Plant uptake of pesticides and human health: dynamic modeling of residues in wheat and human intake

Human intake of pesticide residues from consumption of processed food plays an important role for evaluating current agricultural practice. We take advantage of latest developments in crop-specific plant uptake modeling and propose an innovative dynamic model to estimate pesticide residues in the wheat-environment system, dynamiCROP. We used this model to analyze uptake and translocation of pesticides in wheat after foliar spray application and subsequent intake fractions by humans. Based on the evolution of residues in edible parts of harvested wheat we predict that between 22 mg and 2.1 g per kg applied pesticide are taken in by humans via consumption of processed wheat products. Model results were compared with experimentally derived concentrations in wheat ears and with estimated intake via inhalation and ingestion caused by indirect emissions, i.e. the amount lost to the environment during pesticide application. Modeled and measured concentrations in wheat fitted very well and deviate from less than a factor 1.5 for chlorothalonil to a maximum factor 3 for tebuconazole. Main aspects influencing pesticide fate behavior are degradation half-life in plant and time between pesticide application and crop harvest, leading to variations in harvest fraction of at least three orders of magnitude. Food processing may further reduce residues by approximately 63%. Intake fractions from residues in sprayed wheat were up to four orders of magnitude higher than intake fractions estimated from indirect emissions, thereby demonstrating the importance of exposure from consumption of food crops after direct pesticide treatment. (C) 2011 Elsevier Ltd. All rights reserved.

Modelling pesticides residues

Raphael Charles

This work is a contribution to the development of a specific method to assess the presence of residues in agricultural commodities. The following objectives are formulated: to identify and describe main processes in environment — plant exchanges, to build of a model to assess the residue concentration at harvest in agricultural commodities, to understand the functioning of the modelled system, to characterise pesticides used in field crops and identify optimisation potentials in phytosanitary measures. The frame for the methodological developments corresponds to the procedure for the evaluation of the toxicity provided for the Life Cycle Impact Assessment methodology and for the method Impact 2002+. In chapter 2, the methodological procedure for the assessment of human toxicity potential is introduced. First the factors of fate and exposure are described, including the notion of harvest fraction, the amount of substance found in harvest per unit of substance emitted initially in the system, the main result of the present study. Then the effect factors and the framework for impact evaluation are introduced. Chapter 3 describes the principles accounted for the building of the fate model. Wheat crop and a restricted list of substances are chosen for these methodological developments. The model is composed by compartments describing the environment and the plant. Its functioning is based on initial amounts of substance in the source compartments, on transfer rates linking the compartments and on a dynamic evolution as a function of time between the treatment and the harvest. Air, soil and formulation deposit on plant are the primer compartments receiving the treated substance. Each transport is described by a transfer rate accounting for the process and for the equilibrium partitioning between the two exchanging compartments. Degradation of substance and plant growth are additional processes considered. Each compartment is described by a linear differential equation for the variation of mass accumulating and dissipating. Their assembly builds the model solved as a function of time. This exact resolution is complemented by additional tools to better understand the system functioning and to provide further approximations of the results: the system is simplified into subsystems describing the source and the receiving plant compartment and analytically solved using interpretable equations. Chapter 4 describes and discusses all transport and dissipation processes determining the fate of the substance in the limits of the system. The recent publications concerning the understanding and the modelling of pesticide transfer from formulation deposit on plant through the cuticular membranes give new possibilities to model pesticide fate and to better account for the direct applications on the plant. In chapter 5, the core model is first applied and its functioning analysed. The low availability and partly unsatisfying quality of data for pesticides description is a main complication for the methodology application: the lack of data for the half-life of the substance in the plant especially leads to a strong extrapolation for this determinant factor. A large difference is observed between early and late applied pesticides with respectively a major release to soil or a release to formulation deposit on plant surface. The initial transport processes quickly distribute the substance in the system. Once each plant compartment has accumulated residues up to a maximum amount, a dissipation phase occurs. The duration of these periods is determinant for the level of residue in harvest. The soil is a determinant source for long term evolutions of the system, for soon applied substances with low degradation rate. The half-life of substance deposited on plant is equal to a few days, but the transfer is fast from formulation deposit to the inner plant, where degradation is generally much slower. The accumulation of substance from the air is mostly negligible. The sum of the subsystems gives an approximation of the total system, useful for interpretation. The possibility to simplify the subsystem by ignoring the transfer back from receiving to the source compartment underlines the low contribution of these transfers in the functioning of the model. An approximated resolution is based on the determination of the maximum accumulated substance and on the subsequent dissipation process. However, an important loss of precision is observed. This approximation is useful for interpretation and for extrapolations. In chapter 6, an evaluation of the model is conducted through a sensitivity and uncertainty analysis. The sensitivity analysis consists of evaluating the effect on the output of a change in an input, on the basis of three complementary approaches: the effect of a fixed change in the input of e.g. 0.1%, the effect of a change specific to the uncertainty of the input and the effect of a change in input value from a minimum to a maximum. The uncertainty of an output is evaluated according to the relative contribution of the confidence factors of the inputs. Results show that the half-lives and the time are the most important factors determining the sensitivity of the system and the propagation of uncertainty. The contribution of the half-life to the confidence factor of the harvest fraction reaches between 30% to 98% of the total uncertainty. The confidence factors of results increase exponentially with the time interval between application and harvest. The role of partition coefficients to the behaviour of substances is highly variable, may be determinant or negligible, with increasing or limiting effect on mobility. Sensitivity and uncertainty for parameters describing the agricultural or environmental system are very variable, but sometimes determinant and so confirmed as essential for the system functioning. Consequently, differences in harvest fractions between substances are only significant if they are high. A first comparison of the computed results with measures of residues obtained by an experiment and with references such as tolerance values lead to a pertinent verification of the overall methodology. Finally, the qualitative comparison with other models underlines the specificities and the originality of the present methodology in particular by comparison with environmental multi-media models running in steady state. In chapter 7, the model is finally applied for an ultimate interpretation. The harvest fractions for more than 100 substances are evaluated. Among all types of substances, low and high levels of residues per treatment are found, representative for the high variability of harvest fractions from 5E-16 for bromoxynil to 7E-03 for tebuconazole sprayed on wheat. The fate process represents the highest source of variation for the toxicity. If the application rate does not explain the high differences in residue level at harvest, the time of application may represent an optimisation potential particularly for late treatments. However, the toxicity needs to account for both fate and effect factors, as only their combination effectively allows to evaluate the toxicity. According to the available list of Human Damage Factors per treatment, problematic substances may be effectively identified and substituted. In chapter 8 answers to questions brought with the objectives bring a conclusion to the study. The appendices include notably the results of harvest fractions and toxicity per unit substance applied, per treatment and per unit cultivated crop area, for the main substances and field crops. A LCA is also presented on the intensity level of wheat production.

