Spatial modeling of personalized exposure dynamics: the case of pesticide use in small-scale agricultural production landscapes of the developing world

Simulating exposure-related human mobility behavior at the neighborhood-level under COVID-19 in Porto Alegre, Brazil

Modeling experts have been continually researching the interplay of human mobility and COVID-19 transmission since the outbreak of the pandemic. They tried to address this problem and support the control of the pandemic spreading at the national or regional levels. However, these modeling approaches had little success in producing empirically verifiable results at the neighborhood level due to a lack of data and limited representation of low spatial scales in the models. To fill this gap, this research aims to present an agent-based model to simulate human mobility choices in the context of COVID-19, based on social activities of individuals in the neighborhood. We apply the VIABLE model to the decision-making process of heterogeneous agents, who populate the system's environment. The agents adapt their mobility and activities autonomously at each iteration to improve their well-being and respond to exposure risks. The study reveals significant temporal variations in mobility choices between the groups of agents with different vulnerability levels under the Covid-19 pandemic. Agents from the same group with similar economic backgrounds tend to select the same mobility patterns and activities leading to segregation at this low scale. We calibrated the model with a focus on Porto Alegre in Brazil.

Role of land use and land cover in residential exposures to agricultural pesticide models

Exposure of the general population to pesticides, especially in agricultural areas, is a major public health concern. This review analyses the role of Land Use and Land Cover (LULC) in Residential Exposure to Agricultural Pesticides (REAP) and how it is measured and modelled. Some epidemiological studies have shown that basic LULC variables, such as distance to a crop and field size, are relevant for explaining REAP. However, the potential of LULC mitigation elements, such as vegetation barriers, grassy strips and buffer zones, to mitigate REAP has been poorly studied. The availability of recent low-cost and high-quality geospatial data enables REAP models to include alternative and more precise LULC variables. This review also highlights the need for (i) generic environmental sampling protocols, (ii) exposure and spraying datasets and (iii) assessment of the mitigation capacity of LULC to improve REAP modelling significantly.

[Impact of endocrine disrupting pesticides on breast cancer]

Of the 800 pesticides used worldwide, about 650 can affect the functioning of the endocrine system: endocrine disrupting pesticides (EDPs). Dietary or environmental exposure to EDPs is a concern, as their presence is currently demonstrated in most biological fluids. Some EDPs are prohibited, classified as carcinogenic, others are "probable" or "possible" carcinogens when there is limited evidence of their tumor effect. The impact of EDPs on breasts is not well known to date. However, since most EDPs have a long half-life and are lipophilic, breasts, composed mainly of adipose tissue, are a suitable site for their concentration. The objective of our review was to analyze the impact of EDPs related to our environmental exposure on breast cancer risk, through an analysis of recent literature, including epidemiological and biological data. Our review showed a positive association between the presence of EDPs and breast cancer, especially among women farmers or EDPs users but also in the general population. Studies on breast tumors have found a higher concentration of EDPs in estrogen-sensitive tumors. As for mortality, studies are contradictory, but confirm the dangerousness of some EDPs. The different series analyzed have several limitations, such as the low number of EDPs evaluated, small numbers and insufficient follow up. The potentiating effect of different EDPs used concomitantly and the window of exposure to these substances are parameters to be assessed.

An Agent-Based Modeling Framework for Simulating Human Exposure to Environmental Stresses in Urban Areas

Several approaches have been used to assess potential human exposure to environmental stresses and achieve optimal results under various conditions, such as for example, for different scales, groups of people, or points in time. A thorough literature review in this paper identifies the research gap regarding modeling approaches for assessing human exposure to environment stressors, and it indicates that microsimulation tools are becoming increasingly important in human exposure assessments of urban environments, in which each person is simulated individually and continuously. The paper further describes an agent-based model (ABM) framework that can dynamically simulate human exposure levels, along with their daily activities, in urban areas that are characterized by environmental stresses such as air pollution and heat stress. Within the framework, decision-making processes can be included for each individual based on rule-based behavior in order to achieve goals under changing environmental conditions. The ideas described in this paper are implemented in a free and open source NetLogo platform. A basic modeling scenario of the ABM framework in Hamburg, Germany, demonstrates its utility in various urban environments and individual activity patterns, as well as its portability to other models, programs, and frameworks. The prototype model can potentially be extended to support environmental incidence management through exploring the daily routines of different groups of citizens, and comparing the effectiveness of different strategies. Further research is needed to fully develop an operational version of the model.

