The importance of reliably assessing human exposure to environmental toxins from agricultural management activities has been growing due to human health impairments within farm-workers and the rural population, worldwide , and particularly in developing countries . According to estimations of The World Bank there are 355,000 deaths each year due to unintentional poisoning from pesticide exposure . Despite recognized assessment uncertainties  the majority of these incidents are clearly related to developing countries in Africa, Asia and Central and South America [5, 6]. One common management practice in these regions is small-scale farming with manual (backpack) pesticide spraying. The main reasons for pesticide exposure in such agricultural production landscapes are the availability of toxic substances, as well as a lack of protective measures, education and health care . Educational efforts to reduce health risks of farmers in these regions have had limited success [2, 6]. The lack of quantitative information with regard to potential exposure pathways to estimate the risks for different groups, such as inhabitants or backpack sprayers could be a factor.
Exposure occurs when there is contact of a chemical, physical or biological agent of a specific concentration with an organism for an interval of time [8, 9]. Human exposure to pesticides can occur through inhalation via air or dust, dermal contact with the pesticide or deposited residue on surfaces, ingestion, or interpersonal contact with adherent residues on the body (especially hands) or clothing. Personal activity patterns were recognized as one of the main determinants of the magnitude, frequency, duration, and pathways of exposure . Thus a need exists for improving exposure assessments on the individual level resulting in personalized exposure assessment (PEA) . To date there is no model approach that completely implements the conceptual idea of PEA in small-scale agriculture in the developing world.
Spatial factors such as the location of the exposed individuals and their activity in relation to the contaminant source have been identified as important determinants for more reliable exposure assessments . Thus new technologies that allow the assessment of external environmental exposure to support PEA such as Geographical Information Systems (GIS) and environmental sensors have been discussed [11, 13, 14]. Case studies for exposure assessment based on GIS are reported for pesticides [15, 16], urban pollution [17, 18], trichloroethylene in water , and pollutants from landfill sites . Whereas these approaches resulted in the improvement of exposure assessments, impediments to a specific personalized analysis have been encountered. Identified impediments include the high aggregation of spatial data, e.g., land use records , the scale dependence of exposure estimates , the lack of consideration of spatial and temporal variation  and the lack of accounting for individual activity patterns .
In response to some of these impediments Spatio-Temporal Information Systems (STIS) [25, 26] were developed to build up individual histories of exposure to arsenic concentrations in water supplies. These approaches include environmental variations and residential mobility history of individuals. However, STIS do not incorporate personal activity patterns, which are highly relevant to pesticide exposure.
The first approaches that incorporated personal activity patterns to break down the population level to the individual level for deriving exposure metrics  applied Global Positioning Systems (GPS) [27, 28]. Here, individual time-location data and activity-related information relevant for exposure were collected. However, it remains an open question how to incorporate such activity data into a model framework that links them with spatio-temporal distributions of contaminant concentrations in the environment for PEA [29, 30].
Spatial-explicit dynamic modeling approaches such as individual-based models (IBM), which are a subcategory of agent-based models (ABM), have gained increased attention for epidemiological studies and public health [31, 32]. IBMs have been applied to model management decisions in farming systems [33, 34], human-wildlife interactions , as well as integrated pest management . By modeling activity and characteristics of moving individuals, IBMs break down the analysis to the individual level . IBMs thus account for heterogeneity within the population with regard to individual characteristics and for local interactions between individuals and the environment [38, 39]. One common approach of modeling actors or individuals in the physical environment is to couple ABMs with Cellular automata (CA) . CAs create a discrete time system in a spatial context  in which cells in a lattice undergo state transitions at a given time interval. These transitions are defined by simple rules of interactions between cells within the local environment [42, 43]. These conceptual principles illustrate that IBMs provide an appropriate methodological framework to address the problem of PEA. However, IBMs have never been applied for developing spatially explicit PEA approaches.
In this paper we propose a conceptual framework for the assessment of personalized pesticide exposure of farm-workers due to primary drift and dermal contact with deposited contaminant residuals in small-scale agricultural management settings in less-developed regions where backpack spraying is carried out. The proposed IBM prototype integrates dynamic distributions of deposited contaminant residuals, which are influenced by decisions related to pesticide application, with individual spatial activity of farm-workers under different safety conditions. The model prototype incorporates simulated movement patterns and simplified assumptions for the dynamics in the system. The main purpose of this conceptual framework is to demonstrate the capability of such tools to assess effects of protection measures, activities performed, patterns of movement as well as patterns of pesticide application on potential exposure of individuals. We develop this conceptual model and test first simulations using underlying spatial data of Vereda la Hoya, a rural part of the Departamento de Boyacá, Colombia.