials, however, not only ancestral allele proportions but also probabilities of ancestral particle configurations can be calculated easily. Assuming only mutation and drift, the use of orthogonal polynomials is numerically advantageous over alternative strategies.