Improving plant bioaccumulation science through consistent reporting of experimental data

Highlights

• Plant bioaccumulation guidelines and testing practice are reviewed.

• Overview of plant bioaccumulation models and required input data is given.

• Key parameters are identified for improved plant bioaccumulation science.

• Recommendations are given for data be reported in future experimental studies.

Abstract

Experimental data and models for plant bioaccumulation of organic contaminants play a crucial role for assessing the potential human and ecological risks associated with chemical use. Plants are receptor organisms and direct or indirect vectors for chemical exposures to all other organisms. As new experimental data are generated they are used to improve our understanding of plant-chemical interactions that in turn allows for the development of better scientific knowledge and conceptual and predictive models. The interrelationship between experimental data and model development is an ongoing, never-ending process needed to advance our ability to provide reliable quality information that can be used in various contexts including regulatory risk assessment. However, relatively few standard experimental protocols for generating plant bioaccumulation data are currently available and because of inconsistent data collection and reporting requirements, the information generated is often less useful than it could be for direct applications in chemical assessments and for model development and refinement. We review existing testing guidelines, common data reporting practices, and provide recommendations for revising testing guidelines and reporting requirements to improve bioaccumulation knowledge and models. This analysis provides a list of experimental parameters that will help to develop high quality datasets and support modeling tools for assessing bioaccumulation of organic chemicals in plants and ultimately addressing uncertainty in ecological and human health risk assessments.

Introduction

Terrestrial plants constitute the largest global mass fraction of living organisms and are the primary food source for humans and most terrestrial animals (Houghton et al., 2009). Plants take up, translocate, transform, and accumulate organic chemicals that are not essential for plant growth and development (ITRC, 2011, U.S. EPA, 2012f), thereby contributing to the cycling of organic contaminants from local to global scales (Collins et al., 2011). Plants are subject to toxic effects from exposure to chemical stressors. Plants are also direct and indirect vectors for chemical exposures to higher trophic level organisms. Environmental concentrations and plant bioaccumulation (toxicokinetics) determine the likelihood for adverse effects to plants directly and to subsequent exposures and potential adverse effects to higher trophic level organisms. The extent of bioaccumulation is a function of substance-specific physicochemical properties, plant species-specific characteristics, and environmental conditions (Collins et al., 2011, Fantke et al., 2014, Trapp, 2015). Understanding plant uptake and bioaccumulation is crucial for a variety of regulatory applications including the authorization of formulations containing pesticides (EC, 2009) or biocides (EC, 1998), and for commercial chemicals falling under the REACH regulation (EC, 2006). Plant uptake has also been exploited to phytoremediate chemically contaminated sites and to delineate the extent of groundwater plumes using plants as biomonitors. The potential influence of plants in the overall fate and persistence of chemicals in the environment has been modelled at various scales but is largely unknown, particularly for chemicals that may be subject to degradation on or in plants (Cousins and Mackay, 2001, Undeman and McLachlan, 2011).

Experimentally, plant bioaccumulation data are collected from in vivo and in vitro studies. In vivo studies (field and greenhouse grown plants) usually focus on accumulation and dissipation from harvested plant components or whole plants and attempt to simulate realistic environmental conditions (Burrows et al., 2002). In contrast, in vitro studies (cell cultures) provide information on transport and degradation processes in plant cells under controlled laboratory conditions (Schwitzguébel et al., 2011). Data from in vivo and in vitro studies demonstrate the capacity of plants to biotransform and bioaccumulate a wide range of organic contaminants (Bacci et al., 1990, Eggen et al., 2011, Fantke and Juraske, 2013, Jones and Duarte-Davidson, 1997, Liu et al., 2009, Macherius et al., 2012, Mikes et al., 2009, Samsøe-Petersen et al., 2002, Scheunert et al., 1994, Sharma et al., 2007, St-Amand et al., 2007, Stahl et al., 2009, Willis and McDowell, 1987). For most chemical-plant species combinations no experimental bioaccumulation and biotransformation data exist (Arnot et al., 2013, Fantke et al., 2014) and in the few cases where data are available, the critical information necessary to assess data reproducibility and interpretability are often lacking (Fantke and Juraske, 2013).

Mathematical models are used to complement expensive and time-consuming experimental studies for generalizing and extrapolating findings from specific experimental scenarios and as input for decisions in exposure- and risk-related science-policy fields. Models thereby show considerable potential for improving the basic understanding of contaminant transport processes in plants (Gobas et al., 2016). In this study, we seek to help identifying key test parameters that are required to improve the interpretation and evaluation of plant bioaccumulation data, and to support the development, parameterization, application and evaluation of plant bioaccumulation models.

We first review existing plant bioaccumulation testing guidelines and their reporting requirements to identify whether information crucial for interpreting experimental data and for supporting modeling is reported. Next, we give a brief overview of data that are essential for developing and testing plant bioaccumulation models. Finally, we evaluate how data reporting requirements in current test protocols can be improved to better support the interpretation of experimental data and their use in plant bioaccumulation modeling. We will thereby emphasize that reporting the most relevant additional data is usually feasible and does not provide additional financial challenges. Overall, our study aims to improve the understanding of plant bioaccumulation in support of various regulatory and non-regulatory applications.