Investigating the effectiveness of simplified labels for safe use communication : the case of household detergents

This study assessed the effectiveness of safety communication on the back labels of hazardous products (with regulatory and safety information as dictated by regulatory requirements), with household detergents as a test case. The potential of simplification to increase label effectiveness was evaluated by comparing the currently used labelling approach with two simplified alternatives. The labels mainly differed in terms of the amount of information and the prominence of pictograms. The generalisability of theoretical insights on the effectiveness of pictograms in safety messages to a more real-life context was tested by (a) realistic labels containing several other information elements besides the safety information and (b) target users who are knowledgeable about the product type. One thousand eight hundred (1,800) respondents participated in an online experiment and were randomly exposed to one of the labels. The positive cognitive and behavioural effects commonly attributed to pictorials could not be confirmed, but positive affective effects did emerge. Specifically, even though participants were asked to carefully read the label, they did not spend enough time to process all the content except for the most simplified label. The results did not show meaningful differences between the three labels in terms of information recall (which was poor for all executions), hazard perceptions and behavioural intentions when confronted with an accident. In contrast to this lack of differentiation in cognitive and behavioural intention effects, we did find a clear difference in the affective measure. A majority of the respondents preferred the simplified safety labels. As such, avoiding information overload, and conveying the information in an easier way by means of more prominent use of pictograms, appeared to be appreciated by consumers of household products, while it did not negatively impact label effectiveness

Toxicity Ranking and Toxic Mode of Action Evaluation of Commonly Used Agricultural Adjuvants on the Basis of Bacterial Gene Expression Profiles

The omnipresent group of pesticide adjuvants are often referred to as “inert” ingredients, a rather misleading term since consumers associate this term with “safe”. The upcoming new EU regulation concerning the introduction of plant protection products on the market (EC1107/2009) includes for the first time the demand for information on the possible negative effects of not only the active ingredients but also the used adjuvants. This new regulation requires basic toxicological information that allows decisions on the use/ban or preference of use of available adjuvants. In this study we obtained toxicological relevant information through a multiple endpoint reporter assay for a broad selection of commonly used adjuvants including several solvents (e.g. isophorone) and non-ionic surfactants (e.g. ethoxylated alcohols). The used assay allows the toxicity screening in a mechanistic way, with direct measurement of specific toxicological responses (e.g. oxidative stress, DNA damage, membrane damage and general cell lesions). The results show that the selected solvents are less toxic than the surfactants, suggesting that solvents may have a preference of use, but further research on more compounds is needed to confirm this observation. The gene expression profiles of the selected surfactants reveal that a phenol (ethoxylated tristyrylphenol) and an organosilicone surfactant (ethoxylated trisiloxane) show little or no inductions at EC20 concentrations, making them preferred surfactants for use in different applications. The organosilicone surfactant shows little or no toxicity and good adjuvant properties. However, this study also illustrates possible genotoxicity (induction of the bacterial SOS response) for several surfactants (POEA, AE, tri-EO, EO FA and EO NP) and one solvent (gamma-butyrolactone). Although the number of compounds that were evaluated is rather limited (13), the results show that the used reporter assay is a promising tool to rank commonly used agricultural adjuvants based on toxicity and toxic mode of action data

GEO-REFERENCED PREDICTION OF ENVIRONMENTAL CONCENTRATIONS OF CHEMICALS IN RIVERS: A HYPOTHETICAL CASE STUDY ABSTRACT

For use within environmental risk assessment, a new tool for chemical fate prediction, GREAT-ER (Geography-referenced Regional Exposure Assessment Tool for European Rivers), is being developed. In this paper, the practical applicability of the GREAT-ER simulation methodology is illustrated, by means of a hypothetical but realistic case study. Temporal distributions of predicted environmental concentrations (PEC) were calculated and analyzed. For the analysis of seasonality (i.e. the variability of river flows through the year), the Monte Carlo simulation technique was compared to a discrete ‘flow scenario ’ approach. Finally, the scale-independent character of the approach was investigated, by upscaling from a detailed to a larger geographical scale

A GEO-REFERENCED FATE SIMULATION METHODOLOGY FOR AQUATIC EXPOSURE ASSESSMENT OF ‘DOWN-THE-DRAIN ’ CHEMICALS

In the GREAT-ER project (Geography-referenced Regional Exposure Assessment Tool for European Rivers) a refined exposure assessment method for ‘down-the-drain ’ chemicals was developed. Real-world data are used, including spatial and temporal variability and uncertainty. The results are geo-referenced distributions of predicted environmental concentrations (PEC). To calculate these distributions, a hybrid stochastic/deterministic simulation approach is applied. Geographies are segmented, based on river network properties. In each segment, several processes (emission or transport/conversion) can occur, which further consist of sub-processes (e.g. different treatment types). The system’s core consists of steady-state deterministic models, which describe chemical fate processes in ‘main rivers ’ and in the waste water pathway (emission, transport, treatment). The results of these models are discrete PECs in the considered catchments. A stochastic (Monte Carlo) simulation is applied on top of this. Discrete ‘shots’, each of which applies to the entire simulated geography, are sampled from the input distributions. All ‘shots ’ are processed in the deterministic model, of which the discrete results are statistically analyzed to obtain the PEC distributions