Mechanistic modeling of pesticide exposure: The missing keystone of honey bee toxicology

The role of pesticides in recent honey bee losses is controversial, partly because field studies often fail to detect effects predicted by laboratory studies. This dissonance highlights a critical gap in the field of honey bee toxicology: there exists little mechanistic understanding of the patterns and processes of exposure that link honey bees to pesticides in their environment. The authors submit that 2 key processes underlie honey bee pesticide exposure: 1) the acquisition of pesticide by foraging bees, and 2) the in-hive distribution of pesticide returned by foragers. The acquisition of pesticide by foraging bees must be understood as the spatiotemporal intersection between environmental contamination and honey bee foraging activity. This implies that exposure is distributional, not discrete, and that a subset of foragers may acquire harmful doses of pesticide while the mean colony exposure would appear safe. The in-hive distribution of pesticide is a complex process driven principally by food transfer interactions between colony members, and this process differs importantly between pollen and nectar. High priority should be placed on applying the extensive literature on honey bee biology to the development of more rigorously mechanistic models of honey bee pesticide exposure. In combination with mechanistic effects modeling, mechanistic exposure modeling has the potential to integrate the field of honey bee toxicology, advancing both risk assessment and basic research. Environ Toxicol Chem 2017;36:871-881. © 2016 SETAC.

Current challenges in air sampling of semivolatile organic contaminants: sampling artifacts and their influence on data comparability

With current science and policy needs, more attention is being given to expanding and improving air sampling of semivolatile organic contaminants (SVOCs). However, a wide range of techniques and configurations are currently used (active and passive samplers, different deployment times, different sorbents, etc.) and as the SVOC community looks to assess air measurements on a global scale, questions of comparability arise. We review current air sampling techniques, with a focus on sampling artifacts that can lead to uncertainties or biases in reported concentrations, in particular breakthrough, degradation, meteorological influences, and assumptions regarding passive sampling. From this assessment, we estimate the bias introduced for SVOC concentrations from all factors. Due to the effects of breakthrough, degradation, particle fractions and sampler uptake periods, some current passive and active sampler configurations may underestimate certain SVOCs by 30-95%. We then recommend future study design, appropriateness of sampler types for different study goals, and finally, how the SVOC community should move forward in both research and monitoring to best achieve comparability and consistency in air measurements.

Pesticide uptake in potatoes: model and field experiments

A dynamic model for uptake of pesticides in potatoes is presented and evaluated with measurements performed within a field trial in the region of Boyacá, Colombia. The model takes into account the time between pesticide applications and harvest, the time between harvest and consumption, the amount of spray deposition on soil surface, mobility and degradation of pesticide in soil, diffusive uptake and persistence due to crop growth and metabolism in plant material, and loss due to food processing. Food processing steps included were cleaning, washing, storing, and cooking. Pesticide concentrations were measured periodically in soil and potato samples from the beginning of tuber formation until harvest. The model was able to predict the magnitude and temporal profile of the experimentally derived pesticide concentrations well, with all measurements falling within the 90% confidence interval. The fraction of chlorpyrifos applied on the field during plant cultivation that eventually is ingested by the consumer is on average 10(-4)-10(-7), depending on the time between pesticide application and ingestion and the processing step considered.

Spatio-temporal epidemiology: principles and opportunities

Space-time analysis of disease data has historically involved the search for patterns in aggregated data to identify how regions of high and low risk change through time. Space-time analysis of aggregated data has great value, but represents only a subset of space-time epidemiologic applications. Technological advances for tracking and mapping individuals (e.g., global positioning systems) have introduced mobile populations as an important element in space-time epidemiology. We review five domains critical to the developing field of spatio-temporal epidemiology: (1) spatio-temporal epidemiologic theory, (2) selection of appropriate spatial scale of analysis, (3) choice of spatial/spatio-temporal method for pattern identification, (4) individual-level exposure assessment in epidemiologic studies, and (5) assessment and consideration of locational and attribute uncertainty. This review provides an introduction to principles of space-time epidemiology and highlights future research opportunities.