Gianna Serafina Monti

Toxicity on the luminescent bacterium Vibrio Fischeri (Beijerinck). I: QSAR equation for narcotics and polar narcotics

Toxicity data on chemicals, supposed to have a narcotic or polar narcotic toxicological mode of action, have been produced on the luminescent bacterium Vibrio fischeri using the Microtox test procedure. Advanced statistical methods have been used to calculate statistically sound values for ecotoxicological endpoints. Simple quantitative structure activity relationship (QSAR) equations were developed for narcotics and polar narcotics. These equations were compared with those proposed by the European Technical Guidance Document on Risk Assessment for other aquatic organisms (algae, Daphnia, and fish). Similarities and differences are discussed. The need for including the bacterial component in the ecotoxicological risk assessment for aquatic ecosystems is highlighted.

Toxicity on the luminescent bacterium Vibrio fischeri (Beijerinck). II: Response to complex mixtures of heterogeneous chemicals at low levels of individual components

The toxicity of eight complex mixtures of chemicals with different chemical structures and toxicological modes of action (narcotics, polar narcotics, herbicides, insecticides, fungicides) was tested on the luminescent bacterium Vibrio fischeri. There were maximum 84 individual chemicals in the mixtures. Suitable statistical approaches were applied for the comparison between experimental results and theoretical predictions. The results demonstrated that the two models of Concentration Addition (CA) and Independent Action (IA) are suitable to explain the effect of the mixtures.Even extremely lower concentrations of individual chemicals contributed to the effect of the mixtures. Synergistic effects were not observed in any of the tested mixtures. In particular, the CA approach well predicted the effects of six out of eight mixtures and slightly overestimated the effects of the remaining two mixtures. Therefore, the CA model can be proposed as a pragmatic and adequately protective approach for regulatory purposes.

SSD-based rating system for the classification of pesticide risk on biodiversity

A novel approach, based on Species sensitivity distribution (SSD), is proposed for the development of an index for classifying ecotoxicological pesticide risk in surface waters. In this approach, the concept of TER (Toxicity Exposure Ratio), commonly used in traditional risk indices, is substituted by the concept of PAF (Potentially Affected Fraction), which takes into account several species within the biological community of interest, rather than just a small number of indicator species assumed as being representative of the ecosystem. The procedure represents a probabilistic tool to quantitatively assess the ecotoxicological risk on biodiversity considering the distribution of toxicological sensitivity. It can be applied to assess chemical risk on generic aquatic and terrestrial communities as well as on site-specific natural communities. Examples of its application are shown for some pesticides in freshwater ecosystems. In order to overcome the problem of insufficient reliable ecotoxicological data, a methodology and related algorithms are proposed for predicting SSD curves for chemicals that do not have sufficient available data. The methodology is applicable within congeneric classes of chemicals and has been tested and statistically validated on a group of organophosphorus insecticides. Values and limitations of the approach are discussed.

Risk of POP mixtures on the Arctic food chain

The exposure of the Arctic ecosystem to persistent organic pollutants (POPs) was assessed through a review of literature data. Concentrations of 19 chemicals or congeneric groups were estimated for the highest levels of the Arctic food chain (Arctic cod, ringed seals, and polar bears). The ecotoxicological risk for seals, bears, and bear cubs was estimated by applying the concentration addition (CA) concept. The risk of POP mixtures was very low in seals. By contrast, the risk was 2 orders of magnitude higher than the risk threshold for adult polar bears and even more (3 orders of magnitude above the threshold) for bear cubs fed with contaminated milk. Based on the temporal trends available for many of the chemicals, the temporal trend of the mixture risk for bear cubs was calculated. Relative to the 1980s, a decrease in risk from the POP mixture is evident, mainly because of international control measures. However, the composition of the mixture substantially changes, and the contribution of new POPs (particularly perfluorooctane sulfonate) increases. These results support the effectiveness of control measures, such as those promulgated in the Stockholm Convention, as well as the urgent need for their implementation for new and emerging POPs. Environ Toxicol Chem 2017;36:1181-1192.