EPFL2004

Healthy lighting: integrating non-visual responses to light into building simulation

María Lovísa Ámundadóttir

The goal of this thesis is to develop a computer-based lighting simulation framework capable of predicting human non-visual responses to light and to validate novel guidelines that can inform designers about how lighting might affect human non-visual responses in the built environment. The framework consists of four steps, including a mathematical model that accounts for spectral and dynamic variations in light exposure. As a part of the framework, different light exposure patterns are generated to simulate the effects of occupants’ actions, movements, and activities. The results are interpreted based on pre-defined design goals. Light is one of the most significant factors that affect human health and wellbeing in the built environment. Since 2002, when the first reports on the discovery of a novel type of photoreceptor were published, a new field of study started to emerge at the intersection of photobiology and architecture. The novel photoreceptors are the primary mediators of non-visual responses to light in humans, including synchronizing circadian rhythms and directly alerting the brain. These new findings emanating from the photobiology field have sparked a growing interest in the role of lighting design on human health and wellbeing. The novel photoreceptors contain the photopigment meanopsin. Melanopsin is more sensitive to short- wavelength light, with a peak sensitivity that is blue-shifted (λmax ≅ 480 nm), relative to the photopic visual system (λmax ≅ 555 nm), which is dominated by the response of cone photoreceptors. In addition to the difference between the photoreceptors’ spectral sensitivity, researchers have identified intensity, duration/pattern, history, and timing of light exposure as important variables that control the non-visual light response in humans. Considering the blue-shifted sensitivity of the melanopsin-containing photoreceptors, the current recommendations for lighting, which are based mainly on visual criteria, may not provide the necessary amount of light to synchronize important physiological and behavioral rhythms to the 24-hour day. Moreover, a number of recent studies have demonstrated that exposure to bright light can improve alertness, performance, and mood. However, exposures to light at night can disturb circadian rhythms, such as hormone production and sleep-wake cycles, and have been linked to increased risk of cancer. Lighting simulation software tools are designed to evaluate visual requirements and comfort taking into account the stochastic fluctuations linked to the climate and to the behavior of the buildings’ occupants. The evaluation compares simulated light intensity values on a horizontal plane with static threshold values. Since the non-visual system responds slowly to light exposure and adapts to changes in light intensity and spectral composition over much longer time periods than the visual system, the lighting simulation software tools cannot be applied directly to evaluate the non-visual response to light. The response must be evaluated based on dynamic threshold values, which depend on intensity, spectrum, duration, history, and timing of light exposure. Currently, there is no mathematical model that incorporates all five variables to predict the non-visual effects of light on humans. Further, the movements of humans must be simulated to account for the amount of light received at the eye. Thus horizontal sensors on a plane must be replaced with vertical sensors at the eye level that can rotate and move around. The importance of building simulations is growing with increasing complexity of building design and higher performance requirements regarding sustainability. New methods, created at the interface between photobiology and architecture, predicting the non-visual response to light are needed to support design decisions.

2013

Healthy lighting: integrating non-visual responses to light into building simulation

María Lovísa Ámundadóttir

2013

Modelling pesticides residues

Raphael Charles

EPFL2004