A system dynamics simulative modeling framework to assess bioaccessibility of lead and facilitate lead reduction in the urban environment

Lead (Pb) exposure to residents of impacted communities depends on the environmental concentrations of lead that is potentially bioaccessible. Such concentrations are the result of a complex array of interactive factors that influence one another through direct or indirect linkages. Current models to predict the health impact of Pb exposure often do not consider the complexity from a system perspective. Therefore, there exists a great need to develop a holistic modeling strategy to simulate the risk to Pb exposure and resulting blood Pb concentrations based on bioaccessible Pb concentrations in the environment and how socioeconomic status, policy/ scholarly intervention, and collective community behaviors influence that concentration. Our study attempts to develop a grey system integrated system dynamics simulative modeling framework to simulate general Pb bioaccessibility in the environments and how it transmits from the soil, the water, the house, and the general environment to human bodies. The study aims to predicting risk of Pb exposure in the long run in a community/neighborhood, especially the risk to vulnerable populations, such as young children and the aged population. The model also aims to identify the most effective ways to curb human exposure to bioaccessible Pb. This is the first stage of a multi-stage research activity. In this stage, the study focuses on developing a theoretical and empirical modeling framework of the simulative model, and the data structure. In this study, we take a macro perspective to treat a neighborhood/community, a city, or a designated area as an integrated and dynamic system in that it is composed of many interrelated, feedback-linked components. Each component exists and acts because of its interaction with other system components, both observable and hidden. The integrated mutual interaction and multiple components collectively determine how the system will change and evolve in the future, manifested as the change and evolution of the various system components. Bioaccessible Pb in the neighborhood’s environment is our key system component. The grey system and system dynamic simulative model attempts to analyze the potential interactive co-variations among different system components, or the changing and/or evolving trend a system component demonstrates over time. By establishing the interactive feedback loops that connect all the observable system components with sufficient data, the model will be able to simulate the dynamics of the system to predict its behavior and manifestation in the future. Since the simulative model is built upon the interactive feedback loops among all system components, the model will produce simulated results for all observable system components as well. Our goals in later stages are to predict the potential damage to human bodies that will be the basis for Pb reduction and removal related policies at the macro management levels.