Preprocessing of centred logratio transformed density functions using smoothing splines

With large-scale database systems, statistical analysis of data, occurring in the form of probability distributions, becomes an important task in explorative data analysis. Nevertheless, due to specific properties of density functions, their proper statistical treatment of these data still represents a challenging task in functional data analysis. Namely, the usual metric does not fully accounts for the relative character of information, carried by density functions; instead, their geometrical features are captured by Bayes spaces of measures. The easiest possibility of expressing density functions in an space is to use centred logratio transformation, even though this results in functional data with a constant integral constraint that needs to be taken into account in further analysis. While theoretical background for reasonable analysis of density functions is already provided comprehensively by Bayes spaces themselves, preprocessing issues still need to be developed. The aim of this paper is to introduce optimal smoothing splines for centred logratio transformed density functions that take all their specific features into account and provide a concise methodology for reasonable preprocessing of raw (discretized) distributional observations. Theoretical developments are illustrated with a real-world data set from official statistics and with a simulation study.

A structured Dirichlet mixture model for compositional data: inferential and applicative issues

The flexible Dirichlet (FD) distribution (Ongaro and Migliorati in J. Multvar. Anal. 114: 412–426, 2013) makes it possible to preserve many theoretical properties of the Dirichlet one, without inheriting its lack of flexibility in modeling the various independence concepts appropriate for compositional data, i.e. data representing vectors of proportions. In this paper we tackle the potential of the FD from an inferential and applicative viewpoint. In this regard, the key feature appears to be the special structure defining its Dirichlet mixture representation. This structure determines a simple and clearly interpretable differentiation among mixture components which can capture the main features of a large variety of data sets. Furthermore, it allows a substantially greater flexibility than the Dirichlet, including both unimodality and a varying number of modes. Very importantly, this increased flexibility is obtained without sharing many of the inferential difficulties typical of general mixtures. Indeed, the FD displays the identifiability and likelihood behavior proper to common (non-mixture) models. Moreover, thanks to a novel non random initialization based on the special FD mixture structure, an efficient and sound estimation procedure can be devised which suitably combines EM-types algorithms. Reliable complete-data likelihood-based estimators for standard errors can be provided as well.

A Robust Approach to Risk Assessment Based on Species Sensitivity Distributions

The guidelines for setting environmental quality standards are increasingly based on probabilistic risk assessment due to a growing general awareness of the need for probabilistic procedures. One of the commonly used tools in probabilistic risk assessment is the species sensitivity distribution (SSD), which represents the proportion of species affected belonging to a biological assemblage as a function of exposure to a specific toxicant. Our focus is on the inverse use of the SSD curve with the aim of estimating the concentration, HCp, of a toxic compound that is hazardous to p% of the biological community under study. Toward this end, we propose the use of robust statistical methods in order to take into account the presence of outliers or apparent skew in the data, which may occur without any ecological basis. A robust approach exploits the full neighborhood of a parametric model, enabling the analyst to account for the typical real-world deviations from ideal models. We examine two classic HCp estimation approaches and consider robust versions of these estimators. In addition, we also use data transformations in conjunction with robust estimation methods in case of heteroscedasticity. Different scenarios using real data sets as well as simulated data are presented in order to illustrate and compare the proposed approaches. These scenarios illustrate that the use of robust estimation methods enhances HCp estimation.

Log-ratio approach in curve fitting for concentration-response experiments

The assessment of the ecological risk of chemical contamination by pollutants, pesticides or toxicants is of primary interest in environmental statistics. Concentration-response models play a fundamental role in computing the risk values connected with some exposure levels of a particular contaminant in living organisms. The present paper proposes a regression model called simplicial regression. This model is able to cope with the relative character of the explanatory and response parts via the logratio methodology of compositional data. Consequently, it allows performance of the corresponding statistical inference under the assumption of normality. Some real-world examples show that simplicial regression even outperforms the existing well-established methodologies on standard accuracy and quality-of-fit criteria. The better fit is due to the change of scale entailed by the new model.